http://www.clinfowiki.org/wiki/api.php?action=feedcontributions&user=Vishaghera&feedformat=atomClinfowiki - User contributions [en]2024-03-29T14:16:14ZUser contributionsMediaWiki 1.22.4http://www.clinfowiki.org/wiki/index.php/Protected_Health_Information_(PHI)Protected Health Information (PHI)2015-04-09T02:24:07Z<p>Vishaghera: </p>
<hr />
<div>'''Protected health information (PHI)''' is individually identifiable health information. PHI is demographic data that relates to individual’s physical or mental health, provision of health care, payment for the provision of health care, and common identifiers such as name, address, phone numbers, birth date, and Social Security Number. All protected health information must comply with [[Health Insurance Portability and Accountability Act (HIPAA)]] standards.There is also electronic protected health information (ePHI). The ePHI is PHI that is sent or transmitted electronically. <br />
<br />
PHI is defined by UCSF as "Protected health information (PHI) is any information in the medical record or designated record set that can be used to identify an individual and that was created, used, or disclosed in the course of providing a health care service such as diagnosis or treatment": <ref name="UCSF"> HIPAA - PHI: LIST OF 18 IDENTIFIERS AND DEFINITION OF PHI, University of California, San Fransisco. http://www.research.ucsf.edu/chr/HIPAA/chrHIPAAphi.asp#Definition </ref>.<br />
<br />
== Introduction ==<br />
<br />
PHI and ePHI is found in many locations in paper medical records and the [[EMR|electronic medical record]]. Data can be found in medical records, billing records, insurance/benefit enrollment and payment, claims payment, and case management records.<br />
<br />
Security and privacy go hand in hand. Security is about controlling access to electronic PHI, while privacy is about controlling how electronic, oral, and written PHI is used and disclosed. Covered entities need to make it a top priority to establish and implement policies and procedures to protect patient information (1).<br />
<br />
==Covered Entities==<br />
Covered entities covered under the Privacy Rule include (4):<br />
<br />
1) Health Plans <br />
<br />
2) Health Care Providers<br />
<br />
3) Health Care Clearinghouses<br />
<br />
=== Examples of PHI Identifiers===<br />
<br />
Examples Include (5)(6):<br />
<br />
• Names<br />
<br />
• Addresses: All geographical subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000.<br />
<br />
• Dates: All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older<br />
<br />
• Phone numbers<br />
<br />
• Fax numbers<br />
<br />
• E-mail address<br />
<br />
• Social Security Numbers<br />
<br />
• Medical records numbers<br />
<br />
• Account numbers<br />
<br />
• Health plan beneficiary number<br />
<br />
• Certification/license number<br />
<br />
• Vehicle identifiers and serial numbers, including license plate numbers <br />
<br />
• Device identifiers and serial numbers<br />
<br />
• Web Universal Resource Locators (URLs)<br />
<br />
• Internet Protocol (IP) address numbers<br />
<br />
• Names of relatives<br />
<br />
• Biometric identifiers, including finger and voice prints<br />
<br />
• Full face photographic images and any comparable images<br />
<br />
• Any other unique identifying number, characteristic, or code (note this does not mean the unique code assigned by the investigator to code the data)<br />
<br />
== Administrative Safeguards==<br />
<br />
The Privacy Rule requires covered entities to perform administrative tasks to protect privacy of health information. Scalable confidentiality and security procedures, designated security officer, sanctions for violations, and signed statement by all employees regarding confidentiality of data (1).<br />
<br />
=== Compliance guidelines ===<br />
<br />
Organizations compliance guidelines, like law and industry codes reflect and are intended to serve patients by safeguarding medical information, enabling us to advance patient care while protecting patient privacy.<br />
<br />
Fundamental elements to an effective compliance program:<br />
* Written policies and procedures for compliance<br />
* A designated compliance officer and committee<br />
* Effective training and education for employees<br />
* Effective lines of communication<br />
* Internal monitoring and auditing procedures<br />
* Enforcement of standards through disciplinary guidelines<br />
* Prompt responses to detected problems and implementation of corrective action (2)<br />
<br />
==Technical Safeguards==<br />
<br />
Technical safegyards include:<br />
<br />
* unique IDs<br />
* [[encryption|encrypted]] password storage system<br />
* disallowing weak [[password|passwords]]<br />
* automatic time logoff<br />
* system enforced password changes<br />
* firewall<br />
* virus checking<br />
* disallow sharing of passwords<br />
<br />
===Protecting Electronic Data===<br />
<br />
Confidential information stored on a portable electronic device such as a laptop, USB drive, CD, DVD or PDA should be encrypted to ensure data cannot be retrieved by an unauthorized person if lost or stolen.<br />
<br />
===Protecting Paper Medical Records===<br />
<br />
Paper medical records containing PHI should be kept in a locked and secure location. Only authorized or designated people should have access to any paper records containing PHI. <br />
<br />
===Recycling===<br />
<br />
All covered entities are required to properly dispose of PHI. A reliable way for most organizations is to hire a shredding company that will destroy all PHI off site in a manner that is consistent with HIPAA Privacy and Security rules and regulations. Placing protected information in an unsecured garbage can (including blue recycle cans) is not an acceptable method of disposal for documents that contain private information. Such information should be secured until shredded or properly destroyed.<br />
<br />
===Privacy and Security violation of PHI===<br />
<br />
If PHI is not protected properly by all covered entities there are severe penalties, fines and corrective action that may take place. Anyone can file a compliant if they feel that their privacy has been violated. More information is found at [http://www.hhs.gov/ocr/privacy/hipaa/complaints/index.html]<br />
<br />
<br />
==Summary==<br />
<br />
Healthcare providers in all settings implement compliance programs to protect patient privacy and to ensure ethical business practices. This is necessary due to the increased severity of penalties established by the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and the Balanced Budget Act of 1997 (public law 105-33). By ensuring ethical business practices through compliance programs, healthcare providers reduce their risk of criminal and civil litigation in regards to privacy and security.(3)<br />
<br />
== Reviews ==<br />
<br />
* [[“Not all my friends need to know”: a qualitative study of teenage patients, privacy, and social media]]<br />
<br />
== References ==<br />
<references/><br />
<br />
# Hartley, C. & Jones, E. (2004) HIPAA Plain and Simple, a compliance guide for healthcare professionals. AMA Press, Chicago, IL<br />
# Healthcare compliance-an introductory guide for employees. Johnson and Johnson. Retrieved from: http://www.shareholder.com/Shared/DynamicDoc/jnj/1293/6210%20Overview%20Guide_WEB_single_pg.pdf<br />
# AHIMA (2011). Healthcare compliance. Retrieved from: http://www.ahima.org/resources/compliance.aspx<br />
#http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/index.html<br />
#http://www.irb.emory.edu/researchers/formstools/docs/other/phi_identifiers.pdf<br />
# http://cphs.berkeley.edu/hipaa/hipaa18.html<br />
<br />
<br />
Submitted by Sherry Dexheimer<br />
<br />
[[Category:BMI512-SUM-11]]<br />
<br />
Submitted by Molly Kneen<br />
<br />
[[Category:BMI512-FALL-12]]<br />
[[Category: Definitions]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Views_on_health_information_sharing_and_privacy_from_primary_care_practices_using_electronic_medical_recordsViews on health information sharing and privacy from primary care practices using electronic medical records2015-04-08T21:54:35Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>=== Abstract ===<br />
The purpose of this article is to explore how physicians and patients balance the potential benefits and dangers of sharing patients’ electronic health information in regards to patient safety as well as miscellaneous secondary purposes <ref name="Privacy">Perera, G., Holbrook, A., Thabane, L., Foster, G., & Willison, D. J. (2011). Views on health information sharing and privacy from primary care practices using electronic medical records. International journal of medical informatics, 80(2), 94-101. http://www.sciencedirect.com.ezproxyhost.library.tmc.edu/science/article/pii/S138650561000225X</ref>.<br />
<br />
=== Methods ===<br />
A Health Information Privacy Questionnaire(s) (HIPQ) which was composed of before and after surveys were filled out by both physicians and patients in practices which had [[EMR|electronic medical records (EMRs)]] implemented and were part of a clinical trial in Ontario, Canada. Thirteen questions were asked in the following four categories<ref name="Privacy"></ref>:<br />
* [[privacy|Privacy]] of EMRs<br />
* Use of patients' health information by someone outside the health care organization<br />
* Sharing patients' information within the health care system<br />
* The overall perception of benefits versus harms of computerization in health care<br />
<br />
=== Results ===<br />
There were a total of 511 patients and 46 physicians who participated in the survey. Over 90% of those surveys had favorable opinions regarding the sharing of electronic health information amongst health care professionals for the purpose of providing clinical advice. Less than 70% agreed health data lacking identification information should be shared with non health care professionals. Approximately 38%-50% believed computerized records could have greater [[Security|security]] than paper records, but 58% of patients and 70% of physicians believed the benefits gained from having electronic health information outweighed the risk towards [[confidentiality]]<ref name="Privacy"></ref>.<br />
=== Conclusions ===<br />
The majority of patients and physicians highly valued the benefits which EMRs can provide, but it is important to note the large percentage of those who had doubts regarding any and all secondary uses of de-identified personal health information.<br />
<br />
=== Comments ===<br />
I agree with the conclusion of the article as 58% of patients believed the good EMRs can provide are worth the risk, and only 38%-50% believed electronic records could be better protected than paper records. These results are concerning because if only approximately half of the patients surveyed were supportive of EMRs then they might be reluctant to allow their information to be entered into an EMR, and lack of patient participation could cause problematic issues towards EMR implementations.<br />
<br />
Related Read: [[The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality|The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality]]<br />
<br />
= References =<br />
<references/><br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: PHR]]<br />
[[Category: Security]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Giving_Patients_Control_of_Their_EHR_DataGiving Patients Control of Their EHR Data2015-04-08T21:47:14Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>This is a review of David Blumenthal and David Squires’ (2015) article, Giving Patients Control of Their EHR Data. <ref name="control"> Blumenthal, D., Squires, D. (2015). Giving Patients control of their EHR data. Retrieved from http://link.springer.com.ezproxyhost.library.tmc.edu/article/10.1007%2Fs11606-014-3071-y </ref><br />
<br />
<br />
== Summary ==<br />
Should patient have control of who can view, access, and use their [[electronic health record | electronic health records (EHR)]]? The authors believe that patients should have full control of who can access and use their information as well as determining what information is recorded on their EHR. The federal government encourages privacy, transparency and accountability with healthcare information. Patients need to be very involved in the healthcare decisions this is reflected in the [http://www.nist.gov/nstic/NSTIC-FIPPs.pdf| Fair Information Practice Principles]<br />
In this article, the authors talk about why patients should have full control, the limitations, and also suggestions on how patient can better control their data. <br />
<br />
== Reasoning ==<br />
The authors believe patients should have full control of their own health information even if it will interfere with the patient's well being. Providers should respect the patient’s wishes and their right to control their health because patients are the only person that can make the best judgment for their own health.<br />
<br />
== Exceptions and Limitations==<br />
If patients control who and what can be accessed from their medical charts, they cannot hold the clinicians accountable for any negative outcomes with their care because certain information was withheld. Also, if the health of others is affected, then patients should not have control of their health data. For example, a patient with Ebola cannot restrict access to that information. <ref name="control"></ref> <br />
<br />
== Suggestions ==<br />
There should be training and education for clinicians and patients to learn more about patient health information. By educating patients about how their health data are used and shared, patients are more likely to make informed decisions of their own medical data.<br />
<br />
== Conclusions ==<br />
Patients should have control over their own health information. However, it is very important to educate and inform patients on how their information is used so that they can make the best decision for their own well-being.<br />
<br />
== Comments ==<br />
Initially, when I started reading this article, I thought that patients should not have full control over their own health data because if they refuse to share crucial health information, it can be dangerous for their health? However, after understanding the authors reasoning, I too have to agree that patients have a right to control their own data and health. But it is important for patients to be properly informed so that they can see what options they have.<br />
Read: [[PHR|Patient Health Records]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: Security]]<br />
[[Category: PHR]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Giving_Patients_Control_of_Their_EHR_DataGiving Patients Control of Their EHR Data2015-04-08T21:46:46Z<p>Vishaghera: </p>
<hr />
<div>This is a review of David Blumenthal and David Squires’ (2015) article, Giving Patients Control of Their EHR Data. <ref name="control"> Blumenthal, D., Squires, D. (2015). Giving Patients control of their EHR data. Retrieved from http://link.springer.com.ezproxyhost.library.tmc.edu/article/10.1007%2Fs11606-014-3071-y </ref><br />
<br />
<br />
== Summary ==<br />
Should patient have control of who can view, access, and use their [[electronic health record | electronic health records (EHR)]]? The authors believe that patients should have full control of who can access and use their information as well as determining what information is recorded on their EHR. The federal government encourages privacy, transparency and accountability with healthcare information. Patients need to be very involved in the healthcare decisions this is reflected in the [http://www.nist.gov/nstic/NSTIC-FIPPs.pdf| Fair Information Practice Principles]<br />
In this article, the authors talk about why patients should have full control, the limitations, and also suggestions on how patient can better control their data. <br />
<br />
== Reasoning ==<br />
The authors believe patients should have full control of their own health information even if it will interfere with the patient's well being. Providers should respect the patient’s wishes and their right to control their health because patients are the only person that can make the best judgment for their own health.<br />
<br />
== Exceptions and Limitations==<br />
If patients control who and what can be accessed from their medical charts, they cannot hold the clinicians accountable for any negative outcomes with their care because certain information was withheld. Also, if the health of others is affected, then patients should not have control of their health data. For example, a patient with Ebola cannot restrict access to that information. <ref name="control"></ref> <br />
<br />
== Suggestions ==<br />
There should be training and education for clinicians and patients to learn more about patient health information. By educating patients about how their health data are used and shared, patients are more likely to make informed decisions of their own medical data.<br />
<br />
== Conclusions ==<br />
Patients should have control over their own health information. However, it is very important to educate and inform patients on how their information is used so that they can make the best decision for their own well-being.<br />
<br />
== Comments ==<br />
Initially, when I started reading this article, I thought that patients should not have full control over their own health data because if they refuse to share crucial health information, it can be dangerous for their health? However, after understanding the authors reasoning, I too have to agree that patients have a right to control their own data and health. But it is important for patients to be properly informed so that they can see what options they have.<br />
Read: [[Patient Health Records|Patient Health Records]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: Security]]<br />
[[Category: PHR]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Giving_Patients_Control_of_Their_EHR_DataGiving Patients Control of Their EHR Data2015-04-08T21:44:35Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>This is a review of David Blumenthal and David Squires’ (2015) article, Giving Patients Control of Their EHR Data. <ref name="control"> Blumenthal, D., Squires, D. (2015). Giving Patients control of their EHR data. Retrieved from http://link.springer.com.ezproxyhost.library.tmc.edu/article/10.1007%2Fs11606-014-3071-y </ref><br />
<br />
<br />
== Summary ==<br />
Should patient have control of who can view, access, and use their [[electronic health record | electronic health records (EHR)]]? The authors believe that patients should have full control of who can access and use their information as well as determining what information is recorded on their EHR. The federal government encourages privacy, transparency and accountability with healthcare information. Patients need to be very involved in the healthcare decisions this is reflected in the [http://www.nist.gov/nstic/NSTIC-FIPPs.pdf| Fair Information Practice Principles]<br />
In this article, the authors talk about why patients should have full control, the limitations, and also suggestions on how patient can better control their data. <br />
<br />
== Reasoning ==<br />
The authors believe patients should have full control of their own health information even if it will interfere with the patient's well being. Providers should respect the patient’s wishes and their right to control their health because patients are the only person that can make the best judgment for their own health.<br />
<br />
== Exceptions and Limitations==<br />
If patients control who and what can be accessed from their medical charts, they cannot hold the clinicians accountable for any negative outcomes with their care because certain information was withheld. Also, if the health of others is affected, then patients should not have control of their health data. For example, a patient with Ebola cannot restrict access to that information. <ref name="control"></ref> <br />
<br />
== Suggestions ==<br />
There should be training and education for clinicians and patients to learn more about patient health information. By educating patients about how their health data are used and shared, patients are more likely to make informed decisions of their own medical data.<br />
<br />
== Conclusions ==<br />
Patients should have control over their own health information. However, it is very important to educate and inform patients on how their information is used so that they can make the best decision for their own well-being.<br />
<br />
== Comments ==<br />
Initially, when I started reading this article, I thought that patients should not have full control over their own health data because if they refuse to share crucial health information, it can be dangerous for their health? However, after understanding the authors reasoning, I too have to agree that patients have a right to control their own data and health. But it is important for patients to be properly informed so that they can see what options they have.<br />
Read: [[Patient Health Records|Patient Health Records]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: Security]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Giving_Patients_Control_of_Their_EHR_DataGiving Patients Control of Their EHR Data2015-04-08T21:43:45Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>This is a review of David Blumenthal and David Squires’ (2015) article, Giving Patients Control of Their EHR Data. <ref name="control"> Blumenthal, D., Squires, D. (2015). Giving Patients control of their EHR data. Retrieved from http://link.springer.com.ezproxyhost.library.tmc.edu/article/10.1007%2Fs11606-014-3071-y </ref><br />
<br />
<br />
== Summary ==<br />
Should patient have control of who can view, access, and use their [[electronic health record | electronic health records (EHR)]]? The authors believe that patients should have full control of who can access and use their information as well as determining what information is recorded on their EHR. The federal government encourages privacy, transparency and accountability with healthcare information. Patients need to be very involved in the healthcare decisions this is reflected in the [http://www.nist.gov/nstic/NSTIC-FIPPs.pdf| Fair Information Practice Principles]<br />
In this article, the authors talk about why patients should have full control, the limitations, and also suggestions on how patient can better control their data. <br />
<br />
== Reasoning ==<br />
The authors believe patients should have full control of their own health information even if it will interfere with the patient's well being. Providers should respect the patient’s wishes and their right to control their health because patients are the only person that can make the best judgment for their own health.<br />
<br />
== Exceptions and Limitations==<br />
If patients control who and what can be accessed from their medical charts, they cannot hold the clinicians accountable for any negative outcomes with their care because certain information was withheld. Also, if the health of others is affected, then patients should not have control of their health data. For example, a patient with Ebola cannot restrict access to that information. <ref name="control"></ref> <br />
<br />
== Suggestions ==<br />
There should be training and education for clinicians and patients to learn more about patient health information. By educating patients about how their health data are used and shared, patients are more likely to make informed decisions of their own medical data.<br />
<br />
== Conclusions ==<br />
Patients should have control over their own health information. However, it is very important to educate and inform patients on how their information is used so that they can make the best decision for their own well-being.<br />
<br />
== Comments ==<br />
Initially, when I started reading this article, I thought that patients should not have full control over their own health data because if they refuse to share crucial health information, it can be dangerous for their health? However, after understanding the authors reasoning, I too have to agree that patients have a right to control their own data and health. But it is important for patients to be properly informed so that they can see what options they have.<br />
Read: [[Patient Healthcare Records|Patient Healthcare Records]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: Security]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Mobile_Technology_Era:_Potential_Benefits_and_the_Challenging_Quest_to_Ensure_Patient_Privacy_and_ConfidentialityThe Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality2015-04-08T21:29:50Z<p>Vishaghera: /* Safeguards */</p>
<hr />
<div>The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality. <ref name="Rodriguez-Feliz, Jose R."> Rodriguez-Feliz, Jose R.. The Mobile Technology Era. Plastic and reconstructive surgery (2012 ) </ref>.<br />
<br />
=== Introduction===<br />
Smartphones and mobile devices have a profound effect on all and have become a part of everyone’s personal and professional lives, including those of doctors. The effect of using these devices on patient confidentiality is assessed <br />
<br />
=== Impact of Mobile Devices===<br />
Physicians and surgeons increasingly use smartphones, mobile gadgets especially mobile photography in professional and academic setups to facilitate patient care management, communication with other health care professionals and as a tool to streamline and hasten workflows, potentially reducing costs. <br />
<br />
===Concerns===<br />
The potentially insecure storage of confidential patient information on mobile devices and electronic transmission of this data over cloud based networks etc are a source of concern and violation of the Privacy and Security Rules of Health Insurance Portability and Accountability Act of 1996 ( [[HIPAA|HIPAA]]). The confidentiality of health information is at risk not only by the risk of improper access to the devices, but also by the risk of interception during electronic transmission. These concerns are weighed against the benefits of rapid interventions, physician and resident education and patient care to determine the best way to incorporate the use of mobile devices without affecting patient confidentiality<br />
<br />
=== Safeguards===<br />
<br />
1. The Privacy Rule<br />
<br />
Privacy concerns are weighed against the benefits of rapid interventions, physician and resident education and patient care to determine the best way to incorporate the use of mobile devices without affecting patient confidentiality. The Privacy Rule was finalized on August 14, 2002. The goal was to establish national standards to protect all “individually identifiable health information” held or transmitted by a covered entity or its business associate, in any form or media, whether electronic, paper, or oral”, called protected health information.<br />
<br />
2. The Security Rule <br />
<br />
With the purpose of adopting safeguards to protect the confidentiality, integrity, and availability of electronic protected health information, the Security Rule of the Administrative Simplification provisions was made effective on April 21, 2003. The Security Rule protects a subset of information which is all Individually Identifiable Health Information a covered entity creates, receives, maintains or transmits in electronic form ( not applicable to protected health information transmitted orally or in writing). The Security Rule requires the covered entities to review and modify their security measures to continue protecting electronic protected health information in a changing environment.<br />
<br />
=== Conclusion and Discussion===<br />
It is very important for all people having access to patient data to be familiar with these rules and safeguards and to be very careful in the use and transmission of electronic information. They also have the responsibility of assuring patients, providers and other stakeholders of the security of their protected information<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category:HIPAA]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Mobile_Technology_Era:_Potential_Benefits_and_the_Challenging_Quest_to_Ensure_Patient_Privacy_and_ConfidentialityThe Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality2015-04-08T21:28:57Z<p>Vishaghera: </p>
<hr />
<div>The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality. <ref name="Rodriguez-Feliz, Jose R."> Rodriguez-Feliz, Jose R.. The Mobile Technology Era. Plastic and reconstructive surgery (2012 ) </ref>.<br />
<br />
=== Introduction===<br />
Smartphones and mobile devices have a profound effect on all and have become a part of everyone’s personal and professional lives, including those of doctors. The effect of using these devices on patient confidentiality is assessed <br />
<br />
=== Impact of Mobile Devices===<br />
Physicians and surgeons increasingly use smartphones, mobile gadgets especially mobile photography in professional and academic setups to facilitate patient care management, communication with other health care professionals and as a tool to streamline and hasten workflows, potentially reducing costs. <br />
<br />
===Concerns===<br />
The potentially insecure storage of confidential patient information on mobile devices and electronic transmission of this data over cloud based networks etc are a source of concern and violation of the Privacy and Security Rules of Health Insurance Portability and Accountability Act of 1996 ( [[HIPAA|HIPAA]]). The confidentiality of health information is at risk not only by the risk of improper access to the devices, but also by the risk of interception during electronic transmission. These concerns are weighed against the benefits of rapid interventions, physician and resident education and patient care to determine the best way to incorporate the use of mobile devices without affecting patient confidentiality<br />
<br />
=== Safeguards===<br />
<br />
1. The Privacy Rule<br />
Privacy concerns are weighed against the benefits of rapid interventions, physician and resident education and patient care to determine the best way to incorporate the use of mobile devices without affecting patient confidentiality. The Privacy Rule was finalized on August 14, 2002. The goal was to establish national standards to protect all “individually identifiable health information” held or transmitted by a covered entity or its business associate, in any form or media, whether electronic, paper, or oral”, called protected health information.<br />
<br />
2. The Security Rule <br />
With the purpose of adopting safeguards to protect the confidentiality, integrity, and availability of electronic protected health information, the Security Rule of the Administrative Simplification provisions was made effective on April 21, 2003. The Security Rule protects a subset of information which is all Individually Identifiable Health Information a covered entity creates, receives, maintains or transmits in electronic form ( not applicable to protected health information transmitted orally or in writing). The Security Rule requires the covered entities to review and modify their security measures to continue protecting electronic protected health information in a changing environment.<br />
<br />
=== Conclusion and Discussion===<br />
It is very important for all people having access to patient data to be familiar with these rules and safeguards and to be very careful in the use and transmission of electronic information. They also have the responsibility of assuring patients, providers and other stakeholders of the security of their protected information<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category:HIPAA]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Mobile_Technology_Era:_Potential_Benefits_and_the_Challenging_Quest_to_Ensure_Patient_Privacy_and_ConfidentialityThe Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality2015-04-08T21:05:27Z<p>Vishaghera: Created page with "The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality. <ref name="Rodriguez-Feliz, Jose R."> Rodriguez-Feliz, J..."</p>
<hr />
<div>The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality. <ref name="Rodriguez-Feliz, Jose R."> Rodriguez-Feliz, Jose R.. The Mobile Technology Era. Plastic and reconstructive surgery (2012 ) </ref>.<br />
<br />
=== Introduction===<br />
Smartphones and mobile devices have a profound effect on all and have become a part of everyone’s personal and professional lives, including those of doctors. The effect of using these devices on patient confidentiality is assessed <br />
<br />
=== Impact of Mobile Devices===<br />
Physicians and surgeons increasingly use smartphones, mobile gadgets especially mobile photography in professional and academic setups to facilitate patient care management, communication with other health care professionals and as a tool to streamline and hasten workflows, potentially reducing costs. <br />
<br />
===Concerns==<br />
The potentially insecure storage of confidential patient information on mobile devices and electronic transmission of this data over cloud based networks etc are a source of concern and violation of Health Insurance Portability and Accountability Act of 1996 ( [[HIPAA|HIPAA]].<br />
<br />
<br />
===References===<br />
<references/><br />
<br />
<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Health_Insurance_Portability_and_Accountability_Act_(HIPAA)Health Insurance Portability and Accountability Act (HIPAA)2015-04-08T20:52:58Z<p>Vishaghera: /* Implications to Clinical Information Systems */</p>
<hr />
<div>The '''Health Insurance Portability and Accountability Act (HIPAA)''' sets national minimum privacy requirements for personal, [[Protected Health Information (PHI)|protected health information (PHI)]]. It protects the security and privacy of health data. HIPAA also encourages electronic data interchange among different [[EMR|electronic medical record]] systems.<br />
<br />
== History==<br />
<br />
* In 1996 August 21, the United States Congress enacted the Health Insurance Portability and Accountability Act (HIPAA). It is also known as the Kennedy-Kassebaum Act.<br />
* The HIPAA privacy rule went into effect in 2003, implementing the privacy requirements of HIPAA.<br />
* The HIPAA security rule went into effect in 2005, implementing the security requirements of HIPAA.<br />
* The HIPAA enforcement rule went into effect in 2006, specifying sanctions for violations of HIPAA privacy and security rules.<br />
* The American Recovery and Reinvestment Act (ARRA), signed by President Obama on February 17, 2009, included Title XIII – the Health Information Technology for Economic and Clinical Health (HITECH) Act, which includes several amendments to HIPAA.<br />
*As of this date, 11/24/2012, the HITECH-HIPAA Omnibus Rule is pending OMB final review (HHS sent the Rule to OMB 3/24/2012) and will include the final rules for:<br />
**Breach notification (interim rule in effect since 8/2009)<br />
**Enforcement (interim rule in effect since 10/2009)<br />
**Privacy and Security (Notice of Proposed Rulemaking released 7/2010)<br />
**Genetic Information Nondiscrimination Act (NPRM released 10/2009)<br />
<br />
== Purpose of HIPAA ==<br />
<br />
The purpose of HIPAA is to improve the efficiency, effectiveness, and security of the national health system.<br />
<br />
* Increase efficiency: paper work is reduced for healthcare providers due to an electronic system.<br />
* Reduce [[fraud]] and abuse: digital paper trail makes fraud prosecution easier.<br />
* Portability: an employee is guaranteed health insurance coverage, even when he changes jobs. <br />
* Security: increased security for [[Protected Health Information (PHI)|patient health information]] and protect patient rights.<br />
* Accountability: protecting health data integrity, confidentiality and availability.<br />
<br />
== Security standards ==<br />
<br />
Security refers to the ability to control access and protect information from disclosure to unauthorized persons.<br />
<br />
To comply with the security standards, an [[EMR|electronic medical record (EMR)]] must have written, comprehensive security policies, access controls, control over the physical environment, clearance procedures, and a record of all access authorizations.<br />
<br />
Risk Assessment – Technical Safeguards<br />
<br />
== Background ==<br />
One of the lessons learned coming out of The Office for Civil Rights (OCR) HIPAA Audit Program is that we must understand where ePHI is and what our team members and business partners are doing with it. Operational practices and controls must safeguard every record, all the time. Audit controls must be designed and documented to account for ePHI and what activities around that ePHI need to be monitored, internally and with our business associates. Assessment validation of control continuance within those frameworks will become critical with Meaningful Use attestation and stage two requirements.<br />
<br />
=== Access control ===<br />
[[Access control|Access Control (164.312 (a)(1))]]<br />
HIPAA Standard: Implement technical policies and procedures for electronic information systems that maintain electronic protected health information to allow access only to those persons or software programs that have been granted access rights as specified in 164.308(a)(4) (note: this standard supports the Information Access Management Administrative Standard and Facility Access Controls Physical Standard) <br />
<br />
=== Unique User Identification ===<br />
Security users must have a unique and auditable identification number or credential that details when they access ePHI and the activity they perform on the information. <ref name="identifiability">Data Privacy Lab: Identifiability Project http://dataprivacylab.org/projects/identifiability/index.html</ref><br />
<br />
=== Emergency Access Procedure ===<br />
Covered entities must establish users who can access ePHI during an emergency. <br />
<br />
=== Automatic Logoff ===<br />
Implement electronic procedures that terminate an electron session after a predetermined time of inactivity. <br />
<br />
=== Encryption and Decryption ===<br />
This implementation specification ensures that confidentially of ePHI primarily focusing on data at rest. Covered entities must decide how and when to use encryption and decryption. <br />
<br />
Audit Controls (164.312 (b))<br />
HIPAA Standard: Implement hardware, software, and/or procedural mechanisms that record and examine activity information system that contain or use electronic protected health information to help ensure that systems have not been harmed by hackers, insiders, or technical problems. <br />
<br />
Integrity Controls (164.312 (c)(1)) Mechanism to Authenticate ePHI<br />
HIPAA Standard: Implement policies and procedures to protected electronic protected health information from improper alteration or destruction. <br />
<br />
Person or Entity Authentication (164.312 (d))<br />
HIPAA Standard: Implement procedures to verity that a person or entity seeking access to electronic protected health information is the one claimed. <br />
<br />
Transmission Security (164.312 (e)(1))<br />
HIPAA Standard: Implement technical security measures to guard against unauthorized access to electronic protected health information that is being transmitted over an electronic communication network. <br />
Integrity Controls<br />
Covered entities must implement controls to protect against message tampering during ePHI communications. These controls ensue that the received message is the same message that was sent.<br />
Encryption<br />
Covered entities must implement encryption controls where appropriate to protect ePHI.<br />
<br />
The HITECH Act amends HIPAA so that covered entities and business associates are required to notify individuals when their unsecured PHI is disclosed in a manner inconsistent with HIPAA privacy regulations. Such disclosure is known as a breach. Covered entities must have policies and procedures in place regarding breach notification, training of employees on these, and sanctions for violations of the same. Covered entities with a breach affecting 500 or more individuals will have their breach information posted on the HHS “wall of shame” web site: http://www.hhs.gov/ocr/privacy/hipaa/administrative/breachnotificationrule/breachtool.html.<br />
<br />
The HITECH Act specifies the following breach notification requirements [6]:<br />
* Individual Notice – Affected individuals must be notified within 60 days of a discovery of a breach of unsecured PHI. The notification must include a description of the breach, a description of the types of information that were involved in the breach, the steps affected individuals should take to protect themselves from potential harm, a brief description of what the covered entity is doing to investigate the breach, mitigate the harm, and prevent further breaches, as well as contact information for the covered entity.<br />
*Media Notice – A breach affecting more than 500 residents of a State or jurisdiction must notify prominent media outlets serving the State or jurisdiction within 60 days of discovery of the breach.<br />
*Notice to the Secretary – Covered entities must notify the Secretary of Health & Human Services of all breaches of unsecured PHI by submitting an electronic form on the HHS web site: http://ocrnotifications.hhs.gov/. If the breach affects 500 or more individuals such notice must be given within 60 days. If fewer than 500 individuals are affected, the notice may be given on an annual basis. <br />
*Notification by a Business Associate - If a vendor discovers a breach of unsecured PHI, it must notify the client within 60 days and provide information to the client for facilitate its ability to notify affected individuals<br />
<br />
== The Privacy Rule ==<br />
<br />
The [[Treatment, Payment and Operation (TPO)|Privacy Rule]] defines the minimum Federal standards for protection of patient data by a [[covered entity]] for research and other purposes. It specifies who is a Covered Entity, what [[Protected Health Information (PHI)|protected health information (PHI)]] is, and the conditions under which PHI can be distributed.<br />
<br />
In general, there are three ways that PHI can be distributed by a Covered Entity under the Privacy Rule. The first is by the creation of [[Identifiable Health Data|De-Identified Patient Data]]. This process theoretically removes all individually identifying information from the patient record, allowing the data to be used to research or financial gain without the ability to link to the information back to a particular person. In reality, this has not been completely successful.<br />
<br />
The second method is to get written permission from the patient to release their PHI. <br />
<br />
[http://www.hhs.gov/ocr/privacy/hipaa/understanding/summary/index.html]<br />
<br />
Lastly, an Institutional Review Board (IRB) can also allow for use of [[Protected Health Information (PHI)]] in specific situations for certain types of research.<br />
<br />
==Implications to Clinical Information Systems==<br />
* Personal Health Records (PHRs). See [[PHRs and HIPAA]]<br />
<br />
The 2009 HITECH Act, HIPAA provisions, will impact CIS in a number of ways, including:<br />
* Health Information Exchanges, vendors that offer a PHR as part of an EHR, and other organizations that transmit PHI to a covered entity or its business associate must enter into a business associate agreement with the covered entity and will now be held accountable to much of the HIPAA privacy and security provisions.<br />
* Vendors of CIS will need to sign business associates agreements with their clients<br />
*Vendors of CIS are now accountable for directly adhering to the privacy and security provisions of HIPAA<br />
*Vendors of CIS are subject to civil and criminal penalties for HIPAA violations<br />
*If a vendor discovers a breach of unsecured PHI, it must notify the client within 60 days and provide information to the client for facilitate its ability to notify affected individuals<br />
*Vendors of personal health records are required to abide by the same breach notification rules as covered entities and business associates<br />
*Individuals are entitled to receive an accounting for disclosures of PHI maintained in an EHR for a three year period prior to the date of the request. Under HITECH routine disclosures related to treatment, payment, and health care operations are no longer excluded. Therefore CIS will need to include a mechanism to track such disclosures to facilitate the covered entity’s ability to provide the information when requested<br />
*Individuals may request access to PHI, and transmission of PHI, in an electronic format if PHI is maintained in an EHR. Vendors will need to ensure this capability exists<br />
*Vendors will need to ensure that EHRs include mechanisms to securely de-identify patient information to facilitate sharing of data for public health and research purposes.<br />
<br />
*[[Reconciliation of the cloud computing model with US federal electronic health record regulations|Vendors of cloud-based EHR face particular challenges to achieve regulatory compliance (article review)]]<br />
[[The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality|The Mobile Technology Era: Potential Benefits and the Challenging Quest to Ensure Patient Privacy and Confidentiality]]<br />
<br />
== References ==<br />
<references/><br />
<br />
#<br />
# http://privacyruleandresearch.nih.gov/pdf/HIPAA_Privacy_Rule_Booklet.pdf<br />
#National Institute of Standards and Technology (NIST) Risk Management Guide for Information Technology Systems # http://csrc.nist.gov/publications/nistpubs/800-30/sp800-30.pdf<br />
#NIST Guide for Implementing Health Insurance Portability and Accountability Act # http://csrc.nist.gov/publications/nistpubs/800-66-Rev1/SP-800-66-Revision1.pdf<br />
#45 CFR Part 160 and 164 # http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/index.html<br />
#http://www.hhs.gov/ocr/privacy/hipaa/administrative/breachnotificationrule/index.html<br />
#http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/hitechblurb.html<br />
<br />
<br />
[[Category:BMI512-SPRING-12]]<br />
[[Category:BMI512-FALL-12]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Secondary_use_of_EMRSecondary use of EMR2015-04-08T20:13:53Z<p>Vishaghera: /* Confidentiality, privacy, security, and data access */</p>
<hr />
<div>The electronic health record (EHR) is the primary point of data capture for patient care. In addition to its primary purpose, EHRs can serve as data capture points for secondary uses such as clinical research.<br />
<br />
Secondary use of health data applies personal health information (PHI) for uses outside of direct health care delivery(1).<br />
<br />
EHR holds a great potential for supporting clinical research through improving efficiency, quality and reducing the cost of clinical trials. An optimal EHR can be developed to support research related activities including: clinical trials; comparative effectiveness quality measurement; and public health and safety monitoring, including post-marketing surveillance. <br />
<br />
<br />
==Advantages of secondary use health data for research purposes include but not limited to==<br />
<br />
# Observational and case series can be conducted quickly facilitating new research hypothesis for potential intervention.<br />
# Without the assistance of Information technology , recruitment is extremely slow, expensive, and low-yield. EHR can also aid in expediting the clinical trials by allowing the available database to be used for screening potential subjects .<br />
# Through data mining of data set the drug responses and potential toxicities, response to treatment long term sequeale and survival can be monitored for individual patients enrolled in the trials . Trough real time data collection clinical research , patient care and safety can be enhanced.<br />
<br />
<br />
==Challenges for secondary use of health data for clinical research==<br />
<br />
=== Confidentiality, privacy, security, and data access ===<br />
<br />
The most important concern is protecting the information from inappropriate use .<br />
This can be addressed through appropriate regulatory processes such as HIPPA and IRB approval/privacy board approval and modern security techniques such as access control encryption etc .<br />
<br />
[[Genetic Data Sharing and Privacy|Genetic Data Sharing and Privacy]]<br />
<br />
=== Standardization EHR data for Research purposes ===<br />
<br />
Standardization of data increase data accuracy, availability and enable data integration. <br />
Several initiatives such as (RCRIM) Regulated clinical research Information Model through HL7 and non profit initiative through Clinical data Interchangeable standards Consortium (CDISC) are now collaborating together to develop final model of data standardization to meet the needs of clinical research .<br />
<br />
=== Improving the quality of data research ===<br />
<br />
As mentioned above data standardization is critical for improving quality of research. In addition to standardization, data collected in real time at the point of care by the primary care provider will ensure accurate and consistent data collection as opposed to retrospective reviews.<br />
<br />
<br />
==As noted in HIT Policy Committee report (2) the following three areas were projected to have a positive impact on meaningfull use of EHR for clinical research==<br />
<br />
<br />
=== Supporting Opportunities for Patient Participation by ===<br />
<br />
Enabled interface can increasing patient interest and queries recarding the clinical trials thus doubling the enrollemnet in 3 years.<br />
Incentives can reduce costs and recruitment time for federally sponsored trials.<br />
<br />
<br />
=== Reducing Barriers for Provider Participation in Clinical Research ===<br />
<br />
By providing incentives for meaningful use of EHR greater provider participation in clinical research can be assured.<br />
<br />
=== Use of Standards-based EHR Data for Clinical Research ===<br />
<br />
==Use of standardized data can enhance efficiency and accuracy of data collected thus improving the quality of research==<br />
<br />
<br />
Thus by developing an optimal EHR capable of addressing the needs of clinical research with due consideration to privacy protection and data standardization will accelerate research and improve health care effectiveness , efficiency , reduce health care expenditure and enhance patient safety..<br />
<br />
== References ==<br />
<br />
# Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper J Am Med Inform Assoc. 2007 Jan–Feb; 14(1): 1–9. Charles Safran, MD, MS, Meryl Bloomrosen, MBA, W. Edward Hammond, PHD, Steven Labkoff, MD, Suzanne Markel-Fox, PHD, Paul C. Tang, MD, Don E. Detmer, MD, MA, <br />
# Designation of Clinical Research Information Integration as an Objective of“Meaningful Use” of Electronic Health Record Systems Presentation to the HIT Policy Committee Gregory Downing. <br />
<br />
Submitted by Nivedita Kumar<br />
<br />
<br />
<br />
== Additional stuff==<br />
<br />
The huge increase in coded health data generated by electronic medical records has an enormous potential to increase our ability to do clinical research. Compared to traditional research methods, there are many potential benefits and detriments to secondary use of clinical data. <br />
<br />
<br />
==Limitations==<br />
Limitations of secondary use of clinical data are that of retrospective research, and which is inherently subject to many sources of bias and error. With prospective study design, a research question is posed and the study designed to be able to accurately measure and analyze the data required to answer the question. Inconsistencies of medical terminology are a recognized challenge to research validity (misclassification bias), and each research plan requires careful attention to the definitions necessary to answer the question. The conditions to be studied, the treatments rendered, and the outcomes to measure are carefully defined. Templates are designed to enhance accuracy and minimize missing data. Potential sources of bias and confounding are considered and managed.<br />
<br />
==Case study==<br />
<br />
The studies of estrogen therapy after menopause are an excellent example of bias and erroneous findings in retrospective studies. Briefly, before the Women’s Health Initiative (WHI) results were published 2002 (Rossouw JE, JAMA, 2002) (a randomized trial of estrogen therapy in menopausal women), there were numerous retrospective studies indicating that women who used menopausal hormone therapy had a 50% reduction in death from heart disease. Women were encouraged to take estrogen by clinicians as a strategy for reducing heart disease. A question posed by many researchers was “Did this finding occur because 1) estrogen improves cardiovascular function or 2) healthier women choose estrogen more often than less healthy women?” (selection bias). The randomized trial found that estrogen did NOT confer a cardiac benefit, and now estrogen is NOT recommended as a strategy for reducing heart disease. This story emphasizes the magnitude and impact of potential errors that may result from retrospective research. Note--this illustration is simplified, and does not represent the complexities of an individual woman’s benefit or risk of taking hormone therapy.)<br />
<br />
Understanding the potential limitations of secondary use of data will facilitate changes to mitigate the risks. Already there is much emphasis on improving the clarity of medical terminology. Research questions can be “designed in” to EMR’s to accurately capture the data needed to answer the question, using templates, drop-down menus with definitions provided (research decision support). Currently the risks of secondary use of data are large, but it is within the realm of EMR design to mitigate these risks. When such design changes have been implemented, EMR’s will be able to provide a rich source of data for analysis for clinical research to enhance human health.<br />
<br />
[[Category:BMI512-W-10]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Implementation_of_a_simple_electronic_transfusion_alert_system_decreases_inappropriate_ordering_of_packed_red_blood_cells_and_plasma_in_a_multi-hospital_care_systemImplementation of a simple electronic transfusion alert system decreases inappropriate ordering of packed red blood cells and plasma in a multi-hospital care system2015-04-03T04:39:06Z<p>Vishaghera: </p>
<hr />
<div>The following is a review of the article, “Implementation of a simple electronic transfusion alert system decreases inappropriate ordering of packed red blood cells and plasma in a multi-hospital care system" <ref name="Smith"> Smith, M., Triulzi, D. J., Yazer, M. H., Rollins-Raval, M. A., Waters, J. H., & Raval, J. S. <br />
(2014). Implementation of a simple electronic transfusion alert system decreases <br />
inappropriate ordering of packed red blood cells and plasma in a multi-hospital care <br />
system. Transfusion and Apheresis Science, 51(3), 53-58. <br />
</ref>.<br />
<br />
<br />
== Abstract ==<br />
The authors of this article understand the major role that [[CPOE|Computerized Physician order Entry (CPOE)]] may have in preventing physicians and nurses from ordering blood transfusions when the patient did not meet the institutional transfusion criteria. There are many supporting articles that have shown a decrease in ordering lab activities when a [[CPOE| CPOE]] was used during the time when the order entry is being written. This article will focus on the effects of [[CPOE| CPOE]] on reducing [http://learn.fi.edu/learn/heart/blood/red.html-RBC/ Red Blood Cells (RBC)] and plasma orders that not meet the criteria for institutional transfusion.<br />
<br />
==Methods==<br />
10 hospitals in a regional healthcare system used an institutional transfusion guideline that required the patient’s hemoglobin to be less than or equal to 8gm/dl in order to qualify for [http://learn.fi.edu/learn/heart/blood/red.html-RBC/ RBC]transfusion and an international normalized ration (INR) was greater than or equal to 1.6 in 24 hours before the order is written. While writing for the [http://learn.fi.edu/learn/heart/blood/red.html-RBC/ RBC] order or the plasma order, an alert would be triggered for the physician or nurse if the patient did not meet the institutional transfusion guideline. Data was collected over a 15 month period for the [http://learn.fi.edu/learn/heart/blood/red.html-RBC/ RBC] orders and a 10 month period for the plasma orders.<br />
<br />
== Results ==<br />
The study was able to establish that alerts from [[CPOE|CPOE]] was able to reduce transfusion orders that were not evidence based and did not meet the institutional transfusion criteria. Physicians and nurses cancelled 11.3% of [http://learn.fi.edu/learn/heart/blood/red.html-RBC/ RBC] orders and 19.6% of plasma orders after an alert was triggered.<br />
<br />
== Comments ==<br />
This article served as a good reminder that orders are sometimes written that are not evidence based. This leads to poor quality of care for patients, wastes necessary medical resources, and create unnecessary expenses. I think alerts that occur simultaneously when orders are written are a good thing but there needs to be a balance to the alerts because too many alerts are overwhelming and frustrating and too little alerts allow for errors to occur.<br />
<br />
Related Read: [[Factors contributing to an increase in duplicate medication order errors after CPOE implementation |Factors contributing to an increase in duplicate medication order errors after CPOE implementation ]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]<br />
[[Category: nurse, physician]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Patient_Accessible_Electronic_Health_Records:_Exploring_Recommendations_for_Successful_Implementation_StrategiesPatient Accessible Electronic Health Records: Exploring Recommendations for Successful Implementation Strategies2015-04-03T04:34:57Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>=== Abstract ===<br />
The purpose of this article is to develop recommended strategies for successful implementation of patient accessible [[EHR|electronic health records]]. <ref name= "Special">Wiljer, D., Urowitz, S., Apatu, E., DeLenardo, C., Eysenbach, G., Harth, T., ... & Canadian Committee for Patient Accessible Health Records (CCPAEHR. (2008). Patient accessible electronic health records: exploring recommendations for successful implementation strategies. Journal of Medical Internet Research, 10(4). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629367/ </ref><br />
<br />
=== Methods ===<br />
The Patient Accessible Electronic Health Record (PAEHR) workshop was held in Toronto, Canada in 2006<ref name= "Special"></ref> where experts from around the world met to discuss the issues facing allowing patients to have access to the EHR, as well as the type institutional changes which would be required after such a change is made. The objective was to compose a draft with recommendations that would aid health care organizations in providing patients access to their electronic health record in a responsible way.<ref name= "Special"></ref><br />
<br />
=== Results ===<br />
Forty-five participants attended the workshop and made recommendations in the following four categories<ref name= "Special"></ref>: <br />
* Providing patient access to the EHR<br />
* Maintaining [[privacy]] and [[confidentiality]] related to the PAEHR<br />
* Patient education and navigation of the PAEHR<br />
* Strategies for managing institutional change<br />
The discussion focused on the need for “clear definitions for privacy, security and confidentiality, flexible, interoperable solutions, and patient and professional education” <ref name= "Special"></ref> in addition to advocating for additional research to be made to ensure patient accessible EHRs are designed with [[EBM|evidence-based practice]] in mind.<br />
=== Conclusions ===<br />
It is very important for patients to be able to access their personal health information as it is a crucial requirement for increasing patient engagement and empowerment; but health care organizations should carefully weigh the pros and cons of such an endeavor beforehand.<br />
<br />
=== Comments ===<br />
I agree with the conclusion of the review as people should be interested in their own health and well-being as they are their own best advocates; and providing access to their personal health information may be a good way to increase interest.<br />
<br />
Related Read: [[Implementing Patient access to Electronic Health Records under HIPAA: Lessons learned|Implementing Patient access to Electronic Health Records under HIPAA: Lessons learned]]<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category: EHR]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Nursing_domain_of_CI_governance:_recommendations_for_health_IT_adoption_and_optimizationNursing domain of CI governance: recommendations for health IT adoption and optimization2015-04-03T04:32:00Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>This article shows the importance of organizational leaders and how they can make implementation and adoption better. <ref name="cDDs8"> Collins, S. A., Alexander, D., & Moss, J. (2015). Nursing domain of CI governance: recommendations for health IT adoption and optimization. Journal of the American Medical Informatics Association, ocu001. http://jamia.oxfordjournals.org/content/early/2015/02/09/jamia.ocu001 </ref><br />
<br />
== Background ==<br />
<br />
There is a lack of recommended models for [[clinical informatics]] governance that can facilitate successful health information technology implementation. The objective is to understand existing CI governance structures and provide a model with recommended roles, partnerships, and councils based on perspectives of nursing informatics leaders.<br />
<br />
== Methods ==<br />
<br />
They conducted a cross-sectional study through administering a survey via telephone to facilitate semistructured interviews from June 2012 through November 2012. They interviewed 12 nursing informatics leaders, across the United States, currently serving in executive- or director-level CI roles at integrated health care systems that have pioneered electronic health records implementation projects.<br />
<br />
<br />
== Results ==<br />
<br />
They found the following 4 themes emerge: (1) Interprofessional partnerships are essential. (2) Critical role-based levels of practice and competencies need to be defined. (3) Integration into existing clinical infrastructure facilitates success. (4) CI governance is an evolving process. <br />
<br />
== Conclusion ==<br />
<br />
Applied clinical informatics work is highly interprofessional with patient safety implications that heighten the need for best practice models for governance structures, adequate resource allocation, and role-based competencies. Overall, there is a notable lack of a centralized CI group comprised of formally trained informaticians to provide expertise and promote adherence to informatics principles within EHR implementation governance structures.<br />
<br />
== Comments == <br />
<br />
It is very important that organizations that have successfully implemented EHR share their strategies and lessons learned to others who are implementing their own. We should not only learn from our mistakes but also from the mistakes of others. This is the only way we can make our health systems better.<br />
<br />
Related Read:[[Using special people in a computerized physician order entry system implementation: Removing barriers to success|Using special people in a computerized physician order entry system implementation: Removing barriers to success]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:EHR]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Patient_Confidentiality_in_the_Research_Use_of_Clinical_Medical_DatabasesPatient Confidentiality in the Research Use of Clinical Medical Databases2015-04-03T04:15:09Z<p>Vishaghera: </p>
<hr />
<div>The following article is adapted from Krishna R, Kelleher K, and Stahlberg E's "Patient Confidentiality in the Research Use of Clinical Medical Databases".<ref>Krishna R, Kelleher K, and Stahlberg E. Patient Confidentiality in the Research Use of Clinical Medical Databases. ''Am J Public Health. 2007 April; 97(4):'' 654–658</ref><br />
= =<br />
Modern computing power provided quantitative researchers with new techniques for exploring and identifying correlations in large [[Data warehouse |data warehouses]].<ref>Castellani B, Castellani J. Data mining: qualitative analysis with health informatics data. Qual Health Res. 2003; 13:1005–1018</ref> Common to such efforts in the need for access to large quantities of potentially sensitive [[protected health information]]([[PHI]]).<br />
Interest in maintaining-and legal sanctions for-violating patient [[Confidentiality|confidentiality]] are of particular concern for researchers who use medical data. Balancing the conflicting interests of ensuring patient confidentiality with providing access to sufficiently detailed information for adequate research is a serious challenge to healthcare organizations(HCOs), data providers, and their respective [[Institutional Review Board (IRB)| Institutional Review Board (IRBs)]]. Although existing legal restrictions in the United States attempt to strike a balance, no computing system is entirely secure, and there is understandable concern about unintended or inappropriate releases of information.<br />
<br />
Application of [[Data security|data security]] and statistical disclosure techniques allows trade offs between data usability and data security, giving researchers access to relevant data while at the same time minimizing the potential damage of a breach in data security. This authors' discussion is intended to be an introduction for researchers and their human participant oversight structures about the best security solutions for a given situation.<br />
<br />
= '''DEFICIENCIES OF CURRENT REGULATIONS''' =<br />
In the United States, current regulations on the use of PHI under [[HIPAA]] divide medical record sets into 3 categories: identified data, deidentified data, and limited data.<br />
* ''[[Identified data]]'' include any data that could be used by a recipient to uniquely identify the person from an individual patient record. Access to such record requires explicit consent by study participants or a waiver of consent requirement by an IRB. This further involves numerous restrictions that involve tracking of PHI disclosures<br />
* ''[[Deidentified data]]'' is data with all HIPAA-specified 18-data elements removed and this data may be used freely.<br />
* ''[[Limited data]]'' are available only to research, public health, and HCOs. Researchers may access data elements without restrictions for fully identified data.<br />
<br />
Considerable research in privacy-preserving [[data mining]],<ref>Agrawal R, Srikant R. Privacy-preserving data mining. In: Proceedings of 2000 ACM SIGMOD Conference on Management of Data; May 16–18, 2000; Dallas, Tex</ref> <ref>Verykios V, Bertino E, Fovino I, Provenza L, Saygin Y, Theodoridis Y. State-of-the-art in privacy preserving data mining. ACM SIGMOD Record. 2004;33:50–57</ref> disclosure risk assessment,<ref>Steel P. Disclosure risk assessment for microdata. Available at: http://www.census.org/srd/sdc/steel.disclosure\%20risk\%20assessment\%20for\%20microdata.pdf. Accessed June 2005</ref> <ref>Domingo-Ferrer J, Torra V. Disclosure risk assessment in statistical data protection. J Computational Appl Math. 2004;164:285–293</ref> and data deidentification,[[obfuscation]], and protection,<ref>Sweeney L. Computational Disclosure Control: A Primer on Data Privacy Protection [PhD thesis]. Cambridge, Mass: Massachusetts Institute of Technology; 2001</ref> <ref>Bakken DE, Rarameswaran R, Blough DM, Franz AA, Palmer TJ. Data obfuscation: anonymity and desensitization of usable data sets. IEEE Secur Privacy. 2004;2:34–41</ref> found in computing and database management literature is often directly applicable to medical privacy issues. Researchers do not exploit the flexibility of disclosure limitation techniques. Instead, they depend on deidentified data or the physical security of data infrastructure.<br />
<br />
This tendency gives decision makers the impression that deidentified data is safe for public use and data security restrictions on the use of identified data sets will ensure confidentiality. The greatest concern is that little effort is applied to the documentation of the data security efforts when the results of an analysis is published.<br />
<br />
= '''METHODS OF DATA SECURITY''' =<br />
== Data exclusion ==<br />
Exclusion of specific data elements is the basis of most general restrictions on data use. In this realm, carefully constructed aggregate data or removal of entire records provide the highest level of confidentiality. Second to this is individual record deidentification. The goal is to verify that deidentification process maximizes data a particular researcher needs while ensuring sufficient commonality between records for anonymity.<br />
The concept of [http://en.wikipedia.org/wiki/K-anonymity ''k''-anonymity]<ref>Sweeney L. K-anonymity: a model for protecting privacy. Int J Uncertainty, Fuzziness Knowledge-Based Syst. 2002; 10:557–570</ref> and the use of systems such as Datafly<ref>Sweeney L. Datafly: a system for providing anonymity in medical data. In: Lin TY, Qian S, eds. Database Security XI: Status and Prospects. New York, NY: Chapman & Hall; 1998:356–381</ref> ensure that at least ''k'' records in any data set are indistinguishable along any parameter of interest. Field masking can maintain specific aspects of the data set that are of research interest. Concept Match<ref>Berman J. Concept–match medical data scrubbing: how pathology text can be used in research. Arch Pathol Lab Med. 2003;127:680–686</ref> provides a system for deidentifying free text fields by removing words that do not match a predetermined set of interest words for a domain.<br />
<br />
== Data transformation ==<br />
Data transformation[http://en.wikipedia.org/wiki/Data_transformation] techniques provide a statistical guarantee of confidentiality. The common theme in these techniques is to make an irreversible modification to the data that destroys the original values or correlations while prescribing the relationships of interest. As with data exclusion, techniques exist to modify data globally or at the level of individual elements.<br />
<br />
Data perturbation is an example of global data transformation. The idea is to preserve aggregate trends in the original data while removing or altering the actual data.<br />
<br />
Hashing of individual data elements involves a lossy 1-way transformation or mapping of data. A simple hash of 20 unique zip codes(protected under HIPAA)may replace each unique zip code with a value between 1 and 1000 at each entry in the data set. This transformation probabilistically maintains the uniqueness of zip code values and thus preserve much of the research value. Many standard hashing algorithms exist include the Message-Digest Algorithm(MD5)[http://en.wikipedia.org/wiki/MD5]<ref> Rivest R. The MD5 message digest algorithm. Available at: http://www.faqs.org/rfcs/rfc1321.html. Accessed December 22, 2006</ref> developed at the M.I.T[http://en.wikipedia.org/wiki/Massachusetts_Institute_of_Technology], and the Secure Hash Algo-rithm1(SHA1)[http://en.wikipedia.org/wiki/SHA-1] developed by N.I.S.T[http://en.wikipedia.org/wiki/National_Institute_of_Standards_and_Technology].<ref>Schneier B. Applied Cryptography: Protocols, Algorithms, and Source Code in C. 2nd ed. New York, NY: John Wiley & Sons; 1995</ref><br />
<br />
== [[Data encryption]] ==<br />
A further step from absolute confidentiality leads to reversible data transformations,such as data element encryption. The idea of encryption[http://en.wikipedia.org/wiki/Encryption] is to take input data(plain text) and output new data(cipher text)[http://en.wikipedia.org/wiki/Ciphertext] from which the original cannot be recovered without the use of a key. A simple example would be to create a 1-to-1 mapping letter for each letter in the alphabet or a code.<br />
<br />
A good cryptographic technique will hide all relationships between the original text and cipher text. This however creates a problem for researchers,particularly in situations of semi-free text fields. If the name is entered as free text, small variations in the entry(eg., Rajanis vs Rajani's) could lead to substantial variations in the cipher text. Fixing these variations in letters used(syntax) for words with the same meaning is a process called ''Normalization''[http://en.wikipedia.org/wiki/Data_normalization].<br />
<br />
Good encryption is not a substitute for good data access security. This can at best be an added safeguard. Given time,nearly every cryptographic technique can be compromised.<br />
<br />
Another important lesson from cryptography[http://en.wikipedia.org/wiki/Cryptography] is the value of variability in data. Although constructing research data sets to establish one uniform deidentified data set for all researchers is the easiest and sometimes the only solution, it also increases the risk of exposure. This risk can be reduced by a number of simple steps. Ideally, individual data sets should be constructed for each research effort. Furthermore, each data set should be encoded independently. The ordering of individual records should be randomized whenever possible. This ensures that data from a research project cannot be compared against data from another, reduces the potential of a security breach, and limits the damage should a breach occur.<br />
<br />
== Data obfuscation ==<br />
''Data obfuscation'' is an approach of masking data that is weaker than cryptography and is employed primarily to preserve relationships within data set that would be destroyed by more rigorous masking techniques. Although this complicates recovery of the original information and reduces the pool of would-be intruders, it does not provide the level of structural security that encryption or hashing systems[http://en.wikipedia.org/wiki/Cryptographic_hash_function] do.<br />
<br />
= '''MAXIMIZING DATA SECURITY IN RESEARCH''' =<br />
== What data is needed? ==<br />
The obvious first step in any data protection is careful specification of the data requirement. This is the standard practice in most research efforts,and consideration should be given for what records are necessary. Furthermore,as research progresses,access to any subsets of data deemed unnecessary upon inspection should be removed.<br />
<br />
== What data can be encrypted? ==<br />
Any relevant relationships discovered in transformed or obfuscated data would be useless without the ability to recover original values. In the general case, this will require that at least one field be masked in a recoverable fashion. Should an intruder recover the encrypted data, exploiting the information would still require breaking the security of the main data base to gain full access to the record.<br />
<br />
== What data should be transformed or obfuscated? ==<br />
Researchers should now determine confidentiality and the level of acceptable data loss for each field in the desired records. These fields that only require aggregate properties or probabilisitic uniqueness should be masked by lossy transformation techniques. However, some effort should be dedicated to considering how such data elements may be obfuscated without destroying relevant relationships.<br />
<br />
== Establishing the confidentiality of remaining data ==<br />
It is clearly impractical and often detrimental to mask or obfuscate every field in a data set. As a final step in the construction of a research data set, it may be valuable to assess any remaining unique records. Application of techniques such as a ''k''-anonymity can ensure that.<br />
<br />
== Physical data security and auditing ==<br />
The best defense is good physical data security. Standard data security practices should be used to ensure that the data remain in a secure access-restricted storage area and separate unique credentials should be given to each authorized user. A short training session on basic data security may be warranted for study staff.<br />
<br />
= '''CONCLUSIONS''' =<br />
Data security is of particular relevance with the proliferation of electronic medical and administrative records and the ease with which data can be exported outside of the secure institutional infrastructure. The authors described an approach that researchers,information services departments, and IRB committees can use. Indeed,coordination among these groups and the incorporation of security considerations into IRB and journal approval procedures are the keys to ensuring continued patient data protection in an increasingly digital and interconnected world.<br />
<br />
= '''COMMENTS''' =<br />
Maintaining patient confidentiality is of paramount importance to researchers. Balancing this with access to sufficiently detailed information for adequate research is a big challenge to HCOs and their respective IRBs. There is a lack of common vocabulary/infrastructure for describing data security measures. I totally agree with the authors who should be sincerely commended for producing such a crucial work of introducing a framework that researchers and IRBs can use in applying data security techniques.<br />
<br />
===Related Reads===<br />
[[Professionalism in Medical Informatics|Professionalism in Medical Informatics]]<br />
<br />
= '''REFERENCES''' =<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Technologies]]<br />
[[Category: Training and User Support]]<br />
[[Category: Methodologies and Frameworks]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:52:09Z<p>Vishaghera: </p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
The implementation of [[CPOE|CPOE]]/ [[CDS|CDS]] is extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflows, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart reviews, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors and their causes,in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
*1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
<br />
*2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
<br />
*3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
Related Read: [[Duplicate orders: an unintended consequence of computerized provider/physician order entry (CPOE) implementation: analysis and mitigation strategies|Duplicate orders: an unintended consequence of computerized provider/physician order entry (CPOE) implementation: analysis and mitigation strategies]]<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:49:27Z<p>Vishaghera: /* Causative Factors */</p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
The implementation of [[CPOE|CPOE]]/ [[CDS|CDS]] is extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflows, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart reviews, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors and their causes,in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
*1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
<br />
*2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
<br />
*3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:48:54Z<p>Vishaghera: /* Design */</p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
The implementation of [[CPOE|CPOE]]/ [[CDS|CDS]] is extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflows, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart reviews, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors and their causes,in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:47:45Z<p>Vishaghera: /* Introduction */</p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
The implementation of [[CPOE|CPOE]]/ [[CDS|CDS]] is extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflows, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:46:59Z<p>Vishaghera: /* Introduction */</p>
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<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
Implementation of [[CPOE|CPOE]]/ [[CDS|CDS]] are extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflow, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:46:16Z<p>Vishaghera: </p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
Implementation of CPOE/ CDS are extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflow, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Results===<br />
Data were collected on 630 patients, 45 658 medication orders, and 4147 patient-days pre-CPOE, and 625 patients, 32 841 medication orders, and 4013 patient-days post-CPOE. The number of duplicate medication orders increased after CPOE implementation (pre: 48 errors, 1.16 errors/100 patient-days; post: 167 errors, 4.16 errors/100 patient-days; p<0.0001) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Causative Factors===<br />
1. Number of providers involved in care of patient and entry of orders.<br />
Due to various causes including lack of visibility of previous doctor’s orders, involvement of multiple caregivers creating orders and alert fatigue and overriding of orders.<br />
2. Inadequate duplicate alert algorithms and design<br />
Lack of detection of duplicate orders due to passage of an interval of time between orders, different routes of administration etc.<br />
3. Duplicate alert design and physician perception of usefulness of alerts<br />
Multiple false positive alerts lead to alert fatigue among physicians leading to the masking of the true positive alert.<br />
<br />
=== Discussion===<br />
As seen in this study, implementation of EMR systems like CPOE and CDS requires a robust change management plan. Training and gradual implementation is important as well as continuous improvement activities catering to the adoption and customization of the technology in the physicians and nurses’ workflows. Usability , teamwork and communication between stakeholders is essential<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:27:55Z<p>Vishaghera: </p>
<hr />
<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
<br />
=== Introduction===<br />
Implementation of CPOE/ CDS are extremely beneficial in simplifying and optimizing the order management system, streamlining physicians’ and nurses’ workflow, minimizing medication errors and improving healthcare delivery. However, the implementation has to be planned and executed carefully to avoid issues that might arise out of inadequate planning , lack of attention to integration with existing workflows or other unintended consequences<br />
<br />
===Objective===<br />
The objective of the study was to study the incidence of duplicate medication orders before and after the implementation of a CPOE/ CDS system and to study contributory factors for the same.<br />
<br />
===Design===<br />
The authors carried out a prospective pre and post implementation study in a 400-bed Northeastern US community tertiary care teaching hospital and used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs) <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
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===References===<br />
<references/><br />
<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Factors_contributing_to_an_increase_in_duplicate_medication_order_errors_after_CPOE_implementationFactors contributing to an increase in duplicate medication order errors after CPOE implementation2015-04-01T19:15:15Z<p>Vishaghera: Created page with "Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., ..."</p>
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<div>Factors contributing to an increase in duplicate medication order errors after CPOE implementation : <ref name="wetterneck"> Wetterneck, T. B., Walker, J. M., Blosky, M. A., Cartmill, R. S., Hoonakker, P., Johnson, M. A., … Carayon, P. (2011). Factors contributing to an increase in duplicate medication order errors after CPOE implementation. Journal of the American Medical Informatics Association : JAMIA, 18(6), 774–782. doi:10.1136/amiajnl-2011-000255. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198002/?tool=pmcentrez</ref>.<br />
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===References===<br />
<references/></div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/CPOECPOE2015-04-01T19:10:03Z<p>Vishaghera: /* Implementation Strategies */</p>
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<div>'''Computerized physician order entry (CPOE)''' is defined by the Healthcare Information and Management Systems Society (HIMSS) dictionary as an "order entry application specifically designed to assist clinical practitioners in creating and managing medical orders for patient services and medications". <ref name="himss definition">HIMSS dictionary of healthcare information technology terms, acronyms and organizations. (2010). Chicago, IL: Healthcare Information and Management Systems Society.</ref>. It is an [[EMR|electronic medical record]] technology that allows physicians to enter orders, medications, or procedures directly into the computer instead of handwriting them. <ref name="kuperman 2003">Kuperman & Gibson 2003. http://www.annals.org/content/139/1/31.abstract></ref><br />
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CPOE systems are becoming integral additions to electronic health records, being used by more practitioners in all areas of healthcare. Studies show that CPOE use can reduce medication errors and treatment orders, along with errors that often come when misreading providers’ handwriting. <ref name="love 2012">Love, J.S., Wright, A., Simon, S.R., Jenter, C.A., Soran, C.S., Volk, L.A., Bates, D.W., and Poon, E.G. (2012). Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use? Journal of American Medical Informatics Association, 19(4), 610-614. DOI 10.1136/amiajnl-2011-000544 http://www.ncbi.nlm.nih.gov/pubmed/22199017</ref> The system transmits the order to the appropriate department or individual so the order can be carried out. <ref name="improving outcomes">Osheroff JA, Pifer EA, Teich JM, Sittig DF, Jenders RA. Improving Outcomes with Clinical Decision Support. http://ebooks.himss.org/product/improving-outcomes-clinical-decision-support</ref> The most advanced implementations of such systems also provide real-time [[CDS|clinical decision support]] such as dosage and alternative medication suggestions, duplicate therapy warnings, and [[adverse drug event|drug-drug interaction]] checking. <ref name="improving outcomes"></ref><br />
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== History of CPOE ==<br />
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1969 was the founding of the [[Regenstrief Medical Record System (RMRS)|Regenstrief Institute]] in Indianapolis. [[Regenstrief Chair in Health Services Research -- Indiana University School of Medicine|Dr. Clement McDonald, MD]] introduced the idea of a longitudinal medical record encompassing inpatient and outpatient patient encounters. The [[Regenstrief|Regenstrief medical record system (RMRS)]] began in 1972 with 35 of Dr. Charles Clark's MD diabetic patients. In 1984, '''physician order entry''' also known as '''computerize provider order entry (CPOE)''' of outpatient medicines was initiated at a collaborating facility called the Wishard Memorial Hospital. Physician order entry was expanded to inpatient medication orders in 1990. <ref name="mcdonald 1999">McDonald,J.M. Improving Outcomes with Clinical Decision Support. The Regenstrief Medical Record System:a quarter century experience. http://www.ncbi.nlm.nih.gov/pubmed/10405881</ref><br />
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Purported benefits of Electronic Prescribing have included:<br />
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E-RX enhances pharmacy efficiency. For sure, electronic delivery of the prescription eliminates the tried and true problems of doctors scribbling and enables the pharmacist to prepare the prescription to ease patient pickup.<br />
E-RX promotes formulary adherence. Managed care organizations find that physicians choose the drugs for which they have contracted for cheaper purchase, thus it enhances their profits and perhaps promotes some quality where their pharmacy and therapeutic committee decision-making in all intents and purposes well assesses efficacy and cost-effectiveness of the various entities on the formulary.<br />
E-RX enhances prescribing errors by physicians being caught. Pharmacy software can check for the proper drug being prescribed at the right dosage in many cases so medication errors may be minimized.<br />
E-RX reduces adverse drug reactions (ADRs) by electronic entry into the pharmacy’s computer allowing patient allergies, past bad experiences with certain drugs, and drug-drug interactions to potentially be identified, also pending pharmacist intervention.<br />
E-RX may catch dosage errors, particularly in light of the differences between pediatric formulations and adult dosage levels. This can also be part of the assessment done electronically before the pharmacist prepares the prescription.<br />
E-RX decreases drug-drug interactions. Much existing pharmacy software already checks the patient’s profile (assuming that patients use just a single pharmacy) to raise flags to the pharmacist before dispensing about any potential of multiple drugs interacting.<br />
E-RX helps prevent injuries and reduce health costs. Alerts given to physicians reduce the likelihood and severity of ADRs, according to one study in the Archives of Internal Medicine.<br />
E-RX improves quality of care and reduces malpractice claims. Again, it is asserted to yield a reduction in medication misadventuring, reducing both physicians’ and pharmacists’ making mistakes. Most of these depend upon the pharmacist’s vigilance in interacting with a well-designed clinical software system with a caring professional role. EMRs in one study in the Archives of Internal Medicine saw an association with “a significant reduction in malpractice claims against physicians.” <br />
E-RX increases patient pickup from the pharmacy and patient compliance. This benefit is assumed by a few reports that patients arrive at the pharmacy to receive their drugs more so when delivered electronically, rather than when they carry a piece of paper. Patients with electronic prescribing allegedly pick up their drugs and take them more assiduously than those with paper prescriptions. Add-on programmed dispensing devices for patients have been found to work best to alert providers of non-compliance <ref name="salmon">Salmon JW, Jiang R. E-prescribing: history, issues, and potentials. Online J Public Health Inform. 2012;4(3). http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC3615836/</ref>.<br />
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== Security configuration ==<br />
<br />
The security system should be configured correctly.<br />
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* [[Password|Passwords]] should be secure yet easy to remember. [http://www.silicon.com/technology/security/2005/09/28/biometrics-curing-password-headaches-39152802/]<br />
* Co-signatures allows for multiple levels of function and security (eg, an RN can place an order but only with a signature from a physician)<br />
* [[Time-out settings]] prevent accidental unauthorized access.<br />
* [[Removing Paper|Clinical staff are sometimes reluctant to switch from paper to electronics]]. Active encouragement, additional training, and a deadline to fully integrate into CPOE increases compliance.<br />
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=== Dealing with Patient Transfers ===<br />
[[Dealing with Patient Transfers]]<br />
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===Pre-Admission Order Policies ===<br />
[[Pre-Admission Order Policies]]<br />
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=== Creating Order Sets ===<br />
[[Creating Order Sets]]<br />
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== Initial Selection of What to Alert on ==<br />
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During a CPOE) pilot, one organization discovered how much people communicate with those yellow sticky notes. For example, they found notes that said "Oxygen is up for renewal" or "you’ve got a narcotic that’s going to expire in twenty-four hours." Everybody just stuck sticky notes all over the chart.<br />
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One of the known disadvantages of CPOE is that not as many people are touching the patient's chart. Many physician's log in from home, and just place their morning orders. They are not looking at that paper chart with those sticky notes on it.<br />
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One way of deciding which alerts and rules to put in place is to replace the world of sticky notes. The organization developed alerts that said, "Your twenty-four hours are up with oxygen. Do you want the patient to continue?" or "narcotics are up for renewal." They started with basic alerts that helped with communication and work flow. Physicians expected to get an alert that says, "A narcotic’s getting ready to expire." They were used to it in the [[Removing Paper|paper world]], so they commented, "Okay, this is okay."<br />
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=== Standardized dictionaries ===<br />
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Standardized dictionaries from the [[Unified Medical Language System (UMLS)]] are essential. There are many controlled vocabularies to choose from.<br />
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=== Co-signing ===<br />
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Orders must be co-signed within a brief time period, usually less than 48 hours. Doctors often do not date and time their orders or their signatures, and it is common for physicians to sign orders weeks or even months after the fact. CPOE will allow the regulator to see the time to the second that the order was entered and signed.<br />
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Payne et al proposed creating a model of the life cycle of clinical documents to serve as a framework for discussion of document workflow. The model of the life cycle of a clinical document can be view: [http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC1513669/figure/fig1/]. The life cycle model has 3 axes: Stage, role and action.<ref name="Payne">Payne TH and G Graham. Managing the Life Cycle of Electronic Clinical Documents. J Am Med Inform Assoc. 2006 Jul-Aug; 13(4): 438–445. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC1513669//</ref>.<br />
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==== AMDIS Response to the Federal Tamper-Resistant Rx Law ====<br />
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[[AMDIS Response to the Federal Tamper-Resistant Rx Law]]<br />
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== Physical computing environment==<br />
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A great selection of [[Physical computing environment|computers]] help facilitate CPOE.<br />
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=== Success Factors ===<br />
[[Success Factors]]<br />
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After evaluating multiple CPOE systems using 40 + parameters, my conclusion is that the success factors can be easily classified to fall under three major categories as follows. <br />
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Productivity (intuitive, ease of use, speed, context sensitive help)<br />
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Care Quality (error reduction, reliability, Interaction accuracy, Overrides)<br />
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Security (programmed timeouts, role based authorization, authentication, access control, granularity of data acce<br />
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If the competing systems are still using green screens like RMRS or BICS (text based and keystroke or function key driven and not windows based and mouse driven), those fall out of favor against the more recent, Windows/web based systems like Practice Fusion or NextGen.<br />
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Clearly, the CPOE systems will need to integrate the Order Entry piece of their functionality with the Decision Support Systems (DSS) that create operational intelligence, so it can be brought out in real time during an encounter.<br />
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Studies reveal that very large Health Plan sponsors like Humana, Aetna, United Health, all have acquired companies that specialize in decision support. <br />
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To give specific examples, Humana has acquired Anvita and Aetna has acquired ActiveHealth. They claim that they have 1,200 health monitored events and 9,000 clinical rules that fire on the patient's cleansed, normalized and aggregated data to create operational intelligence that can be shared with the Physician during the encounter for optimizing care, cost of care and to influence both patient's as well as the physician's behavior.<br />
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===CPOE and Meaningful Use===<br />
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In order for eligible providers and hospitals to qualify for federal stimulus dollars, they must use certified [[EMR|electronic health technology]] in a [[meaningful use|meaningful]] way. [http://edocket.access.gpo.gov/2010/E9-31217.htm] Sometimes organizations struggle to achieve [[meaningful use]]. [http://www.ihealthbeat.org/special-reports/2010/small-midsize-physician-practices-could-face-barriers-in-meeting-meaningful-use-criteria.aspx] [[http://journal.ahima.org/2010/02/17/clinical-quality-measures-for-providers/] Computer physician order entry is one of the meaningful use measures that looks at all orders for a patient and how many were entered electronically by a licensed healthcare professional. <br />
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There are studies emerging that indicate that CPOE may actually increase [[Medication errors|medical errors]] especially if not implemented correctly [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1297629/] [http://www.ncbi.nlm.nih.gov/pubmed/15755942] [http://archinte.ama-assn.org/cgi/content/abstract/165/10/1111]. There is evidence that the current [[Certification Commission for Health Information Technology (CCHIT)|CCHIT-certified]] EHR technology is challenging to use for physicians and hospitals and takes years of training. The [[Certification Commission for Health Information Technology (CCHIT)|CCHIT]] certification model is mandates hundreds of required features and functions, often which are non user-friendly. [http://www.thehealthcareblog.com/the_health_care_blog/]<br />
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An interesting study was performed by a team of authors that set out to study the myth associated with eHealth initiatives implementation that this lead to substantial gains in quality and patient safety and concluded that evidence they found using qualitative methods is not that compelling but they leave great room for improvements.<ref>http://clinfowiki.org/wiki/index.php/The_Impact_of_eHealth_on_the_Quality_and_Safety_of_Health_Care:_A_Systematic_Overview#Approach</ref><br />
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However, it is clear that the federal government is doing everything in its power to get various [[House Approves SGR Deal With Major Health IT Provisions|health care systems certified and working together and willing to commit funds and oversight]] and where necessary, impose penalties to make it happen.<br />
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==Implementation Strategies==<br />
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Some organizations hire computer scribes who follow and enter orders for physicians. This allows [[Ranked Levels of Influence Model: Selecting Influence Techniques to Minimize IT Resistance |reluctant physicians]] to also comply with CPOE.<br />
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===Big Bang vs. Incremental Roll-out===<br />
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In the 1990s, one site used a gradual implementation with the old TDS system. First, very useful things to physicians were introduced, such as x-ray reports, labs results, and rounding lists. This allowed everyone to get accustomed to the user interface. Then, the CPOE introduced electornic ordering with the least dangerous medications. By the time the pharmacy was also using CPOE, everyone in the hospital was accustomed to the interface. In fact, most saw the benefit of doing things online instead of the paper system. The entire process took about a year and a half to get to full CPOE (93% of all orders by physicians). Paper orders were a fall back, however, with great pressure not to use them. There is also a psychological benefit to a paper fall-back system. [[Physician resistance as a barrier to implement clinical information systems|Physicians get angry]] when they are in a hurry and can't order because they can't navigate the system.<br />
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===Whether, when, and how to remove paper from the process?===<br />
[[Removing Paper|Whether, when, and how to remove paper from the process?]]<br />
===Can Utilizing a Computer Provider Order Entry (CPOE) System Prevent Hospital Medical Errors and Adverse Drug Events?===<br />
This is a review of an article by Charles et al 2014. Can utilizing a Computer Provider Order Entry (CPOE) System Prevent Hospital Medical Errors and Adverse Drug Events?<br />
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===Unintended Consequences of Implementing CPOE===<br />
[[Factors contributing to an increase in duplicate medication order errors after CPOE implementation|Factors contributing to an increase in duplicate medication order errors after CPOE implementation]]<br />
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== Reviews ==<br />
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* [[Computer physician order entry: benefits, costs, and issues.]]<br />
* [[Implementation of Physician Order Entry: User Satisfaction and Self-reported Usage Patterns.]]<br />
* [[Summary and Frequency of Barriers to Adoption of CPOE in the U.S.]]<br />
* [[Principles for a Successful Computerized Physician Order Entry Implementation.]]<br />
* [[Does CPOE support nurse-physician communication in the medication order process]]<br />
*[[Reduction in medication erros in hospitals due to adoption of computerized provider order entry systems]]<br />
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== References ==<br />
<references/></div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Category:WorkflowCategory:Workflow2015-04-01T19:08:35Z<p>Vishaghera: </p>
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<div>This is a core category for Clinfowiki. It belongs to Clinfowiki > Applications.<br />
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[[Category:Applications]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Category:WorkflowCategory:Workflow2015-04-01T19:08:06Z<p>Vishaghera: </p>
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<div>This is a core category for Clinfowiki. It belongs to Clinfowiki > Applications.<br />
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[[Category:Applications]]<br />
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[[Factors contributing to an increase in duplicate medication order errors after CPOE implementation|Factors contributing to an increase in duplicate medication order errors after CPOE implementation]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Real-time_pharmacy_surveillance_and_clinical_decision_support_to_reduce_adverse_drug_events_in_acute_kidney_injury_%E2%80%93_a_randomized,_controlled_trial.Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury – a randomized, controlled trial.2015-03-26T01:42:12Z<p>Vishaghera: /* Conclusion */</p>
<hr />
<div>This is a review of McCoy, A.B., Cox, Z.L., Neal, E.B., Waitman, L.R., Peterson, N.B., Bhave, G., Siew, E.D., Danciu, I., Lewis, J.B. and Peterson, J.F. (2012). “Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury – a randomized, controlled trial.” <ref> McCoy, A.B., Cox, Z.L., Neal, E.B., Waitman, L.R., Peterson, N.B., Bhave, G., Siew, E.D., Danciu, I., Lewis, J.B. and Peterson, J.F. (2012). Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury – a randomized, controlled trial. Applied Clinical Informatics; 3: 221–238. </ref><br />
<br />
= Background =<br />
Medications comprise the largest source of medical errors in the healthcare industry today. Therefore building [[CDS]] systems to monitor the ordering and dispensing of prescription drugs could prevent significant morbidity, mortality and inefficiencies in hospitals around the world. Polypharmacy is not uncommon in today’s healthcare arena as people live longer with chronic disease often entailing much comorbidity. Numerous drugs are used to manage one individual’s health and drug-drug interactions can be all too common.<br />
<br />
= Methods =<br />
McCoy et al., (2012) examined whether adding pharmacy surveillance to a clinical practice already utilizing CDS [[alert]] software could reduce adverse drug events and/ or avoid occurrences of drug induced kidney dysfunction to patients with underlying renal disease. The authors carried out a randomized clinical trial with 396 patients who were admitted during a three month period. The intervention group received an extra look in by a pharmacist for medication events associated with declining renal function.<br />
<br />
= Results =<br />
McCoy and colleagues found no added benefit or reduction in kidney functioning between the control versus intervention groups. This study was fishing for a potential intervention which would potentially improve patient safety by reducing ADEs. When comparing provider response to abnormal serum creatinine due to active medications McCoy et al., (2012) found no “statistically significant differences between the control and intervention groups” (p.225). <br />
<br />
= Conclusion =<br />
McCoy et al., (2012) found that “while CDS is effective at preventing (potential [[Adverse drug event]]s) pADEs and ADEs in patients with AKI, further research is necessary to determine whether surveillance can improve CDS performance” (p.227). <br />
This article was an enjoyable read; it is interesting how CDS is well accepted by many to improve clinical outcomes but yet proving it with statistics for many has remained quite elusive.<br />
<br />
Related Read: [[Medication dispensing errors and potential adverse drug events before and after implementing bar code technology in the pharmacy|Medication dispensing errors and potential adverse drug events before and after implementing bar code technology in the pharmacy]]<br />
<br />
= References =<br />
<references/><br />
[[Category: Reviews]]<br />
[[Category:CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Evaluating_Clinical_Decision_Support_Systems:Monitoring_CPOE_Order_Check_Override_Rates_in_the_Department_of_Veterans_Affairs%E2%80%99_Computerized_Patient_Record_SystemEvaluating Clinical Decision Support Systems:Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System2015-03-26T01:33:16Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>A review of research article (2008) titled "Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System"<ref name="Lin">Lin, C.P., Payne, T. H., Nichol, W. P., Hoey, P. J., Anderson, C. L., Gennari, J. H. Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System.2008.Journal of the American Medical Informatics Association Volume 15 (5), 620-626. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528033/</ref> by Lin et al.<br />
<br />
==Objectives==<br />
<br />
To reevaluate and compare the VA Veterans Affairs Puget Sound-[http://www.pugetsound.va.gov/ VA Puget Sound] Health Care System’s computerized practitioner order entry [[CPOE]] system generated critical order checks over ride rates in 2001 to that of 2006. A secondary objective was to assess the impact of system changes related to topical medication order checks.<br />
<br />
==Introduction==<br />
<br />
VA Puget Sound Health System health care providers had been using CPOE which has an inbuilt order checking to mitigate the potential medication errors in orders in view of patient safety since 1997. Most of the times the order checks alerts with “high severity” were overridden by healthcare providers in view of clinical irrelevance. A study was conducted in 2001 to analyze the various factors which affect the order checking overrides. A follow up study had been conducted to reassess if the changes in the [[Computerized Patient Record System]] (CPRS) order check rules have an influence on the overridden rates of order checks.<br />
<br />
==Design and Setting==<br />
<br />
The Computerized Patient Record System (CPRS) part of the larger [[Veterans Health Information Systems and Technology Architecture (VistA)]] had been used by VA Puget Sound for note entry, results review and order entry. Critical overridden order checks were analyzed for patients from VA Puget Sound Health Care System Hospitals following VA centrally developed and controlled National Drug File (NDF) and few locally developed Drug files. <br />
VA CPOE also classifies order checks as “critical” or “significant” were high mainly for drug-allergy and drug-drug order checks and also few other types. To be classified as critical, the interaction must be identified in the manufacturer’s black box warning, or be well documented in the literature to cause significant sequelae. Significant drug interactions do not meet the critical [[drug-drug interaction]] criteria but are still thought to have substantial clinical importance.<br />
<br />
==Methods==<br />
<br />
Retrospective analysis by post-hoc logging into system for order activity for two time 3 day periods were Wednesday, January 4, 2006 14:11 to Friday, January 6, 2006 15:46 (Period1) and from Monday, January 9, 2006 08:41 to Wednesday, January 11, 2006 10:30 (Period 2).<br />
*Inclusion criteria: Direct practitioner entry in the ordering package.<br />
*Exclusion criteria: Orders entered through the Pharmacy, Lab or Radiology packages.<br />
Lin et al., defined override rate as the percentage of distinct orders receiving a high severity, critical order check that are signed. <br />
<br />
<br />
<br />
==Results==<br />
<br />
Chi-square contingency table test was applied to compare results from the 2001 and 2006 studies. <br />
Eight different types of critical order checks identified were Drug-Drug Interaction, [[Drug-allergy interaction]], Clozapine appropriateness, Procedure uses intravenous contrast media - abnormal biochemistry result/no creatinine results within 30 days, Metformin - no serum creatinine, Patient has no allergy assessment, Patient allergic to contrast media, Procedure uses intravenous contrast media and patient is taking metformin.<br />
The percentages of overridden high severity order checks had increased from 0.5% in 2001 to 2.5% in 2006. Drug-Drug order checks override rate percentage declined by a percentage in 2006 (87%) than in 2001(88%) with rate being still over 85%. But the percentage of Drug-Allergy Order Checks escalated a difference of 12% from 2001(69%) to 2006(81%).Pearson’s chi-square contingency table test calculated that overall there had been a statistically significant change in the rate of critical order checks from 2001 to 2006. A slight decrease in critical drug-drug overrides on Topical form medications was observed from 29% in 2001 to 25.9% in 2006.<br />
<br />
==Discussion==<br />
<br />
Lin et al., study highlighted that the override rates were due to diverse factors. It strongly agrees with Kuperman et al. who recommended that drug knowledge base designers need to provide the necessary tools to understand, customize and share rule information and that organizations need to create policy and procedure infrastructure to support the use of these tools.<br />
System behavior should also be easily monitored, and ease of evaluation and the development of built-in evaluation tools should be accessible in system design.<br />
Abookire et al. study highlighted that periodic evaluation of system operators to identify the unexpected effects on order checking and particularly after the introduction of new policies, or updates, or changes in the system. Further studies will be interesting in this aspect. <br />
<br />
==Limitations==<br />
<br />
The retrospective analysis applied in this study, sampling the data orders at different times during the year, identifying the factor or combination of factors, both technical and social, may have contributed to new system behaviors including significantly higher drug allergy order check and override rates. So, Lin et al., could not control for many possible changes in the environment and so were not certain about the cause of the overridden rates. <br />
<br />
==Conclusion==<br />
<br />
Lin et al were successful in finding the quantitative data but still need to assume few factors which could have contributed to such high percentages of overridden rates of critically high rates particularly life threatening order checks for drug-allergy orders which might have been due to policy changes and changes in rule bases and drug files.<br />
Simultaneous Analysis of order check systems both qualitatively observational with quantitative order checks monitoring to better understand clinical decision making and the interactions physicians have with information and decision support systems. These outcomes must be clinically relevant for correlation.<br />
<br />
<br />
==Comments==<br />
More studies on overridden rates qualitatively and quantitatively on patient outcomes and educating the physicians about the documented clinically relevant data of the importance of order checks, might decrease the overridden rates in future.<br />
Related study at the VA Puget Sound Healthcare System [[A qualitative cross-site study of physician order entry|A qualitative cross-site study of physician order entry]]<br />
<br />
=Second Review=<br />
==Background==<br />
The purpose of this study was to identify and measure the number of override rates in 2006 for computerized practitioner order entry (CPOE) for the Veteran Affairs Puget Sound Health Care System. Alerts are set in place to reduce errors and provide information over drug-allergies and drug-drug interactions <ref name="van"> van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. J Am Med Inform Assoc. 2006;13(2):138–47. http://www.ncbi.nlm.nih.gov/pubmed/16357358 </ref>. A previous study conducted in 2001 would compare results. <br />
<br />
==Methods==<br />
A post-hoc logging program helped identify and analyze ordering data to then measure the number of orders, order check types, and order check overrides by order check type. Pearson’s chi-square tests were used to compare results from 2006 to previous study in 2001.<br />
<br />
==Results==<br />
The study reviewed 37,040 orders that generated 908 (2.5%) critical order checks and identified an 74/85 (87%) override rate for drug-drug critical alerts compared to 95/108 (88%) in the 2001 study (X2=0.04, df=1,p=0.85). The study also identified a 341/420 (81%) override rate compared to 72/105 (69%) in 2001 for drug-allergies (X2=7.97, df=1,p=0.005). Of these override rates, there were 420/37040 (1.13%) orders generated compared to 105/42621 (0.25%) during a drug-allergy order check in 2001.<br />
<br />
==Conclusion==<br />
The override rates of these orders generated including drug-drug and drug-allergy order checks were high. From the 2001 study to the current 2006 study, there was a significant increase in the frequency of drug-allergy order checks. For purposes of clinical computing systems frequent monitoring of override rates and study further physician action during ordering and decision support.<br />
<br />
==Comments==<br />
I have been interested in the varying VA’s EHRs as it has provided insight for a wide range of uses. It was an interesting read to look at override rates of the alerts put in place for efficiency and patient safety. There were policy and drug file changes between 2006 and 2001 overrides that would have led to believe the need for less overrides in 2006. It indicates the need for continued monitoring. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Evaluating_Clinical_Decision_Support_Systems:Monitoring_CPOE_Order_Check_Override_Rates_in_the_Department_of_Veterans_Affairs%E2%80%99_Computerized_Patient_Record_SystemEvaluating Clinical Decision Support Systems:Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System2015-03-26T01:32:02Z<p>Vishaghera: /* Comments */</p>
<hr />
<div>A review of research article (2008) titled "Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System"<ref name="Lin">Lin, C.P., Payne, T. H., Nichol, W. P., Hoey, P. J., Anderson, C. L., Gennari, J. H. Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System.2008.Journal of the American Medical Informatics Association Volume 15 (5), 620-626. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528033/</ref> by Lin et al.<br />
<br />
==Objectives==<br />
<br />
To reevaluate and compare the VA Veterans Affairs Puget Sound-[http://www.pugetsound.va.gov/ VA Puget Sound] Health Care System’s computerized practitioner order entry [[CPOE]] system generated critical order checks over ride rates in 2001 to that of 2006. A secondary objective was to assess the impact of system changes related to topical medication order checks.<br />
<br />
==Introduction==<br />
<br />
VA Puget Sound Health System health care providers had been using CPOE which has an inbuilt order checking to mitigate the potential medication errors in orders in view of patient safety since 1997. Most of the times the order checks alerts with “high severity” were overridden by healthcare providers in view of clinical irrelevance. A study was conducted in 2001 to analyze the various factors which affect the order checking overrides. A follow up study had been conducted to reassess if the changes in the [[Computerized Patient Record System]] (CPRS) order check rules have an influence on the overridden rates of order checks.<br />
<br />
==Design and Setting==<br />
<br />
The Computerized Patient Record System (CPRS) part of the larger [[Veterans Health Information Systems and Technology Architecture (VistA)]] had been used by VA Puget Sound for note entry, results review and order entry. Critical overridden order checks were analyzed for patients from VA Puget Sound Health Care System Hospitals following VA centrally developed and controlled National Drug File (NDF) and few locally developed Drug files. <br />
VA CPOE also classifies order checks as “critical” or “significant” were high mainly for drug-allergy and drug-drug order checks and also few other types. To be classified as critical, the interaction must be identified in the manufacturer’s black box warning, or be well documented in the literature to cause significant sequelae. Significant drug interactions do not meet the critical [[drug-drug interaction]] criteria but are still thought to have substantial clinical importance.<br />
<br />
==Methods==<br />
<br />
Retrospective analysis by post-hoc logging into system for order activity for two time 3 day periods were Wednesday, January 4, 2006 14:11 to Friday, January 6, 2006 15:46 (Period1) and from Monday, January 9, 2006 08:41 to Wednesday, January 11, 2006 10:30 (Period 2).<br />
*Inclusion criteria: Direct practitioner entry in the ordering package.<br />
*Exclusion criteria: Orders entered through the Pharmacy, Lab or Radiology packages.<br />
Lin et al., defined override rate as the percentage of distinct orders receiving a high severity, critical order check that are signed. <br />
<br />
<br />
<br />
==Results==<br />
<br />
Chi-square contingency table test was applied to compare results from the 2001 and 2006 studies. <br />
Eight different types of critical order checks identified were Drug-Drug Interaction, [[Drug-allergy interaction]], Clozapine appropriateness, Procedure uses intravenous contrast media - abnormal biochemistry result/no creatinine results within 30 days, Metformin - no serum creatinine, Patient has no allergy assessment, Patient allergic to contrast media, Procedure uses intravenous contrast media and patient is taking metformin.<br />
The percentages of overridden high severity order checks had increased from 0.5% in 2001 to 2.5% in 2006. Drug-Drug order checks override rate percentage declined by a percentage in 2006 (87%) than in 2001(88%) with rate being still over 85%. But the percentage of Drug-Allergy Order Checks escalated a difference of 12% from 2001(69%) to 2006(81%).Pearson’s chi-square contingency table test calculated that overall there had been a statistically significant change in the rate of critical order checks from 2001 to 2006. A slight decrease in critical drug-drug overrides on Topical form medications was observed from 29% in 2001 to 25.9% in 2006.<br />
<br />
==Discussion==<br />
<br />
Lin et al., study highlighted that the override rates were due to diverse factors. It strongly agrees with Kuperman et al. who recommended that drug knowledge base designers need to provide the necessary tools to understand, customize and share rule information and that organizations need to create policy and procedure infrastructure to support the use of these tools.<br />
System behavior should also be easily monitored, and ease of evaluation and the development of built-in evaluation tools should be accessible in system design.<br />
Abookire et al. study highlighted that periodic evaluation of system operators to identify the unexpected effects on order checking and particularly after the introduction of new policies, or updates, or changes in the system. Further studies will be interesting in this aspect. <br />
<br />
==Limitations==<br />
<br />
The retrospective analysis applied in this study, sampling the data orders at different times during the year, identifying the factor or combination of factors, both technical and social, may have contributed to new system behaviors including significantly higher drug allergy order check and override rates. So, Lin et al., could not control for many possible changes in the environment and so were not certain about the cause of the overridden rates. <br />
<br />
==Conclusion==<br />
<br />
Lin et al were successful in finding the quantitative data but still need to assume few factors which could have contributed to such high percentages of overridden rates of critically high rates particularly life threatening order checks for drug-allergy orders which might have been due to policy changes and changes in rule bases and drug files.<br />
Simultaneous Analysis of order check systems both qualitatively observational with quantitative order checks monitoring to better understand clinical decision making and the interactions physicians have with information and decision support systems. These outcomes must be clinically relevant for correlation.<br />
<br />
<br />
==Comments==<br />
More studies on overridden rates qualitatively and quantitatively on patient outcomes and educating the physicians about the documented clinically relevant data of the importance of order checks, might decrease the overridden rates in future.<br />
Related study at the VA [[A qualitative cross-site study of physician order entry|A qualitative cross-site study of physician order entry]]<br />
<br />
=Second Review=<br />
==Background==<br />
The purpose of this study was to identify and measure the number of override rates in 2006 for computerized practitioner order entry (CPOE) for the Veteran Affairs Puget Sound Health Care System. Alerts are set in place to reduce errors and provide information over drug-allergies and drug-drug interactions <ref name="van"> van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. J Am Med Inform Assoc. 2006;13(2):138–47. http://www.ncbi.nlm.nih.gov/pubmed/16357358 </ref>. A previous study conducted in 2001 would compare results. <br />
<br />
==Methods==<br />
A post-hoc logging program helped identify and analyze ordering data to then measure the number of orders, order check types, and order check overrides by order check type. Pearson’s chi-square tests were used to compare results from 2006 to previous study in 2001.<br />
<br />
==Results==<br />
The study reviewed 37,040 orders that generated 908 (2.5%) critical order checks and identified an 74/85 (87%) override rate for drug-drug critical alerts compared to 95/108 (88%) in the 2001 study (X2=0.04, df=1,p=0.85). The study also identified a 341/420 (81%) override rate compared to 72/105 (69%) in 2001 for drug-allergies (X2=7.97, df=1,p=0.005). Of these override rates, there were 420/37040 (1.13%) orders generated compared to 105/42621 (0.25%) during a drug-allergy order check in 2001.<br />
<br />
==Conclusion==<br />
The override rates of these orders generated including drug-drug and drug-allergy order checks were high. From the 2001 study to the current 2006 study, there was a significant increase in the frequency of drug-allergy order checks. For purposes of clinical computing systems frequent monitoring of override rates and study further physician action during ordering and decision support.<br />
<br />
==Comments==<br />
I have been interested in the varying VA’s EHRs as it has provided insight for a wide range of uses. It was an interesting read to look at override rates of the alerts put in place for efficiency and patient safety. There were policy and drug file changes between 2006 and 2001 overrides that would have led to believe the need for less overrides in 2006. It indicates the need for continued monitoring. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Evaluating_Clinical_Decision_Support_Systems:Monitoring_CPOE_Order_Check_Override_Rates_in_the_Department_of_Veterans_Affairs%E2%80%99_Computerized_Patient_Record_SystemEvaluating Clinical Decision Support Systems:Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System2015-03-26T01:31:13Z<p>Vishaghera: /* Objectives */</p>
<hr />
<div>A review of research article (2008) titled "Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System"<ref name="Lin">Lin, C.P., Payne, T. H., Nichol, W. P., Hoey, P. J., Anderson, C. L., Gennari, J. H. Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System.2008.Journal of the American Medical Informatics Association Volume 15 (5), 620-626. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528033/</ref> by Lin et al.<br />
<br />
==Objectives==<br />
<br />
To reevaluate and compare the VA Veterans Affairs Puget Sound-[http://www.pugetsound.va.gov/ VA Puget Sound] Health Care System’s computerized practitioner order entry [[CPOE]] system generated critical order checks over ride rates in 2001 to that of 2006. A secondary objective was to assess the impact of system changes related to topical medication order checks.<br />
<br />
==Introduction==<br />
<br />
VA Puget Sound Health System health care providers had been using CPOE which has an inbuilt order checking to mitigate the potential medication errors in orders in view of patient safety since 1997. Most of the times the order checks alerts with “high severity” were overridden by healthcare providers in view of clinical irrelevance. A study was conducted in 2001 to analyze the various factors which affect the order checking overrides. A follow up study had been conducted to reassess if the changes in the [[Computerized Patient Record System]] (CPRS) order check rules have an influence on the overridden rates of order checks.<br />
<br />
==Design and Setting==<br />
<br />
The Computerized Patient Record System (CPRS) part of the larger [[Veterans Health Information Systems and Technology Architecture (VistA)]] had been used by VA Puget Sound for note entry, results review and order entry. Critical overridden order checks were analyzed for patients from VA Puget Sound Health Care System Hospitals following VA centrally developed and controlled National Drug File (NDF) and few locally developed Drug files. <br />
VA CPOE also classifies order checks as “critical” or “significant” were high mainly for drug-allergy and drug-drug order checks and also few other types. To be classified as critical, the interaction must be identified in the manufacturer’s black box warning, or be well documented in the literature to cause significant sequelae. Significant drug interactions do not meet the critical [[drug-drug interaction]] criteria but are still thought to have substantial clinical importance.<br />
<br />
==Methods==<br />
<br />
Retrospective analysis by post-hoc logging into system for order activity for two time 3 day periods were Wednesday, January 4, 2006 14:11 to Friday, January 6, 2006 15:46 (Period1) and from Monday, January 9, 2006 08:41 to Wednesday, January 11, 2006 10:30 (Period 2).<br />
*Inclusion criteria: Direct practitioner entry in the ordering package.<br />
*Exclusion criteria: Orders entered through the Pharmacy, Lab or Radiology packages.<br />
Lin et al., defined override rate as the percentage of distinct orders receiving a high severity, critical order check that are signed. <br />
<br />
<br />
<br />
==Results==<br />
<br />
Chi-square contingency table test was applied to compare results from the 2001 and 2006 studies. <br />
Eight different types of critical order checks identified were Drug-Drug Interaction, [[Drug-allergy interaction]], Clozapine appropriateness, Procedure uses intravenous contrast media - abnormal biochemistry result/no creatinine results within 30 days, Metformin - no serum creatinine, Patient has no allergy assessment, Patient allergic to contrast media, Procedure uses intravenous contrast media and patient is taking metformin.<br />
The percentages of overridden high severity order checks had increased from 0.5% in 2001 to 2.5% in 2006. Drug-Drug order checks override rate percentage declined by a percentage in 2006 (87%) than in 2001(88%) with rate being still over 85%. But the percentage of Drug-Allergy Order Checks escalated a difference of 12% from 2001(69%) to 2006(81%).Pearson’s chi-square contingency table test calculated that overall there had been a statistically significant change in the rate of critical order checks from 2001 to 2006. A slight decrease in critical drug-drug overrides on Topical form medications was observed from 29% in 2001 to 25.9% in 2006.<br />
<br />
==Discussion==<br />
<br />
Lin et al., study highlighted that the override rates were due to diverse factors. It strongly agrees with Kuperman et al. who recommended that drug knowledge base designers need to provide the necessary tools to understand, customize and share rule information and that organizations need to create policy and procedure infrastructure to support the use of these tools.<br />
System behavior should also be easily monitored, and ease of evaluation and the development of built-in evaluation tools should be accessible in system design.<br />
Abookire et al. study highlighted that periodic evaluation of system operators to identify the unexpected effects on order checking and particularly after the introduction of new policies, or updates, or changes in the system. Further studies will be interesting in this aspect. <br />
<br />
==Limitations==<br />
<br />
The retrospective analysis applied in this study, sampling the data orders at different times during the year, identifying the factor or combination of factors, both technical and social, may have contributed to new system behaviors including significantly higher drug allergy order check and override rates. So, Lin et al., could not control for many possible changes in the environment and so were not certain about the cause of the overridden rates. <br />
<br />
==Conclusion==<br />
<br />
Lin et al were successful in finding the quantitative data but still need to assume few factors which could have contributed to such high percentages of overridden rates of critically high rates particularly life threatening order checks for drug-allergy orders which might have been due to policy changes and changes in rule bases and drug files.<br />
Simultaneous Analysis of order check systems both qualitatively observational with quantitative order checks monitoring to better understand clinical decision making and the interactions physicians have with information and decision support systems. These outcomes must be clinically relevant for correlation.<br />
<br />
<br />
==Comments==<br />
More studies on overridden rates qualitatively and quantitatively on patient outcomes and educating the physicians about the documented clinically relevant data of the importance of order checks, might decrease the overridden rates in future. <br />
<br />
<br />
=Second Review=<br />
==Background==<br />
The purpose of this study was to identify and measure the number of override rates in 2006 for computerized practitioner order entry (CPOE) for the Veteran Affairs Puget Sound Health Care System. Alerts are set in place to reduce errors and provide information over drug-allergies and drug-drug interactions <ref name="van"> van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. J Am Med Inform Assoc. 2006;13(2):138–47. http://www.ncbi.nlm.nih.gov/pubmed/16357358 </ref>. A previous study conducted in 2001 would compare results. <br />
<br />
==Methods==<br />
A post-hoc logging program helped identify and analyze ordering data to then measure the number of orders, order check types, and order check overrides by order check type. Pearson’s chi-square tests were used to compare results from 2006 to previous study in 2001.<br />
<br />
==Results==<br />
The study reviewed 37,040 orders that generated 908 (2.5%) critical order checks and identified an 74/85 (87%) override rate for drug-drug critical alerts compared to 95/108 (88%) in the 2001 study (X2=0.04, df=1,p=0.85). The study also identified a 341/420 (81%) override rate compared to 72/105 (69%) in 2001 for drug-allergies (X2=7.97, df=1,p=0.005). Of these override rates, there were 420/37040 (1.13%) orders generated compared to 105/42621 (0.25%) during a drug-allergy order check in 2001.<br />
<br />
==Conclusion==<br />
The override rates of these orders generated including drug-drug and drug-allergy order checks were high. From the 2001 study to the current 2006 study, there was a significant increase in the frequency of drug-allergy order checks. For purposes of clinical computing systems frequent monitoring of override rates and study further physician action during ordering and decision support.<br />
<br />
==Comments==<br />
I have been interested in the varying VA’s EHRs as it has provided insight for a wide range of uses. It was an interesting read to look at override rates of the alerts put in place for efficiency and patient safety. There were policy and drug file changes between 2006 and 2001 overrides that would have led to believe the need for less overrides in 2006. It indicates the need for continued monitoring. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Evaluating_Clinical_Decision_Support_Systems:Monitoring_CPOE_Order_Check_Override_Rates_in_the_Department_of_Veterans_Affairs%E2%80%99_Computerized_Patient_Record_SystemEvaluating Clinical Decision Support Systems:Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System2015-03-26T01:29:30Z<p>Vishaghera: /* Objectives */</p>
<hr />
<div>A review of research article (2008) titled "Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System"<ref name="Lin">Lin, C.P., Payne, T. H., Nichol, W. P., Hoey, P. J., Anderson, C. L., Gennari, J. H. Evaluating Clinical Decision Support Systems: Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System.2008.Journal of the American Medical Informatics Association Volume 15 (5), 620-626. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528033/</ref> by Lin et al.<br />
<br />
==Objectives==<br />
<br />
To reevaluate and compare the VA [[Veterans Affairs|Veteran Affairs]] Puget Sound-[http://www.pugetsound.va.gov/ VA Puget Sound] Health Care System’s computerized practitioner order entry [[CPOE]] system generated critical order checks over ride rates in 2001 to that of 2006. A secondary objective was to assess the impact of system changes related to topical medication order checks.<br />
<br />
==Introduction==<br />
<br />
VA Puget Sound Health System health care providers had been using CPOE which has an inbuilt order checking to mitigate the potential medication errors in orders in view of patient safety since 1997. Most of the times the order checks alerts with “high severity” were overridden by healthcare providers in view of clinical irrelevance. A study was conducted in 2001 to analyze the various factors which affect the order checking overrides. A follow up study had been conducted to reassess if the changes in the [[Computerized Patient Record System]] (CPRS) order check rules have an influence on the overridden rates of order checks.<br />
<br />
==Design and Setting==<br />
<br />
The Computerized Patient Record System (CPRS) part of the larger [[Veterans Health Information Systems and Technology Architecture (VistA)]] had been used by VA Puget Sound for note entry, results review and order entry. Critical overridden order checks were analyzed for patients from VA Puget Sound Health Care System Hospitals following VA centrally developed and controlled National Drug File (NDF) and few locally developed Drug files. <br />
VA CPOE also classifies order checks as “critical” or “significant” were high mainly for drug-allergy and drug-drug order checks and also few other types. To be classified as critical, the interaction must be identified in the manufacturer’s black box warning, or be well documented in the literature to cause significant sequelae. Significant drug interactions do not meet the critical [[drug-drug interaction]] criteria but are still thought to have substantial clinical importance.<br />
<br />
==Methods==<br />
<br />
Retrospective analysis by post-hoc logging into system for order activity for two time 3 day periods were Wednesday, January 4, 2006 14:11 to Friday, January 6, 2006 15:46 (Period1) and from Monday, January 9, 2006 08:41 to Wednesday, January 11, 2006 10:30 (Period 2).<br />
*Inclusion criteria: Direct practitioner entry in the ordering package.<br />
*Exclusion criteria: Orders entered through the Pharmacy, Lab or Radiology packages.<br />
Lin et al., defined override rate as the percentage of distinct orders receiving a high severity, critical order check that are signed. <br />
<br />
<br />
<br />
==Results==<br />
<br />
Chi-square contingency table test was applied to compare results from the 2001 and 2006 studies. <br />
Eight different types of critical order checks identified were Drug-Drug Interaction, [[Drug-allergy interaction]], Clozapine appropriateness, Procedure uses intravenous contrast media - abnormal biochemistry result/no creatinine results within 30 days, Metformin - no serum creatinine, Patient has no allergy assessment, Patient allergic to contrast media, Procedure uses intravenous contrast media and patient is taking metformin.<br />
The percentages of overridden high severity order checks had increased from 0.5% in 2001 to 2.5% in 2006. Drug-Drug order checks override rate percentage declined by a percentage in 2006 (87%) than in 2001(88%) with rate being still over 85%. But the percentage of Drug-Allergy Order Checks escalated a difference of 12% from 2001(69%) to 2006(81%).Pearson’s chi-square contingency table test calculated that overall there had been a statistically significant change in the rate of critical order checks from 2001 to 2006. A slight decrease in critical drug-drug overrides on Topical form medications was observed from 29% in 2001 to 25.9% in 2006.<br />
<br />
==Discussion==<br />
<br />
Lin et al., study highlighted that the override rates were due to diverse factors. It strongly agrees with Kuperman et al. who recommended that drug knowledge base designers need to provide the necessary tools to understand, customize and share rule information and that organizations need to create policy and procedure infrastructure to support the use of these tools.<br />
System behavior should also be easily monitored, and ease of evaluation and the development of built-in evaluation tools should be accessible in system design.<br />
Abookire et al. study highlighted that periodic evaluation of system operators to identify the unexpected effects on order checking and particularly after the introduction of new policies, or updates, or changes in the system. Further studies will be interesting in this aspect. <br />
<br />
==Limitations==<br />
<br />
The retrospective analysis applied in this study, sampling the data orders at different times during the year, identifying the factor or combination of factors, both technical and social, may have contributed to new system behaviors including significantly higher drug allergy order check and override rates. So, Lin et al., could not control for many possible changes in the environment and so were not certain about the cause of the overridden rates. <br />
<br />
==Conclusion==<br />
<br />
Lin et al were successful in finding the quantitative data but still need to assume few factors which could have contributed to such high percentages of overridden rates of critically high rates particularly life threatening order checks for drug-allergy orders which might have been due to policy changes and changes in rule bases and drug files.<br />
Simultaneous Analysis of order check systems both qualitatively observational with quantitative order checks monitoring to better understand clinical decision making and the interactions physicians have with information and decision support systems. These outcomes must be clinically relevant for correlation.<br />
<br />
<br />
==Comments==<br />
More studies on overridden rates qualitatively and quantitatively on patient outcomes and educating the physicians about the documented clinically relevant data of the importance of order checks, might decrease the overridden rates in future. <br />
<br />
<br />
=Second Review=<br />
==Background==<br />
The purpose of this study was to identify and measure the number of override rates in 2006 for computerized practitioner order entry (CPOE) for the Veteran Affairs Puget Sound Health Care System. Alerts are set in place to reduce errors and provide information over drug-allergies and drug-drug interactions <ref name="van"> van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. J Am Med Inform Assoc. 2006;13(2):138–47. http://www.ncbi.nlm.nih.gov/pubmed/16357358 </ref>. A previous study conducted in 2001 would compare results. <br />
<br />
==Methods==<br />
A post-hoc logging program helped identify and analyze ordering data to then measure the number of orders, order check types, and order check overrides by order check type. Pearson’s chi-square tests were used to compare results from 2006 to previous study in 2001.<br />
<br />
==Results==<br />
The study reviewed 37,040 orders that generated 908 (2.5%) critical order checks and identified an 74/85 (87%) override rate for drug-drug critical alerts compared to 95/108 (88%) in the 2001 study (X2=0.04, df=1,p=0.85). The study also identified a 341/420 (81%) override rate compared to 72/105 (69%) in 2001 for drug-allergies (X2=7.97, df=1,p=0.005). Of these override rates, there were 420/37040 (1.13%) orders generated compared to 105/42621 (0.25%) during a drug-allergy order check in 2001.<br />
<br />
==Conclusion==<br />
The override rates of these orders generated including drug-drug and drug-allergy order checks were high. From the 2001 study to the current 2006 study, there was a significant increase in the frequency of drug-allergy order checks. For purposes of clinical computing systems frequent monitoring of override rates and study further physician action during ordering and decision support.<br />
<br />
==Comments==<br />
I have been interested in the varying VA’s EHRs as it has provided insight for a wide range of uses. It was an interesting read to look at override rates of the alerts put in place for efficiency and patient safety. There were policy and drug file changes between 2006 and 2001 overrides that would have led to believe the need for less overrides in 2006. It indicates the need for continued monitoring. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Relationship_between_medication_event_rates_and_the_Leapfrog_computerized_physician_order_entry_evaluation_toolRelationship between medication event rates and the Leapfrog computerized physician order entry evaluation tool2015-03-26T01:25:30Z<p>Vishaghera: /* Results and Discussion */</p>
<hr />
<div>This is a review of Leung, A. A., Keohane, C., Lipsitz, S., Zimlichman, E., Amato, M., Simon, S. R., Coffey, M.,Kaufman, N., Cadet, B., Schiff, G., Seger, D.L., & Bates, D.W. 2013 article, “Relationship between medication event rates and the Leapfrog computerized physician order entry evaluation tool”.<ref name="Leung, 2013"> Leung, A. A., Keohane, C., Lipsitz, S., Zimlichman, E., Amato, M., Simon, S. R., Coffey, M.,Kaufman, N., Cadet, B., Schiff, G., Seger, D.L., & Bates, D.W. (2013). ''Relationship between medication event rates and the leapfrog computerized physician order entry evaluation tool''. J AM Med Inform Assoc., 20(e1), e85-e90. doi: 10.1136/amiajnl-2012-001549 Retrieved from: http://www.ncbi.nlm.nih.gov/pubmed/23599225 </ref><br />
<br />
== Abstract ==<br />
The objective of this article was to evaluate the Leapfrog [[CPOE]] evaluation tool for its effectiveness and assess the relationship between Leapfrog scores and the rates of preventable [[adverse drug event]]s ([[ADE]]) and potential ADE. The Leapfrog Group has developed this tool to assess the performance of a hospital’s CPOE system by using simulation cases. “The Leapfrog CPOE evaluation tool estimates the potential benefit of a CPOE system by testing how it handles a variety of dangerous medication ordering scenarios”.<ref name="Leung, 2013"> Leung, A. A., Keohane, C., Lipsitz, S., Zimlichman, E., Amato, M., Simon, S. R., Coffey, M.,Kaufman, N., Cadet, B., Schiff, G., Seger, D.L., & Bates, D.W. (2013). ''Relationship between medication event rates and the leapfrog computerized physician order entry evaluation tool''. J AM Med Inform Assoc., 20(e1), e85-e90. doi: 10.1136/amiajnl-2012-001549 Retrieved from: http://www.ncbi.nlm.nih.gov/pubmed/23599225 </ref><br />
<br />
==Methods ==<br />
To determine the relationship between leapfrog scores and the rates of preventable and potential ADEs a cross-sectional study was performed. There were 1000 adult admissions evaluated in five community hospitals in Massachusetts. The authors used random sampling as there were 200 patients that were selected from each site. Observed rates of preventable and potential ADE were compared with scores reported by the Leapfrog CPOE evaluation tool. “The Leapfrog CPOE evaluation tool examines how a system with clinical decision support is able to intercept a variety of potentially dangerous medication order using a variety of simulated clinical scenarios” <ref name="Leung, 2013"> Leung, A. A., Keohane, C., Lipsitz, S., Zimlichman, E., Amato, M., Simon, S. R., Coffey, M.,Kaufman, N., Cadet, B., Schiff, G., Seger, D.L., & Bates, D.W. (2013). ''Relationship between medication event rates and the leapfrog computerized physician order entry evaluation tool''. J AM Med Inform Assoc., 20(e1), e85-e90. doi: 10.1136/amiajnl-2012-001549 Retrieved from: http://www.ncbi.nlm.nih.gov/pubmed/23599225 </ref>. The [https://leapfroghospitalsurvey.org/cpoe-evaluation-tool/ Leapfrog Evaluation tool] “has evolved into a well-balanced survey of hospital quality and safety, focusing on hospital structures, processes of care, and outcomes” <ref name="leapfrog, 2015"> Leapfrog hospital survey: ''About the survey''. (2015). Retrieved from https://leapfroghospitalsurvey.org/about-the-survey/ </ref>.<br />
<br />
==Results and Discussion ==<br />
The authors found that Leapfrog scores were highly related to the rate of preventable ADEs. It was noted that there was “a 43% relative reduction in the rate of preventable ADE was predicted for every 5% increase in Leapfrog scores (rate ratio 0.57; 95% CI 0.37 to 0.88)” <ref name="Leung, 2013"> Leung, A. A., Keohane, C., Lipsitz, S., Zimlichman, E., Amato, M., Simon, S. R., Coffey, M.,Kaufman, N., Cadet, B., Schiff, G., Seger, D.L., & Bates, D.W. (2013). ''Relationship between medication event rates and the leapfrog computerized physician order entry evaluation tool''. J AM Med Inform Assoc., 20(e1), e85-e90. doi: 10.1136/amiajnl-2012-001549 </ref>. The authors also found that there was a 5% improvement in Leapfrog scores associated with the relative rate reduction in preventable ADE by half to 100 hospital admissions. The authors of this articles’ findings therefore support the use of the Leapfrog tool as another means of evaluating a CPOE after implementation.<br />
<br />
Related Study: [[High rates of adverse drug events in a highly computerized hospital|High rates of adverse drug events in a highly computerized hospital]]<br />
<br />
==Comments ==<br />
This study was an interesting study as I was not familiar with the Leapfrog group and its association with the healthcare industry. It was interesting to read about the tool they developed in response to addressing ADEs with a CPOE evaluation tool. I am sure there are other tools and formats out there in which a CPOE may be evaluated. However, I was really impressed with this study’s findings and its implications for improving CPOE performance and monitoring throughout a healthcare facility. This tool will also be able to give providers feedback on system vulnerabilities and serve as an outline for areas of improvement.<br />
<br />
==References ==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:CPOE]]<br />
[[Category:Methodologies and Frameworks]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Planning_for_Radiology_CDS_TechnologyPlanning for Radiology CDS Technology2015-03-26T01:16:02Z<p>Vishaghera: </p>
<hr />
<div>Planning for Radiology CDS Technology.<ref name="KEan">Keen, Cynthia E. Healthcare Informatics. Planning for Radiology CDS Technology. Nov/Dec 2014; 31, 8; ProQuest Nursing & Allied Health Source http://search.proquest.com.ezproxyhost.library.tmc.edu/docview/1640578238?pq-origsite=summon&accountid=7034 </ref>.<br />
<br />
=== Headnote===<br />
With the advent of value based healthcare systems, the importance of correct and appropriate imaging emerges as an important aspect of providing patients value based and cost effective care. To discourage incorrect or unnecessary imaging orders by physicians, the Protecting Access to Medicare Act of 2014 will be in effect from Jan 1, 2017. Under this act any healthcare provider ordering an advanced imaging exam-specifically computerized tomography (CT), magnetic resonance imaging (MRI), nuclear medicine and positron emission tomography (PET) will be required to consult appropriateness criteria approved by the Centers for Medicare and Medicaid Services (CMS).CDS and CPOE systems integrated into EHR systems at hospitals has made the process of complying with these guidelines an intuitive and time saving process, provided hospitals and physicians adopt such systems in an efficient and timely manner<br />
<br />
===Evaluation of CDS Systems===<br />
Factors that should influence the selection of a CDS system include:<br />
• Integration- The system should integrate well with the existing [[CPOE|CPOE]] and [[EHR|EHR]] systems to facilitate smooth data flows across the systems and save time and effort.<br />
• Simplicity, ease of use and efficient workflow: The system should be user friendly, have a friendly UI , designed to minimize input boxes and mouse clicks , with concise and relevant data displayed to the physician as and when he/she needs it.<br />
• Flexibility: The system should have the ability to allow the user to modify orders midstream as per their requirements without impact to workflow or time.<br />
• Speed. The system performance should be superlative to be well accepted<br />
• Customization: The organization should be able to customize the system to suit local conditions, workflows, requirements etc.<br />
• Analytics- The system should have strong analytics built in to enable further planning and modifications.<br />
<br />
===Implementation of CDS System===<br />
Facilitating change management and early adoption of a CDS system to manage and support imaging requirements will enable the hospital to gradually successfully adapt and adopt the new system before the Protecting Access to Medicare Act of 2014 goes into effect. <br />
=== Discussion===<br />
Use of CDS systems in supporting and enhancing the quality and selection techniques of imaging would definitely reduce healthcare costs by minimizing unnecessary or inappropriate imaging orders as well as improving diagnoses and being an aid to physicians.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Planning_for_Radiology_CDS_TechnologyPlanning for Radiology CDS Technology2015-03-26T01:15:15Z<p>Vishaghera: Created page with "Planning for Radiology CDS Technology.<ref name="KEan">Keen, Cynthia E. Healthcare Informatics. Planning for Radiology CDS Technology. Nov/Dec 2014; 31, 8; ProQuest Nursing & ..."</p>
<hr />
<div>Planning for Radiology CDS Technology.<ref name="KEan">Keen, Cynthia E. Healthcare Informatics. Planning for Radiology CDS Technology. Nov/Dec 2014; 31, 8; ProQuest Nursing & Allied Health Source http://search.proquest.com.ezproxyhost.library.tmc.edu/docview/1640578238?pq-origsite=summon&accountid=7034 </ref>.<br />
<br />
=== Headnote===<br />
With the advent of value based healthcare systems, the importance of correct and appropriate imaging emerges as an important aspect of providing patients value based and cost effective care. To discourage incorrect or unnecessary imaging orders by physicians, the Protecting Access to Medicare Act of 2014 will be in effect from Jan 1, 2017. Under this act any healthcare provider ordering an advanced imaging exam-specifically computerized tomography (CT), magnetic resonance imaging (MRI), nuclear medicine and positron emission tomography (PET) will be required to consult appropriateness criteria approved by the Centers for Medicare and Medicaid Services (CMS).<br />
CDS and CPOE systems integrated into EHR systems at hospitals has made the process of complying with these guidelines an intuitive and time saving process, provided hospitals and physicians adopt such systems in an efficient and timely manner<br />
<br />
===Evaluation of CDS Systems===<br />
Factors that should influence the selection of a CDS system include:<br />
• Integration- The system should integrate well with the existing [[CPOE|CPOE]] and [[EHR|EHR]] systems to facilitate smooth data flows across the systems and save time and effort.<br />
• Simplicity, ease of use and efficient workflow: The system should be user friendly, have a friendly UI , designed to minimize input boxes and mouse clicks , with concise and relevant data displayed to the physician as and when he/she needs it.<br />
• Flexibility: The system should have the ability to allow the user to modify orders midstream as per their requirements without impact to workflow or time.<br />
• Speed. The system performance should be superlative to be well accepted<br />
• Customization: The organization should be able to customize the system to suit local conditions, workflows, requirements etc.<br />
• Analytics- The system should have strong analytics built in to enable further planning and modifications.<br />
<br />
===Implementation of CDS System===<br />
Facilitating change management and early adoption of a CDS system to manage and support imaging requirements will enable the hospital to gradually successfully adapt and adopt the new system before the Protecting Access to Medicare Act of 2014 goes into effect. <br />
=== Discussion===<br />
Use of CDS systems in supporting and enhancing the quality and selection techniques of imaging would definitely reduce healthcare costs by minimizing unnecessary or inappropriate imaging orders as well as improving diagnoses and being an aid to physicians.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Main_PageMain Page2015-03-26T01:11:02Z<p>Vishaghera: /* Workflow */</p>
<hr />
<div>'''Welcome to the OHSU Clinfowiki'''<br />
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The OHSU Clinical Informatics Wiki (aka ClinfoWiki) is the implementation of a [http://en.wikipedia.org/wiki/Wiki wiki] website devoted to topics in [[Biomedical Informatics]]. <br />
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=[[:Category:Technologies | Technologies]]=<br />
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==[[:Category: EHR | Electronic Medical Record (EMR)]]==<br />
* [[EHR | What is the Electronic Medical Record]]<br />
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==[[:Category: CPOE | Computerized Physician Order Entry (CPOE)]]==<br />
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<DynamicPageList><br />
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==[[:Category:Interface, Usability and Accessibility | Interface, Usability and Accessibility]]==<br />
* [[Usability]] <br />
* [[Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA]]<br />
* [[Software Test Documentation]]<br />
<br />
=[[:Category:Reference | Reference]]=<br />
<br />
==[[:Category:Academics and Education | Academics and Education]]==<br />
* [[Informatics Students' Contributions|Contributions from OHSU students]]<br />
* [[List of Informatics Departments]]<br />
* [[Endowed Professorships | Endowed Professorships and Chairs in Health / Medical / Nursing / Biomedical Informatics]]<br />
<br />
==[[:Category:Specialites and Disciplines | Specialites and Disciplines]]==<br />
* [[Medical Subspecialty Board of Clinical Informatics]]<br />
* [[Clinical Informatics Fellowship]]<br />
* [[Health informatics]]<br />
* [[Nursing informatics]]<br />
* [[Imaging informatics]]<br />
* [[Consumer health informatics]]<br />
* [[Public Health Informatics]]<br />
* [[Dental informatics]]<br />
* [[Medical laboratory informatics]]<br />
* [[Quality Informatics]]<br />
* [[Bioinformatics]]<br />
* [[Translational Bioinformatics]]<br />
* [[Clinical Social Work Informatics]]<br />
* [[Pharmacy Informatics]]<br />
* [[Clinical research informatics]]<br />
* [[Traditional Chinese Medicine (TCM) informatics]]<br />
<br />
==[[:Category:Literature | Literature]]==<br />
* [[Books | Books on Topics in Clinical Informatics]]<br />
* [[Leading Health Informatics and Medical Informatics Journals]]<br />
<br />
==[[:Category:External Links | External Links]]==<br />
* [http://www.ohsu.edu/xd/ Oregon Health & Science University]<br />
* [http://www.ohsu.edu/xd/education/schools/school-of-medicine/departments/clinical-departments/dmice/ The Department of Medical Informatics and Clinical Epidemiology at OHSU]<br />
* [http://www.cpoe.org Website of the Provider Order Entry Team from Oregon Health]<br />
* [http://hittransition.com/tools.htm Links to online tools for HIT/RHIO development]<br />
* [http://www.hitdashboard.com/unitedStates.aspx Health Information Technology Dashboard]<br />
* [http://wellness.wikispaces.com/Tactic+-+Use+Evolving+Health+Information+Technology+Tools Wellness Wiki: Use Evolving Health Information Technology Tools]<br />
* [http://www.emedicine.com/ eMedicine] Physician contributed medical articles and CME<br />
* [http://www.kmle.com KMLE Medical Dictionary] Medical dictionary and medical related links<br />
* [http://www.merckmedicus.com Merck Medicus] Contains a significant number of textbook resources (requires free registration<br />
* [http://www.nlm.nih.gov NLM] (US National Library of Medicine)<br />
* [http://www.webmd.com WebMD] General comprehensive online health information<br />
* [http://www.open.medicdrive.org Medicine 2.0] Comprehensive online Personal health record information.<br />
* [http://www.searchmedica.com SearchMedica.com] Searches medical literature for health care professionals<br />
* [http://www.ahima.org AHIMA] American Health Information Management Association<br />
* [http://www.amia.org AMIA] American Medical Informatics Association<br />
* [http://www.ania-caring.org ANIA-CARING] American Nursing Informatics Association and the Capital Area Roundtable on Informatics in NursinG<br />
* [http://www.himss.org HIMSS] Healthcare Information and Management Systems<br />
* [http://www.imia-medinfo.org IMIA] International Medical Informatics Association<br />
* [http://wellness.wikispaces.com/Tactic+-+Use+Evolving+Health+Information+Technology+Tools Use Evolving Health Information Technology Tools]<br />
* [http://wellness.wikispaces.com/Blueprint+for+an+Integrated+HIT+system+-+The+Patient+Life-Cycle+Wellness+System Blueprint for an Integrated HIT system - The Patient Life-Cycle Wellness System]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Effect_of_Computerized_Physician_Order_Entry_with_Clinical_Decision_Support_on_the_Rates_of_Adverse_Drug_Events:_A_Systematic_ReviewThe Effect of Computerized Physician Order Entry with Clinical Decision Support on the Rates of Adverse Drug Events: A Systematic Review2015-03-05T01:53:11Z<p>Vishaghera: /* Methods */</p>
<hr />
<div>=== Abstract ===<br />
The purpose of this article is to quantify what impact, if any, [[CPOE | computerized physician order entry]] (CPOE) with [[CDS | clinical decision support systems]] (CDSS) have had in the development of [[ADE | adverse drug events]] (ADE). <ref name= "CDSS">Wolfstadt, J. I., Gurwitz, J. H., Field, T. S., Lee, M., Kalkar, S., Wu, W., & Rochon, P. A. (2008). The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. Journal of General Internal Medicine, 23(4), 451-458. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2359507/</ref><br />
<br />
=== Methods ===<br />
[[Randomized controlled trial (RCT)|Randomized]] and nonrandomized clinical trials as well as studies were analyzed to determine their “the effect on the rates of ADEs when CPOE with CDSS were utilized. The studies kept track of the types of the system, ADEs, drug categories, and the final outcome. <ref name= "CDSS"></ref><br />
<br />
=== Results ===<br />
10 studies out of the 543 originally evaluated qualified for the criteria of this study. The studies were classified as applying to either ‘hospital’ or ‘ambulatory’ and had the following results: <ref name= "CDSS"></ref><br />
<br />
* 50% of the studies had statistically significant decrease in ADE rates<br />
* 40% of the studies had non-statistically significant decrease in ADE rates<br />
* 10% of the studies reported no change in ADE rates<br />
<br />
=== Conclusions ===<br />
There have been very few studies performed on the effect CPOE with CDSS have on ADEs. Further research will need to be conducted in order to accurately quantify the impact CPOE with CDSS are capable of achieving in clinical settings.<br />
<br />
=== Comments ===<br />
Adverse drug events should be prevented as much as possible. CPOE with CDSS have shown they can be an effective tool in assisting with this task. I agree with the article’s conclusion recommendation for further research to be conducted as it might lead to figuring out how method of implementing CPOE with CDSS which obtains statistically significant reduction in ADE rates in all settings, and not just half.<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Effect_of_Computerized_Physician_Order_Entry_with_Clinical_Decision_Support_on_the_Rates_of_Adverse_Drug_Events:_A_Systematic_ReviewThe Effect of Computerized Physician Order Entry with Clinical Decision Support on the Rates of Adverse Drug Events: A Systematic Review2015-03-05T01:52:23Z<p>Vishaghera: /* Methods */</p>
<hr />
<div>=== Abstract ===<br />
The purpose of this article is to quantify what impact, if any, [[CPOE | computerized physician order entry]] (CPOE) with [[CDS | clinical decision support systems]] (CDSS) have had in the development of [[ADE | adverse drug events]] (ADE). <ref name= "CDSS">Wolfstadt, J. I., Gurwitz, J. H., Field, T. S., Lee, M., Kalkar, S., Wu, W., & Rochon, P. A. (2008). The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. Journal of General Internal Medicine, 23(4), 451-458. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2359507/</ref><br />
<br />
=== Methods ===<br />
[[Randomized| Randomized controlled trial (RCT)]] and nonrandomized clinical trials as well as studies were analyzed to determine their “the effect on the rates of ADEs when CPOE with CDSS were utilized. The studies kept track of the types of the system, ADEs, drug categories, and the final outcome. <ref name= "CDSS"></ref><br />
<br />
=== Results ===<br />
10 studies out of the 543 originally evaluated qualified for the criteria of this study. The studies were classified as applying to either ‘hospital’ or ‘ambulatory’ and had the following results: <ref name= "CDSS"></ref><br />
<br />
* 50% of the studies had statistically significant decrease in ADE rates<br />
* 40% of the studies had non-statistically significant decrease in ADE rates<br />
* 10% of the studies reported no change in ADE rates<br />
<br />
=== Conclusions ===<br />
There have been very few studies performed on the effect CPOE with CDSS have on ADEs. Further research will need to be conducted in order to accurately quantify the impact CPOE with CDSS are capable of achieving in clinical settings.<br />
<br />
=== Comments ===<br />
Adverse drug events should be prevented as much as possible. CPOE with CDSS have shown they can be an effective tool in assisting with this task. I agree with the article’s conclusion recommendation for further research to be conducted as it might lead to figuring out how method of implementing CPOE with CDSS which obtains statistically significant reduction in ADE rates in all settings, and not just half.<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Effect_of_Computerized_Physician_Order_Entry_with_Clinical_Decision_Support_on_the_Rates_of_Adverse_Drug_Events:_A_Systematic_ReviewThe Effect of Computerized Physician Order Entry with Clinical Decision Support on the Rates of Adverse Drug Events: A Systematic Review2015-03-05T01:51:35Z<p>Vishaghera: /* Methods */</p>
<hr />
<div>=== Abstract ===<br />
The purpose of this article is to quantify what impact, if any, [[CPOE | computerized physician order entry]] (CPOE) with [[CDS | clinical decision support systems]] (CDSS) have had in the development of [[ADE | adverse drug events]] (ADE). <ref name= "CDSS">Wolfstadt, J. I., Gurwitz, J. H., Field, T. S., Lee, M., Kalkar, S., Wu, W., & Rochon, P. A. (2008). The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. Journal of General Internal Medicine, 23(4), 451-458. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2359507/</ref><br />
<br />
=== Methods ===<br />
[[Randomized| Randomized controlled trials ( RCT)]] and nonrandomized clinical trials as well as studies were analyzed to determine their “the effect on the rates of ADEs when CPOE with CDSS were utilized. The studies kept track of the types of the system, ADEs, drug categories, and the final outcome. <ref name= "CDSS"></ref><br />
<br />
=== Results ===<br />
10 studies out of the 543 originally evaluated qualified for the criteria of this study. The studies were classified as applying to either ‘hospital’ or ‘ambulatory’ and had the following results: <ref name= "CDSS"></ref><br />
<br />
* 50% of the studies had statistically significant decrease in ADE rates<br />
* 40% of the studies had non-statistically significant decrease in ADE rates<br />
* 10% of the studies reported no change in ADE rates<br />
<br />
=== Conclusions ===<br />
There have been very few studies performed on the effect CPOE with CDSS have on ADEs. Further research will need to be conducted in order to accurately quantify the impact CPOE with CDSS are capable of achieving in clinical settings.<br />
<br />
=== Comments ===<br />
Adverse drug events should be prevented as much as possible. CPOE with CDSS have shown they can be an effective tool in assisting with this task. I agree with the article’s conclusion recommendation for further research to be conducted as it might lead to figuring out how method of implementing CPOE with CDSS which obtains statistically significant reduction in ADE rates in all settings, and not just half.<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: CPOE]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Impact_of_clinical_decision_support_on_head_computed_tomography_use_in_patients_with_mild_traumatic_brain_injury_in_the_EDImpact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED2015-03-05T01:42:14Z<p>Vishaghera: </p>
<hr />
<div>This is a review of research article, “Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED by Ivan et al “<ref name =”Ivan”> Ivan K. Ip, Ali S. Raja, Anurag Gupta, James Andruchow, Aaron Sodickson, Ramin Khorasani, Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED, The American Journal of Emergency Medicine, Available online 12 November 2014, ISSN 0735-6757, http://dx.doi.org/10.1016/j.ajem.2014.11.005.<br />
(http://www.sciencedirect.com/science/article/pii/S0735675714007943).</ref>.<br />
<br />
<br />
==Introduction==<br />
<br />
This study was performed to study the effect of real-time computerized clinical decision support ([[CDS]]) on the use of CT scan in adult patients with [http://www.biausa.org/mild-brain-injury.htm/'''mild traumatic brain injuries'''](MTBI) in the ED.<br />
<br />
==Background==<br />
Around 1.2 million outpatient visits occurs for mild traumatic brain injuries in the EDs. Most of the patients undergo unnecessary head CT scan although most patients have no clinical consequence. It leads to over diagnosis and unnecessary cost to the patients and hospitals.<br />
==Methods==<br />
The observational cohort study was performed at academic quaternary care ,793 bed ,level 1 trauma center in between January 1,2009 and December 31,2010.This study has included all adult ED patient with a discharge diagnosis of MTBI.A control cohort consisting of ED patients with MTBI abstracted from most recent publicly available National Hospital Ambulatory Medical Care Survey (NHAMCS) during study period. A real time computerized CDS was installed in the institutional imaging computerized physician order entry ([[CPOE]]) for intervention purpose.<br />
<br />
==Results==<br />
The study result observed a decrease in the utilization rate of head CT scans among the patient with MTBI after intervention of CDS. In the pre-intervention, 58.1% of MTBI ED visits (n = 372/640) were associated with a head CT being performed, whereas in the post-CDS intervention, utilization decreased to a rate of 50.3% (n = 333/662).<br />
==Conclusions==<br />
Implementation of CDS for MTBI patients is associated with significant decrease in head CT use.<br />
==Comments==<br />
This study didn’t mention how did they manage unintended consequences such as changes in workflow, communication changes, duplicate errors on implementation of CPOE and CDS system.<br />
<br />
Related Reviews: [[Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention|Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention]]<br />
<br />
=References=<br />
<references/><br />
<br />
[[Category: CDS]]<br />
[[Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Computer_physician_order_entry:_benefits,_costs,_and_issues.Computer physician order entry: benefits, costs, and issues.2015-03-05T01:26:18Z<p>Vishaghera: /* Introduction */</p>
<hr />
<div>This is an article review of Kuperman, G. J., & Gibson, R. F. (2003). Computer physician order entry: benefits, costs, and issues. Annals of internal medicine, 139(1), 31-39. <ref name= "Gibson 2003">Kuperman, G. J., & Gibson, R. F. (2003). Computer physician order entry: benefits, costs, and issues. Annals of internal medicine, 139(1), 31-39. http://annals.org/article.aspx?articleid=716518</ref><br />
<br />
===Introduction===<br />
An important component of [[Computer Physician Order Entry|CPOE]] (CPOE) is its ability to offer physicians the convenience of entering orders through an electronic means as opposed to the manually entering orders by handwriting. A computerized mode of order entry fundamentally changes the ordering process. However, with technology being used in CPOE, there is a tendency for abuse which could take the form of overuse, underuse and misuse of such platform.<br />
<br />
===Benefits of CPOE===<br />
*In eligible patients, reminders in CPOE systems increased ordering rates for prophylactic aspirin, coronary artery disease, pneumococcal vaccine, influenza vaccine and subcutaneous heparin with CPOE.<br />
*Compliance with the monitoring of drug levels doubled when automated ordering reminders were implemented.<br />
*Overuse of diagnostic procedures and antibiotics has been well documented and can be addressed by CPOE.<br />
*CPOE can present cost data, previous result and information about the likelihood of finding and abnormal result all of which have been shown to reduce the overuse of diagnostic test.<br />
*A feature in CPOE such as patient specific dosing suggestions, reminders to monitor drug levels, reminders to choose an appropriate drug, checking for drug-allergy and drug-drug interactions, and reference information while ordering have been known to enhance patient safety.<br />
*The benefits of interfacing CPOE to a pharmacy application and interfacing CPOE to an electronic medical administration record helped eliminate transcription errors.<br />
<br />
===Costs of CPOE===<br />
*CPOE requires a robust information infrastructure which requires technical costs, cost of process redesign, and cost of implementation and support.<br />
*Technical cost of CPOE includes cost of hardware, software, technical support and integration with existing systems.<br />
*The network infrastructure that connects CPOE systems and devices at every hospital, clinic and pharmacy location must be fast-requiring a robust bandwidth that’s secure and reliable.<br />
*Another important aspect of a CPOE implementation is the cost of the software license fee. <br />
*Post implementation support such as staff training and post go-live should also be considered.<br />
<br />
===Issues to Consider===<br />
*Other clinical systems ideally should be implemented such as an EMR, so that lessons learnt and mistakes made will be avoided during a complex CPOE implementation. <br />
*Because of the size and complexity of a CPOE implementation, unintended consequences and workflow issues that arise during implementation should be addressed. <br />
*Due to the complexity of a CPOE implementation, it is advised that other administrative and clinical projects do not run concurrently with a CPOE implementation. <br />
<br />
===Comments===<br />
Previous studies have shown much progress in CPOE as it reduces the cost of healthcare, reduces the average length of stay, lessens the incidence of medical errors and enables organizations attain regulatory compliance. Other benefits of CPOE that are known to enhance patient safety are standardized order sets, increased legibility, automated communications to ancillary departments and outreach labs while providing easy access to patient data.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CPOE]]<br />
[[Category: CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Reducing_unnecessary_testing_in_a_CPOE_system_through_implementation_of_a_targeted_CDS_interventionReducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention2015-03-05T01:05:44Z<p>Vishaghera: /* Conclusions */</p>
<hr />
<div>Donald L Levick, Glenn Stern, Chad D Meyerhoefer, Aaron Levick4 and David Pucklavage. Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention. BMC Medical Informatics and Decision Making 2013 <ref name="Levick et al"> http://www.biomedcentral.com/1472-6947/13/43 </ref>. <br />
<br />
<br />
=== Background===<br />
Observational studies are needed to assess the effect of [[CDS| CDS]] and CPOE systems on resource optimization and the appropriate use of laboratory tests and imaging techniques. This study describes the development and use of a CDS intervention to reduce unnecessary testing (and associated costs) in a CPOE system, and evaluates its effectiveness. B-Type Natriuretic Peptide (BNP) testing ( a diagnostic laboratory test among critical care patients) was identified as an overused laboratory test that has no beneficial effect to being repeated more than once in a hospital stay. <br />
<br />
===Methods===<br />
The CPOE system was modified to employ an expert rule to alert the ordering clinician that a BNP had been performed during the current admission and that repeat testing would add no value to the clinical decision making process. The authors carried out multivariate regression analysis to assess the effectiveness of the intervention<br />
<br />
=== Results===<br />
The CDS intervention for repeat BNP testing was implemented in June, 2009 and BNP ordering decreased by approximately 65% within six months of introduction of the intervention. The regression results suggested the CDS intervention reduced BNP orders by 21% relative to the mean. The financial impact of the rule was also significant. Multiplying by the direct supply cost of $28.04 per test, the intervention saved approximately $92,000 per year.<br />
<br />
===Conclusion===<br />
The findings of this study suggest that appropriately designed and carefully implemented CDS interventions can have a substantial impact on resource optimization and healthcare costs.<br />
<br />
=== Discussion===<br />
It is important that these interventions should be used appropriately and judiciously as too many alerts can give rise to alert fatigue , thus reducing the impact and efficacy of the CPOE/ CDS systems.<br />
<br />
===References===<br />
<references/><br />
<br />
[[ Category: CDS]]<br />
[[ Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Reducing_unnecessary_testing_in_a_CPOE_system_through_implementation_of_a_targeted_CDS_interventionReducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention2015-03-05T01:04:23Z<p>Vishaghera: Created page with "Donald L Levick, Glenn Stern, Chad D Meyerhoefer, Aaron Levick4 and David Pucklavage. Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS in..."</p>
<hr />
<div>Donald L Levick, Glenn Stern, Chad D Meyerhoefer, Aaron Levick4 and David Pucklavage. Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention. BMC Medical Informatics and Decision Making 2013 <ref name="Levick et al"> http://www.biomedcentral.com/1472-6947/13/43 </ref>. <br />
<br />
<br />
=== Background===<br />
Observational studies are needed to assess the effect of [[CDS| CDS]] and CPOE systems on resource optimization and the appropriate use of laboratory tests and imaging techniques. This study describes the development and use of a CDS intervention to reduce unnecessary testing (and associated costs) in a CPOE system, and evaluates its effectiveness. B-Type Natriuretic Peptide (BNP) testing ( a diagnostic laboratory test among critical care patients) was identified as an overused laboratory test that has no beneficial effect to being repeated more than once in a hospital stay. <br />
<br />
===Methods===<br />
The CPOE system was modified to employ an expert rule to alert the ordering clinician that a BNP had been performed during the current admission and that repeat testing would add no value to the clinical decision making process. The authors carried out multivariate regression analysis to assess the effectiveness of the intervention<br />
<br />
=== Results===<br />
The CDS intervention for repeat BNP testing was implemented in June, 2009 and BNP ordering decreased by approximately 65% within six months of introduction of the intervention. The regression results suggested the CDS intervention reduced BNP orders by 21% relative to the mean. The financial impact of the rule was also significant. Multiplying by the direct supply cost of $28.04 per test, the intervention saved approximately $92,000 per year.<br />
<br />
===Conclusions===<br />
The findings of this study suggest that appropriately designed and carefully implemented CDS interventions can have a substantial impact on resource optimization and healthcare costs. <br />
=== Discussion===<br />
It is important that these interventions should be used appropriately and judiciously as too many alerts can give rise to alert fatigue , thus reducing the impact and efficacy of the CPOE/ CDS systems.<br />
<br />
===References===<br />
<references/><br />
<br />
[[ Category: CDS]]<br />
[[ Category: Reviews]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/CDSCDS2015-03-05T00:45:56Z<p>Vishaghera: /* Reviews */</p>
<hr />
<div>'''Clinical decision support (CDS)''' refers broadly to providing clinicians or patients with clinical knowledge, intelligently filtered and presented at appropriate times. <ref name="slater 2008">Slater, B. Osheroff, JA. Clinical Decision Support, in Electronic Health Records: A Guide for Clinicians and Administrators. American College of Physicians. 2008. http://books.google.com/books?hl=en&lr=&id=KtlUMwaZP98C</ref> Clinical knowledge of interest could range from simple facts and relationships (such as an individual patient's vital signs, allergies and lab data) to relevant medical knowledge (such as best practices for managing patients with specific disease states, new clinical research, professional organizations' practice guidelines, expert opinion, systematic reviews, and other types of information.<br />
<br />
== History ==<br />
<br />
Clinical decision support tools existed prior to development of [[EMR|electronic medical records (EMRs)]]. They include expert consultation, practice guidelines carried in clinicians' pockets, patient cards used by nurses to track a patient's treatments, tables of important medical knowledge, and ICU patient flow sheets. Many of these CDS tools are still relevant, but integration of CDS with current EMRs presents an opportunity for the various types of decision support to be immediately available at the time of the decision-making. CDS can be more relevant, more accurate, and can facilitate and be integrated with clinical workflow.<br />
<br />
For more on the history of CDS, see [[Timeline of the Development of Clinical Decision Support|here]] and [[The Evolution of Clinical Decision Support|here]].<br />
<br />
== CDS components ==<br />
<br />
There are several key components of a good clinical decision support system.<br />
<br />
* Documentation tools<br />
* Clinician Checklists<br />
* Calculators<br />
* Reference Links<br />
<br />
=== Documentation forms/templates ===<br />
As mentioned above, these existed prior to EMRs in the form of structured documentation forms for conducting clinician assessments. Many of these have been supplanted by digital reproductions in EMR of the original paper documentation form.<br />
<br />
Examples include:<br />
* nursing intake forms<br />
* physician "History & Physicals"<br />
* ER templates<br />
<br />
Other tools that were artifacts of clinician workflow and existed prior to EMR implementation, now have the potential for added functionality when computerized, web-based, or automated. Added functionality includes dispersed access to the tool's information (ability for multiple users from multiple disciplines and geographic locations to share a single set of information), auto-population of accurate and current data from the clinical information system, linkages between tool task lists and CPOE, and improved order fulfillment efficiency.<br />
<br />
Examples of these tools include:<br />
* [[Sign-out|Handoff tools]] (lists of patients with summations of clinical data used at time of a shift handoff between clinicians)<br />
* Rounding tools (summaries of data on a single patient, clinical task lists<br />
* ICU flowsheets for documenting and charting vital signs and hemodynamic data.<br />
<br />
=== Alerts and reminders ===<br />
<br />
[[Alerts]] are an important part of CDS.<br />
<br />
Examples include:<br />
* [[Alerts|Alert]] that appropriate cancer screening is due.<br />
* Drug allergy alert<br />
* Drug interaction alert<br />
* Underdose/overdose alerts based on renal or liver function, age, drug level<br />
* Result alerts to follow up with patient if a HBA1c was elevated patient needed to be retested in 3 months. <ref name="The Impact of a Decision Support linked to an Electronic Medical Record on Glycemic Control in People with Type 2 Diabetics">The Impact of a Decision Support Tool Linked to an Electronic Medical Record on Glycemic Control in People with type 2 Diabetes.http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC3869133/</ref><br />
<br />
=== Relevant data presentation ===<br />
<br />
Examples of this include:<br />
<br />
a) Patient specific data such as:<br />
* Display of relevant labs during medication CPOE such as patient's renal and liver function.<br />
* Display of other relevant patient data during CPOE such as patient's age (which may affect side affects and dosing) or conditions.<br />
<br />
b) Population-specific data such as:<br />
* Retrospective filtering and aggregate reporting: disease registries and clinic population dashboards.<br />
* Microbiograms: tables of local bacterial flora and their sensitivity and susceptibility to various antibiotics<br />
<br />
=== Order creation facilitators ===<br />
Examples include: order sets, order menus, tools for complex ordering, and "single-order completers including consequent order."<br />
<br />
==== Order Sets ====<br />
<br />
An [[order set]] is a group of related orders which a physician can place with a few keystrokes or mouse clicks. An order set allows users to issue prepackaged groups of orders that apply to a specified diagnosis or a particular period of time. Using order sets reduces both time spent entering orders and terminal usage. An order set may or may not contain medication orders as part of the set.<br />
<br />
An example order set for Cardiac MRI order would include:<br />
* MRI order<br />
* Prescription to dispense IV contrast<br />
* Prescription for sedative during MRI<br />
* Order for renal function lab if none in EMR in last week<br />
* Order for transportation from hospital ward to radiology at time of MRI<br />
<br />
==== Order Menus ====<br />
An order menu is a group of related orders which are depicted onscreen together via an EMR's GUI so that an ordering clinician visualizes the breath and organization of the orders. An order menu allows CPOE/EMR developers to direct clinicians towards the most common or appropriate orders for a particular topic. Using order sets reduces time spent searching for the desired orders and provides a rudimentary level of knowledge and education. Order sets are commonly made up of medication orders, but non-medication orders may be included.<br />
<br />
Examples of order menu content include:<br />
* anti-hypertensive medications arranged by class, by preference, by cost, or other means.<br />
* common pulmonary medications to treat COPD, asthma, embolisms, and chronic cough.<br />
<br />
<br />
== Interaction models ==<br />
<br />
An [[interaction model]] is a set of rules for making clinical decisions. The rules are based on a large collection of medical knowledge and an accurate computer representation scheme.<br />
<br />
=== Artificial intelligence ===<br />
<br />
[[Artificial intelligence]] is a system that was developed by a team of system engineers and clinicians. The system would take some of the workload from medical teams by assisting the physicians with tasks like diagnosis & Therapy recommendations.<br />
<br />
=== Business Intelligence and Data Warehousing ===<br />
<br />
*[[Business intelligence]]<br />
*[[Data warehouse]]<br />
<br />
=== Validation and Verification of Clinical Decision Support ===<br />
*[[On Validation and Verification Of Decision Support Protocol Subsystems During Implementation-Optimization: Encapsulating P(X)]]<br />
<br />
===Sample Decision Support Content===<br />
<br />
* [[Diabetes CDS Content]]<br />
* [[Drug-Drug Interaction Rules]]<br />
* [[Clinical Reminders from Beth Israel/Deaconess Medical Center]] in Boston<br />
* Symptom Triage Decision Support for Consumers (example: "Chest Pain") [http://www.freemd.com/fmdTriage.html?e=Chest%20Pain]<br />
* [[Weight-based Heparin Dosing Guidelines]]<br />
* [[Flowchart-based decision support sample content]]<br />
* [[Preventive care reminders]]<br />
* [[Mental health clinical decision support]]<br />
* [[Computerized clinical decision support systems for chronic disease management]]<br />
* [[Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records]]<br />
<br />
=== Reviews ===<br />
<br />
* [[A description and functional taxonomy of rule-based decision support content at a large integrated delivery network.]]<br />
* [[Computerized clinical decision support systems for chronic disease management]]<br />
* [[Expert clinical rules automate steps in delivering evidence-based care in the electronic health record]]<br />
* [[Impact of electronic reminders on venous thromboprophylaxis after admissions and transfers]]<br />
* [[Drug–drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records]]<br />
* [[Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis]]<br />
* [[Towards Meaningful Medication-Related Clinical Decision Support: Recommendations for an Initial Implementation]]<br />
* [[Clinical Decision Support: A tool of the Hospital Trade]]<br />
* [[Development and use of active clinical decision support for preemptive pharmacogenomics]]<br />
* [[Effect of Clinical Decision-Support Systems: A Systematic Review]]<br />
* [[Clinical decision support: progress and opportunities]]<br />
* [[A qualitative study of the activities performed by people involved in clinical decision support: recommended practices for success]]<br />
* [[Information system support as a critical success factor for chronic disease management: Necessary but not sufficient]]<br />
* [[A nursing clinical decision support system and potential predictors of head-of-bed position for patients receiving mechanical ventilation.]]<br />
* [[Evaluating Clinical Decision Support Systems:Monitoring CPOE Order Check Override Rates in the Department of Veterans Affairs’ Computerized Patient Record System]]<br />
* [[Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records]]<br />
* [[Clinical decision support or genetically guided personalized medicine: a systematic review]]<br />
* [[The Effect of Computerized Physician Order Entry with Clinical Decision Support on the Rates of Adverse Drug Events: A Systematic Review]]<br />
* [[Effects of Computerized Physician Order Entry and Clinical Decision Support Systems on Medication Safety]]<br />
* [[Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial]]<br />
* [[Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention]]<br />
<br />
== CDS Implementation ==<br />
<br />
CDS should be designed to provide the right information to the right person in the right format through the right channel at the right time.<br />
<br />
At the stage of planning for implementation of any new health IT system or their components, there are some considerations and steps that should be followed to maximize CDS system success:<br />
<br />
# Needs Assessment: ensuring that identified clinical needs and functional requirements<br />
# Assessing Organizational Readiness<br />
i) Understanding prior physician and organizational experience with CDS<br />
ii) Assessment of level of physician knowledge, [[perception]], engagement, and willingness to change<br />
iii) Aligned leadership with clear objectives<br />
# CDS related factors<br />
i) Deciding whether to purchasing a commercial system or build the system<br />
ii) CDS usability: Will CDS increase physician workload? Can the level of intrusiveness of alerts be customized?<br />
iii) Adequate planning for encouraging physicians to use CDS<br />
iv) Appropriate training on using CDS<br />
v) Mechanisms in place to evaluate usage and effectiveness of the CDS<br />
<br />
=== Alerts ===<br />
<br />
* [[Alert fatigue]]<br />
* [[Improving acceptance of computerized prescribing alerts in ambulatory care]]<br />
<br />
=== Liability ===<br />
*[[Clinical decision support liability|Liability of physicians, hospitals, and EHRs]]<br />
<br />
=== Workflow ===<br />
<br />
=== Usability ===<br />
<br />
*Evidence based content / Clinical content accuracy<br />
*Changing behavior (limited interaction by users, adherence to protocol)<br />
*Training and communication<br />
*System design limitations<br />
<br />
*Choosing the right metrics for reporting (Process / Clinical)<br />
*Potential breaks due to system upgrades<br />
<br />
== Clinical Decision Support Overview ==<br />
<br />
*[[National Roadmap for Clinical Decision Support]]<br />
*[[General system features associated with improvements in clinical practice]]<br />
*[http://wellness.wikispaces.com/Tactic+-+Support+Decisions+with+Diagnostic+Aids Support Decisions with Diagnostic Aids]<br />
*[[Clinical Decision Support Liability]]<br />
*[[Exploring a Clinically Friendly Web-Based Approach to Clinical Decision Support Linked to the Electronic Health Record A Design Philosophy Prototype Implementation and Framework for Assessment]]<br />
<br />
== CDS success measures ==<br />
<br />
To estimate the success of the system we should look at the following points[3]:<br />
# System quality.<br />
# Information quality<br />
# Usage<br />
# User satisfaction (Process Outcome)<br />
# Individual impact (Clinical Outcome)<br />
# Organizational impact (Financial outcome).<br />
<br />
<br />
===[[Information Resources]]===<br />
<br />
*[http://himssclinicaldecisionsupportwiki.pbworks.com/ The HIMSS Clinical Decision Support (CDS) Task Force wiki]<br />
*[[Alert placement in clinical workflow]]<br />
*[[Initial Selection of What to Alert on...]]<br />
*[[Alerts versus on-demand CDS]]<br />
*[[Sources of clinical decision support content]]<br />
* Here is a video of CDS in action within the free EHR drchrono [http://www.youtube.com/watch?v=Y9XuXZUE9NI].<br />
<br />
== CDS benefits ==<br />
<br />
Results indicate the potential of CDS to improve the quality of care. These are good reasons for institutions to adopt CDS, but they should do so at their own pace and volition.<br />
<br />
=== Promote use of evidence based recommendations ===<br />
[[Improving antibiotic prescribing for adults with community acquired pneumonia: Does a computerised decision support system achieve more than academic detailing alone?--A time series analysis|A stand-alone, disease-specific CDSS can improve concordance with established prescribing guidelines for a period measured in months.]]<br />
<br />
=== Better clinical decision-making ===<br />
<br />
* [[Decision Support in Psychiatry - a comparison between the diagnostic outcomes using a computerized decision support system versus manual diagnosis]]<br />
* [[Information system support as a critical success factor for chronic disease management]]<br />
* [[Classification models for the prediction of clinicians' information needs]]<br />
<br />
=== Reduced medication errors ===<br />
=== Improved cost-effectiveness ===<br />
More research is needed to identify the cost-effectiveness of CDS as current research has found conflicting results of increased, decreased, or no change in cost of care. [http://www.biomedcentral.com/content/pdf/1472-6947-13-135.pdf] [http://www.implementationscience.com/content/pdf/1748-5908-6-89.pdf]<br />
<br />
=== Increased patient convenience ===<br />
=== Improved quality of healthcare delivery ===<br />
[["Smart Forms" in an Electronic Medical Record: documentation-based clinical decision support to improve disease management.]]<br />
<br />
=== Improved healthcare outcomes for patients and patient populations ===<br />
<br />
Current research has shown various systems associated with improved health outcomes but is still limited and requires more research. However, it has helped improved outcomes for chronic disease management particularly for individuals living with diabetes. [http://www.countyhealthrankings.org/policies/computerized-clinical-decision-support-systems-cdss]<br />
[[Formative evaluation of clinician experience with integrating family history-based clinical decision support into clinical practice|Family Health History]] is a leading predictor of disease risk. Clinical Decision Support can also be used to help healthcare providers fill in the family history gap<br />
<ref name="Family Health History">Formative evaluation of clinician experience with integrated family history-based clinical decision support into clinical practice.http://clinfowiki.org/wiki/index.</ref><br />
<br />
== Reviews ==<br />
<br />
* [[Evaluation of Medication Alerts in Electronic Health Records for Compliance with Human Factors Principles]]<br />
* [[Evaluation of User Interface and Workflow Design of a Bedside Nursing Clinical Decision Support System]]<br />
* [[Clinical Decision Support and Appropriateness of Antimicrobial Prescribing – A Randomized Trail]]<br />
* [[Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective ‘before/after’ cohort study]]<br />
* [[Perceived barriers of heart failure nurses and cardiologists in using clinical decision support systems in the treatment of heart failure patients]]<br />
* [[Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings]]<br />
* [[Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records]]<br />
* [[The Reliability of an Epilepsy Treatment Clinical Decision Support System|The Reliability of an Epilepsy Treatment Clinical Decision Support System]]<br />
* [[Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial]]<br />
* [[A trial of automated decision support alerts for contraindicated medications using computerized physician order entry]]<br />
* [[Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records]]<br />
* [[Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems]]<br />
* [[Impact of electronic health record clinical decision support on diabetes care: a randomized trial]]<br />
* [[Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED]]<br />
<br />
== References ==<br />
<references/><br />
<br />
# slater 2008<br />
# Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [http://www.ncbi.nlm.nih.gov/pubmed/9929233]<br />
# Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [http://www.ncbi.nlm.nih.gov/pubmed/7719793]<br />
# Anderson, JG, et al, Physician Utilization of a hospital information system: a computer simulation model. Pric Annu Symp Compu Appl Med Care, IEEE, 1988;12:858-861. [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245247/]<br />
# Southern Ohio Medical Center, [http://www.somc.org/for_doctors/orders/bonzo/]<br />
# Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009<br />
# Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392<br />
# Determinants of Success of Inpatient Clinical Information Systems: A Literature Review. M J van der Meijden, H J Tange, J Troost, et al. JAMIA 2003 10: 235* 243<br />
# Improving Outcomes with Clinical Decision Support: An Implementer's Guide [Paperback]: Jerry Osheroff, Jonathan Teich, Donald Levick, Luis Saldana, Ferdinand Velasco, Dean Sittig, Kendall Rogers and Robert Jenders <br />
<br />
<br />
[[Category: CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Criteria_for_assessing_high-priority_drug-drug_interactions_for_clinical_decision_support_in_electronic_health_recordsCriteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records2015-02-25T05:58:36Z<p>Vishaghera: /* Abstract */</p>
<hr />
<div>This is a review of Phansalkar, S., Desai, A., Choksi, A., Yoshida, E., Doole, J., Czochanski, M., Tucker, A. D., Middleton, B., Bell, B., & Bates, D.W. 2013 article, “Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records”.<ref name="Phansalkar, 2013"> Phansalkar, S., Desai, A., Choksi, A., Yoshida, E., Doole, J., Czochanski, M., Tucker, A. D., Middleton, B., Bell, B., & Bates, D.W. (2013). ''Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records''. BMC Med Inform Decis Mak., 13(1), doi: 10.1186/1472-6947-13-65 Retrieved from: http://www.biomedcentral.com/1472-6947/13/65 </ref><br />
<br />
== Abstract ==<br />
The purpose of this article was to shed some more light in regards to the high override rates for [[Drug-drug interaction|drug-drug interaction]] (DDI) alerts in [[EHR|electronic health records]] (EHRs). The authors explained that due to high DDI alert overrides many providers are increasingly ignoring those alerts which could be dangerous when administrating patient care. This is called [[Alert Fatigue]]. There is also a lack of uniformity criteria for determining the severity and validity of alerts. Therefore, the authors of this article explore this area of care and identify a set of criteria for assessing DDIs that can be used for [[CDS|clinical decision support]] (CDS) alerts in EHRs.<br />
<br />
==Methods ==<br />
The authors of this article conducted a literature review for the span of 20 years. The dates were from January 1990 to December 2010. They reviewed articles on MEDLINE and EMBASE databases to identify characteristics of high-priority DDI alerts. The method in which the authors sorted through their research was that they looked for the presence of specific keywords and MeSH terms in two categories within the title, abstract and body of every article. Each reviewer extracted criteria from relevant studies on how the evidence on DDIs could be filtered or tailored to identify a high priority DDI. Once the articles were chosen a panel of experts were then used to validate the data gathered from the systematic literature review. To validate their findings a panel of experts were used in which consisted of a wide range of experience within the subject matter. The panel expertise consisted of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers and agencies involved in the regulation of medication use both federal and private state levels.<br />
<br />
==Results and Discussion ==<br />
There were 44 articles that were selected and reviewed in which met the inclusion criteria for assessing characteristics of high-priority DDIs. As a result the panel proposed 5 items that were deemed to be the most important when assessing an interaction. They are the following:<br />
<br />
*'''Severity:''' More research is needed to understand alert variation for assessing the severity of an interaction.<br />
*'''Probability:''' Probability is harder to determine without knowing the patient context. In the case of an ADE it is suggested to include the concentration response curve for the [[Adverse drug event| Adverse Drug Events]] (ADE) of interest.<br />
*'''Clinical Implications of the interaction:''' It is suggested that consideration of management burden of the interaction, and the awareness of the provider regarding the interaction.<br />
*'''Patient characteristics:''' Better integration of the medication knowledge base and the EHR of the patient.<br />
*'''Evidence supporting the interaction:''' Inclusion of information from the [[U.S. Food and Drug Administration (FDA)|U.S. Food and Drug Administration]] (FDA) regarding medication and treatment guidelines in abbreviated form would improve assessment of the evidence in order to make appropriate decisions regarding DDI alerts.<br />
<br />
==Comments ==<br />
This article was a very interesting read in regards to understanding how DDI alerts are becoming a problem in the clinical setting. It also interesting to point out that it is not enough to have a [[CDS| Clinical Decision Support]] (CDS) system and EHR to improve patient care. Investing in personalizing CDS criteria prior to implementation would be most beneficial. Assessing a system with the 5 proposed criteria mentioned above will guide the practice in the right direction. Physicians and clinical providers will not be bombarded with alerts and alert fatigue will decrease.<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Criteria_for_assessing_high-priority_drug-drug_interactions_for_clinical_decision_support_in_electronic_health_recordsCriteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records2015-02-25T05:57:53Z<p>Vishaghera: /* Abstract */</p>
<hr />
<div>This is a review of Phansalkar, S., Desai, A., Choksi, A., Yoshida, E., Doole, J., Czochanski, M., Tucker, A. D., Middleton, B., Bell, B., & Bates, D.W. 2013 article, “Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records”.<ref name="Phansalkar, 2013"> Phansalkar, S., Desai, A., Choksi, A., Yoshida, E., Doole, J., Czochanski, M., Tucker, A. D., Middleton, B., Bell, B., & Bates, D.W. (2013). ''Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records''. BMC Med Inform Decis Mak., 13(1), doi: 10.1186/1472-6947-13-65 Retrieved from: http://www.biomedcentral.com/1472-6947/13/65 </ref><br />
<br />
== Abstract ==<br />
The purpose of this article was to shed some more light in regards to the high override rates for [[Drug-drug interaction|drug-drug interaction]] (DDI) alerts in [[EHR|electronic health records]] (EHRs). The authors explained that due to high DDI alert overrides many providers are increasingly ignoring those alerts which could be dangerous when administrating patient care[[Alert Fatigue]]. There is also a lack of uniformity criteria for determining the severity and validity of alerts. Therefore, the authors of this article explore this area of care and identify a set of criteria for assessing DDIs that can be used for [[CDS|clinical decision support]] (CDS) alerts in EHRs.<br />
<br />
==Methods ==<br />
The authors of this article conducted a literature review for the span of 20 years. The dates were from January 1990 to December 2010. They reviewed articles on MEDLINE and EMBASE databases to identify characteristics of high-priority DDI alerts. The method in which the authors sorted through their research was that they looked for the presence of specific keywords and MeSH terms in two categories within the title, abstract and body of every article. Each reviewer extracted criteria from relevant studies on how the evidence on DDIs could be filtered or tailored to identify a high priority DDI. Once the articles were chosen a panel of experts were then used to validate the data gathered from the systematic literature review. To validate their findings a panel of experts were used in which consisted of a wide range of experience within the subject matter. The panel expertise consisted of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers and agencies involved in the regulation of medication use both federal and private state levels.<br />
<br />
==Results and Discussion ==<br />
There were 44 articles that were selected and reviewed in which met the inclusion criteria for assessing characteristics of high-priority DDIs. As a result the panel proposed 5 items that were deemed to be the most important when assessing an interaction. They are the following:<br />
<br />
*'''Severity:''' More research is needed to understand alert variation for assessing the severity of an interaction.<br />
*'''Probability:''' Probability is harder to determine without knowing the patient context. In the case of an ADE it is suggested to include the concentration response curve for the [[Adverse drug event| Adverse Drug Events]] (ADE) of interest.<br />
*'''Clinical Implications of the interaction:''' It is suggested that consideration of management burden of the interaction, and the awareness of the provider regarding the interaction.<br />
*'''Patient characteristics:''' Better integration of the medication knowledge base and the EHR of the patient.<br />
*'''Evidence supporting the interaction:''' Inclusion of information from the [[U.S. Food and Drug Administration (FDA)|U.S. Food and Drug Administration]] (FDA) regarding medication and treatment guidelines in abbreviated form would improve assessment of the evidence in order to make appropriate decisions regarding DDI alerts.<br />
<br />
==Comments ==<br />
This article was a very interesting read in regards to understanding how DDI alerts are becoming a problem in the clinical setting. It also interesting to point out that it is not enough to have a [[CDS| Clinical Decision Support]] (CDS) system and EHR to improve patient care. Investing in personalizing CDS criteria prior to implementation would be most beneficial. Assessing a system with the 5 proposed criteria mentioned above will guide the practice in the right direction. Physicians and clinical providers will not be bombarded with alerts and alert fatigue will decrease.<br />
<br />
= References =<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Effects_of_Computerized_Clinical_Decision_Support_Systems_on_Practitioner_Performance_and_Patient_OutcomesEffects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes2015-02-25T05:48:35Z<p>Vishaghera: /* Conclusion */</p>
<hr />
<div>This article studies the downstream effects of CPOE alerts and how critical it really gets. <ref name="cDDs6"> Garg AX, Adhikari NJ, McDonald H, et al. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review. JAMA.2005;293(10):1223-1238. http://jama.jamanetwork.com/article.aspx?articleid=200503&resultClick=3<br />
</ref><br />
<br />
== Background ==<br />
<br />
This study focuses on reviewing whether [[CDS]] alerts really improve practitioner performance and patient outcomes. <br />
<br />
== Methods ==<br />
<br />
They included English-language randomized and nonrandomized trials with a contemporaneous control group that compared patient care with a CDSS to routine care without a CDSS and evaluated clinical performance (ie, a measure of process of care) or a patient outcome. They stipulated that the CDSS had to provide patient-specific advice that was reviewed by a health care practitioner before any clinical action. Studies were excluded if the system (1) was used solely by medical students, (2) only provided summaries of patient information, (3) provided feedback on groups of patients without individual assessment, (4) only provided computer-aided instruction, or (5) was used for image analysis. Studies assessing CDSS diagnostic performance against a defined gold standard were not included in this review unless clinical use of the diagnostic CDSS was also compared with routine care. Based on these criteria, they reevaluated all studies from their previous reviews for inclusion.<br />
<br />
== Results ==<br />
<br />
Of the 97 controlled trials assessing practitioner performance, the majority (64%) improved diagnosis, preventive care, disease management, drug dosing, or drug prescribing. However, the effects of these systems on patient health remain understudied—and inconsistent when studied. Fifty-two trials assessed patient outcomes, often in a limited capacity without adequate statistical power to detect clinically important differences. Only 7 trials reported improved patient outcomes with the CDSS, and no study reported benefits for major outcomes such as mortality. Surrogate patient outcomes such as blood pressure and glycated hemoglobin were not meaningfully improved in most studies.<br />
<br />
== Conclusion ==<br />
<br />
It showed that CDS alerts do improve practitioner performance however, patient outcomes were still undermined.<br />
See also: [[Patient centered care]]<br />
<br />
== Comments == <br />
<br />
This study, however, is about 10 years old. The field has rapidly grown since then and it would be interesting to see if there are recent studies measuring if CDS alerts affect patient outcomes. <br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Clinical_decision_support_systemsClinical decision support systems2015-02-25T05:34:19Z<p>Vishaghera: /* Results */</p>
<hr />
<div>This is a review of Beeler, P.E., Bates, D.W. & Hug, B.L. (2014). “Clinical decision support systems”. <ref> Beeler, P.E., Bates, D.W. & Hug, B.L. (2014). Clinical decision support systems. Swiss Medical Weekly; 144:w14073. http://www.ncbi.nlm.nih.gov/pubmed/25668157 </ref><br />
<br />
== Background ==<br />
<br />
[[CDS|Clinical Decision Support ]](CDS) has been shown to help reduce the occurrence of deep vein thrombosis (DVT), promote the use of vaccinations and decrease serious and costly medication errors. <br />
Beeler, Bates & Hug, (2014) point out that “CDS may introduce errors, and therefore the term “e-iatrogenesis” has been proposed to address unintended consequences” (p.1). Keeping in mind the benefits and pitfalls associated with CDS, its implementation must be cautiously carried out.<br />
<br />
== Methods ==<br />
<br />
This was a review article so Beeler, Bates & Hug, (2014) looked at such topics as basic duties and types of CDS, the expected consequences of CDS which are “expected to lead to higher patient safety with better treatment quality, less adverse events and reduced costs” (p.2). Another area examined was the potential for CDS to do harm such as do “false positive alerts increase the risk of alert fatigue” (p.3). Finally the authors dealt with the implementation of CDS and likely future research avenues. <br />
<br />
== Results ==<br />
<br />
Through their research Beeler, Bates & Hug, (2014) found numerous positive outcomes associated with CDS such as “higher patient safety with better treatment quality, less adverse events and reduced costs” (p.2). Also of particular interest the authors found in the literature reviewed that when [[CPOE]] is coupled with CDS the frequency of adverse drug events are lowered considerably. Of course there were references in this work to some cons of CDS mostly citing excessive alerts as well as extra duties and time spent without any direct face to face time with their patients. Various strategies have been proposed to reduce user "Alert Fatigue", please see examples such as [[Drug–drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records]] and [[Characteristics and consequences of drug-allergy alert overrides]]<br />
<br />
== Conclusion == <br />
Beeler, Bates & Hug, (2014) puts forth some very strong arguments in support of CDS systems as well as their combination with CPOE. This paper has a very extensive reference section which endows the study with many important points on CDS pros, cons, its implementation and future research. The authors emphasize the importance of the implementation of the CDS as critical to its overall degree of usability and potential to mitigate possible medical mishaps. <br />
<br />
== References ==<br />
<references/><br />
[[Category: CDS]]<br />
[[Category: Usability]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/Clinical_decision_support_systemsClinical decision support systems2015-02-25T05:30:33Z<p>Vishaghera: /* Background */</p>
<hr />
<div>This is a review of Beeler, P.E., Bates, D.W. & Hug, B.L. (2014). “Clinical decision support systems”. <ref> Beeler, P.E., Bates, D.W. & Hug, B.L. (2014). Clinical decision support systems. Swiss Medical Weekly; 144:w14073. http://www.ncbi.nlm.nih.gov/pubmed/25668157 </ref><br />
<br />
== Background ==<br />
<br />
[[CDS|Clinical Decision Support ]](CDS) has been shown to help reduce the occurrence of deep vein thrombosis (DVT), promote the use of vaccinations and decrease serious and costly medication errors. <br />
Beeler, Bates & Hug, (2014) point out that “CDS may introduce errors, and therefore the term “e-iatrogenesis” has been proposed to address unintended consequences” (p.1). Keeping in mind the benefits and pitfalls associated with CDS, its implementation must be cautiously carried out.<br />
<br />
== Methods ==<br />
<br />
This was a review article so Beeler, Bates & Hug, (2014) looked at such topics as basic duties and types of CDS, the expected consequences of CDS which are “expected to lead to higher patient safety with better treatment quality, less adverse events and reduced costs” (p.2). Another area examined was the potential for CDS to do harm such as do “false positive alerts increase the risk of alert fatigue” (p.3). Finally the authors dealt with the implementation of CDS and likely future research avenues. <br />
<br />
== Results ==<br />
<br />
Through their research Beeler, Bates & Hug, (2014) found numerous positive outcomes associated with CDS such as “higher patient safety with better treatment quality, less adverse events and reduced costs” (p.2). Also of particular interest the authors found in the literature reviewed that when [[CPOE]] is coupled with CDS the frequency of adverse drug events are lowered considerably. Of course there were references in this work to some cons of CDS mostly citing excessive alerts as well as extra duties and time spent without any direct face to face time with their patients. Various strategies have been proposed to reduce user "Alert Fatigue", please see one example titled [[Drug–drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records]].<br />
<br />
== Conclusion == <br />
Beeler, Bates & Hug, (2014) puts forth some very strong arguments in support of CDS systems as well as their combination with CPOE. This paper has a very extensive reference section which endows the study with many important points on CDS pros, cons, its implementation and future research. The authors emphasize the importance of the implementation of the CDS as critical to its overall degree of usability and potential to mitigate possible medical mishaps. <br />
<br />
== References ==<br />
<references/><br />
[[Category: CDS]]<br />
[[Category: Usability]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Reliability_of_an_Epilepsy_Treatment_Clinical_Decision_Support_SystemThe Reliability of an Epilepsy Treatment Clinical Decision Support System2015-02-25T04:52:02Z<p>Vishaghera: /* Conclusion */</p>
<hr />
<div>Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. <ref name="Standridge CDS"> Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. http://link.springer.com/article/10.1007%2Fs10916-014-0119-9#</ref><br />
<br />
<br />
=== Introduction===<br />
It is seen that the treatment of epilepsy is a difficult endeavor for physicians because of the heterogeneous nature of the disease, lack of evidence based treatment protocols and physicians’ inadequate knowledge of the latest protocols and technologies. Thus, a Clinical Decision Support system is found to be very useful in supporting physicians and reducing [[Adverse Drug Events| ADEs]] and improving healthcare delivery. The authors have developed such a knowledge based [[ CDS| CDS ]]system for epilepsy treatment and are now testing the reliability of the ET- CDS(a component of the CDS) , as a part of validation testing.<br />
<br />
===Development===<br />
The CDS system framework being tested is named “ Children’s Hospital Resource In Selecting<br />
Therapy Individualized Expert” (CHRISTINE). CHRISTINE provides an environment for developing and implementing different expert systems such as the multi-expert epilepsy treatment CDS (ET-CDS) system, which in turn comprises of two applications: an expert opinion collection system and a patient data system. On entering the patient details, the output provided is a selection of recommended treatment options based on the disease description, clinical evidence and drug drug interaction records.<br />
<br />
===Methods===<br />
Consistency of the recommendations of epilepsy treatment clinical decision support system for five pediatric epilepsy syndrome profiles was tested in three areas including the preferred anti epilepsy drug choice, the top three recommended choices, and the rank order of the three choices. <br />
<br />
===Results===<br />
The CDS system demonstrated a 100 % reliability on 15,000 executions carried out on five common pediatric epilepsy syndromes ( 100% match rates on treatment reports and system generated reports well as a match to the order of recommended treatment interventions).<br />
<br />
===Conclusion===<br />
The ET-CDS system is a reliable CDS system in the treatment of pediatric epilepsy.<br />
<br />
===Discussion===<br />
Reliability testing is the first step in determining the efficiency of CDS systems. The authors aim to carry out further, rigorous validity testing for the same. Also the current study had limitations like restricting treatment options to only three.<br />
<br />
===Comments===<br />
I would be interested in knowing how effective the system was in reducing ADEs.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Reliability_of_an_Epilepsy_Treatment_Clinical_Decision_Support_SystemThe Reliability of an Epilepsy Treatment Clinical Decision Support System2015-02-25T04:51:20Z<p>Vishaghera: /* Results */</p>
<hr />
<div>Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. <ref name="Standridge CDS"> Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. http://link.springer.com/article/10.1007%2Fs10916-014-0119-9#</ref><br />
<br />
<br />
=== Introduction===<br />
It is seen that the treatment of epilepsy is a difficult endeavor for physicians because of the heterogeneous nature of the disease, lack of evidence based treatment protocols and physicians’ inadequate knowledge of the latest protocols and technologies. Thus, a Clinical Decision Support system is found to be very useful in supporting physicians and reducing [[Adverse Drug Events| ADEs]] and improving healthcare delivery. The authors have developed such a knowledge based [[ CDS| CDS ]]system for epilepsy treatment and are now testing the reliability of the ET- CDS(a component of the CDS) , as a part of validation testing.<br />
<br />
===Development===<br />
The CDS system framework being tested is named “ Children’s Hospital Resource In Selecting<br />
Therapy Individualized Expert” (CHRISTINE). CHRISTINE provides an environment for developing and implementing different expert systems such as the multi-expert epilepsy treatment CDS (ET-CDS) system, which in turn comprises of two applications: an expert opinion collection system and a patient data system. On entering the patient details, the output provided is a selection of recommended treatment options based on the disease description, clinical evidence and drug drug interaction records.<br />
<br />
===Methods===<br />
Consistency of the recommendations of epilepsy treatment clinical decision support system for five pediatric epilepsy syndrome profiles was tested in three areas including the preferred anti epilepsy drug choice, the top three recommended choices, and the rank order of the three choices. <br />
<br />
===Results===<br />
The CDS system demonstrated a 100 % reliability on 15,000 executions carried out on five common pediatric epilepsy syndromes ( 100% match rates on treatment reports and system generated reports well as a match to the order of recommended treatment interventions).<br />
<br />
===Conclusion===<br />
The ET-CDS system is a reliable CTS in the treatment of pediatric epilepsy.<br />
<br />
===Discussion===<br />
Reliability testing is the first step in determining the efficiency of CDS systems. The authors aim to carry out further, rigorous validity testing for the same. Also the current study had limitations like restricting treatment options to only three.<br />
<br />
===Comments===<br />
I would be interested in knowing how effective the system was in reducing ADEs.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]</div>Vishagherahttp://www.clinfowiki.org/wiki/index.php/The_Reliability_of_an_Epilepsy_Treatment_Clinical_Decision_Support_SystemThe Reliability of an Epilepsy Treatment Clinical Decision Support System2015-02-25T04:50:20Z<p>Vishaghera: Created page with "Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. <ref name..."</p>
<hr />
<div>Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. <ref name="Standridge CDS"> Standridge S, Faist R, Pestian J, Glauser T, Ittenbach R. The Reliability of an Epilepsy Treatment Clinical Decision Support System. J Med Syst. 2014 Oct;38(10):119. http://link.springer.com/article/10.1007%2Fs10916-014-0119-9#</ref><br />
<br />
<br />
=== Introduction===<br />
It is seen that the treatment of epilepsy is a difficult endeavor for physicians because of the heterogeneous nature of the disease, lack of evidence based treatment protocols and physicians’ inadequate knowledge of the latest protocols and technologies. Thus, a Clinical Decision Support system is found to be very useful in supporting physicians and reducing [[Adverse Drug Events| ADEs]] and improving healthcare delivery. The authors have developed such a knowledge based [[ CDS| CDS ]]system for epilepsy treatment and are now testing the reliability of the ET- CDS(a component of the CDS) , as a part of validation testing.<br />
<br />
===Development===<br />
The CDS system framework being tested is named “ Children’s Hospital Resource In Selecting<br />
Therapy Individualized Expert” (CHRISTINE). CHRISTINE provides an environment for developing and implementing different expert systems such as the multi-expert epilepsy treatment CDS (ET-CDS) system, which in turn comprises of two applications: an expert opinion collection system and a patient data system. On entering the patient details, the output provided is a selection of recommended treatment options based on the disease description, clinical evidence and drug drug interaction records.<br />
<br />
===Methods===<br />
Consistency of the recommendations of epilepsy treatment clinical decision support system for five pediatric epilepsy syndrome profiles was tested in three areas including the preferred anti epilepsy drug choice, the top three recommended choices, and the rank order of the three choices. <br />
<br />
===Results===<br />
The CDS system demonstrated a 100 % reliability on 15,000 executions carries out on five common pediatric epilepsy syndromes ( 100% match rates on treatment reports and system generated reports well as a match to the order of recommended treatment interventions).<br />
<br />
===Conclusion===<br />
The ET-CDS system is a reliable CTS in the treatment of pediatric epilepsy.<br />
<br />
===Discussion===<br />
Reliability testing is the first step in determining the efficiency of CDS systems. The authors aim to carry out further, rigorous validity testing for the same. Also the current study had limitations like restricting treatment options to only three.<br />
<br />
===Comments===<br />
I would be interested in knowing how effective the system was in reducing ADEs.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]</div>Vishaghera