Difference between revisions of "CDS"
Line 1: | Line 1: | ||
'''Clinical decision support (CDS)''' refers broadly to providing clinicians or patients with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Clinical knowledge of interest could range from simple facts and relationships to best practices for managing patients with specific disease states, new medical knowledge from clinical research and other types of information. | '''Clinical decision support (CDS)''' refers broadly to providing clinicians or patients with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Clinical knowledge of interest could range from simple facts and relationships to best practices for managing patients with specific disease states, new medical knowledge from clinical research and other types of information. | ||
− | == | + | == CDS components == |
− | + | There are several key components of a good clinical decision support system. [http://www.himss.org/ASP/topics_cds_workbook.asp?faid=108&tid=14] | |
− | = | + | === Order set === |
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | == Order set == | + | |
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. One of the main impetuses for order sets comes from the need to improve user acceptance of [[CPOE|computer-based physician order entry]], by decreasing the time physicians require to enter orders. Using order sets reduces both time spent entering orders and terminal usage. [http://www.ncbi.nlm.nih.gov/pubmed/9929233][http://www.ncbi.nlm.nih.gov/pubmed/7719793] [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245247/] | 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. One of the main impetuses for order sets comes from the need to improve user acceptance of [[CPOE|computer-based physician order entry]], by decreasing the time physicians require to enter orders. Using order sets reduces both time spent entering orders and terminal usage. [http://www.ncbi.nlm.nih.gov/pubmed/9929233][http://www.ncbi.nlm.nih.gov/pubmed/7719793] [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245247/] | ||
− | === | + | === Medication decision support === |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
*[[Adverse drug event|Adverse drug reactions]] | *[[Adverse drug event|Adverse drug reactions]] | ||
* Basic Dosing Guidance for medications in CPOE | * Basic Dosing Guidance for medications in CPOE | ||
* [[Formulary decision support]] | * [[Formulary decision support]] | ||
* Duplicate Therapy Checking | * Duplicate Therapy Checking | ||
− | |||
− | |||
− | |||
* Advanced Dosing Guidance in CPOE | * Advanced Dosing Guidance in CPOE | ||
* [[Patient Characteristic dosing support]] | * [[Patient Characteristic dosing support]] | ||
− | |||
− | |||
− | |||
* Medications to be avoided in the elderly | * Medications to be avoided in the elderly | ||
* Medications requiring dosage adjustments in renal insufficiency | * Medications requiring dosage adjustments in renal insufficiency | ||
Line 63: | Line 24: | ||
*[[Vaccination contraindications]] | *[[Vaccination contraindications]] | ||
*[[Common Corollary orders]] | *[[Common Corollary orders]] | ||
− | |||
− | |||
*[[Detection of Adverse Mediation-Related Events]] | *[[Detection of Adverse Mediation-Related Events]] | ||
− | ===Non- | + | === Non-medication safety rules === |
+ | |||
* [[Diagnosis-Order Rules]] | * [[Diagnosis-Order Rules]] | ||
+ | |||
+ | == CDS benefits == | ||
+ | |||
+ | * They can alter clinical decision making and actions towards better practices.* | ||
+ | * Reduce the medication errors. | ||
+ | * Promote preventive screening and use of evidence based recommendations. | ||
+ | * Cost reduction and increased patient convenience. | ||
+ | |||
+ | The overall 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. We should all remember that simple human processes and innovations provide large opportunities for improvement, especially when thoughtfully harmonized with robust technological solutions; so always "Do CDS with users not to them". | ||
+ | |||
+ | ==[[Interaction models for clinical decision support]]== | ||
+ | |||
+ | *[[Interpretation]] | ||
+ | *[[Consultation]] | ||
+ | *[[Monitoring]] | ||
+ | *[[Critiquing]] | ||
+ | *[[Teaching]] | ||
+ | *[[Computer-interpretable guidelines]] | ||
+ | |||
+ | ===Artificial intelligence=== | ||
+ | |||
+ | [[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. | ||
+ | |||
+ | ===Business Intelligence and Data Warehousing=== | ||
+ | |||
+ | *[[Business Intelligence & Data Warehousing for Healthcare]] | ||
+ | *[[Clinical Data Warehousing]] | ||
===Validation and Verification of Clinical Decision Support=== | ===Validation and Verification of Clinical Decision Support=== | ||
Line 74: | Line 62: | ||
===Sample Decision Support Content=== | ===Sample Decision Support Content=== | ||
+ | |||
* [[Diabetes CDS Content]] | * [[Diabetes CDS Content]] | ||
* [[Drug-Drug Interaction Rules]] | * [[Drug-Drug Interaction Rules]] | ||
Line 87: | Line 76: | ||
At the stage of planning for any new health IT system, there are some considerations and steps that should be followed to guarantee the system success; such as identifying the needs and functional requirements, deciding whether to purchase a commercial system or build the system, planning for encouraging physicians to use CDS, designing a system to evaluate how well the system has addressed the identified needs[1]. | At the stage of planning for any new health IT system, there are some considerations and steps that should be followed to guarantee the system success; such as identifying the needs and functional requirements, deciding whether to purchase a commercial system or build the system, planning for encouraging physicians to use CDS, designing a system to evaluate how well the system has addressed the identified needs[1]. | ||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
== Challenges and considerations== | == Challenges and considerations== | ||
Line 130: | Line 109: | ||
# Organizational impact. | # Organizational impact. | ||
− | === | + | ===[[Information Resources]]=== |
− | + | *[[http://himssclinicaldecisionsupportwiki.pbworks.com/ The HIMSS Clinical Decision Support (CDS) Task Force wiki]] | |
− | + | *[[Alert fatigue]] | |
− | + | *[[Alert placement in clinical workflow]] | |
+ | *[[Initial Selection of What to Alert on...]] | ||
+ | *[[Alerts versus on-demand CDS]] | ||
+ | *[[Sources of clinical decision support content]] | ||
== References == | == References == | ||
Line 142: | Line 124: | ||
# 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/] | # 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/] | ||
# Southern Ohio Medical Center, [http://www.somc.org/for_doctors/orders/bonzo/] | # Southern Ohio Medical Center, [http://www.somc.org/for_doctors/orders/bonzo/] | ||
+ | # Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009 | ||
+ | # Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392 | ||
+ | # 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 |
Revision as of 16:45, 21 November 2011
Clinical decision support (CDS) refers broadly to providing clinicians or patients with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Clinical knowledge of interest could range from simple facts and relationships to best practices for managing patients with specific disease states, new medical knowledge from clinical research and other types of information.
Contents
CDS components
There are several key components of a good clinical decision support system. [1]
Order set
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. One of the main impetuses for order sets comes from the need to improve user acceptance of computer-based physician order entry, by decreasing the time physicians require to enter orders. Using order sets reduces both time spent entering orders and terminal usage. [2][3] [4]
Medication decision support
- Adverse drug reactions
- Basic Dosing Guidance for medications in CPOE
- Formulary decision support
- Duplicate Therapy Checking
- Advanced Dosing Guidance in CPOE
- Patient Characteristic dosing support
- Medications to be avoided in the elderly
- Medications requiring dosage adjustments in renal insufficiency
- Medications requiring dosage adjustments in hepatic disease
- Medications to be avoided during pregnancy
- Medications to be avoided while breastfeeding
- Vaccination contraindications
- Common Corollary orders
- Detection of Adverse Mediation-Related Events
Non-medication safety rules
CDS benefits
- They can alter clinical decision making and actions towards better practices.*
- Reduce the medication errors.
- Promote preventive screening and use of evidence based recommendations.
- Cost reduction and increased patient convenience.
The overall 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. We should all remember that simple human processes and innovations provide large opportunities for improvement, especially when thoughtfully harmonized with robust technological solutions; so always "Do CDS with users not to them".
Interaction models for clinical decision support
Artificial intelligence
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.
Business Intelligence and Data Warehousing
Validation and Verification of Clinical Decision Support
Sample Decision Support Content
- Diabetes CDS Content
- Drug-Drug Interaction Rules
- Clinical Reminders from Beth Israel/Deaconess Medical Center in Boston
- Symptom Triage Decision Support for Consumers (example: "Chest Pain") [5]
- Weight-based Heparin Dosing Guidelines
- Flowchart-based decision support sample content
- Preventive care reminders
CDS Implementation
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.
At the stage of planning for any new health IT system, there are some considerations and steps that should be followed to guarantee the system success; such as identifying the needs and functional requirements, deciding whether to purchase a commercial system or build the system, planning for encouraging physicians to use CDS, designing a system to evaluate how well the system has addressed the identified needs[1].
Challenges and considerations
- Improve the human* computer interface.
- Disseminate best practices in CDS design, development, and implementation.
- Summarize patient* level information.
- Prioritize and filter recommendations to the user.
- Create an architecture for sharing executable CDS modules and services.
- Combine recommendations for patients with co* morbidities.
- Create internet* accessible clinical decision support repositories.
- Use free text information to drive clinical decision support.
- Mine large clinical database to create new CDS.
Those are important points that are critical for achievement of the potential of CDS and improve the quality, safety, and efficiency of healthcare[2].
Clinical Decision Support overview
- National Roadmap for Clinical Decision Support
- History of decision support
- General system features associated with improvements in clinical practice
- Support Decisions with Diagnostic Aids
- Clinical Decision Support Liability
Success criteria estimates
To estimate the success of the system we should look at the following points[3]:
- System quality.
- Information quality
- Usage
- User satisfaction
- Individual impact
- Organizational impact.
Information Resources
- [The HIMSS Clinical Decision Support (CDS) Task Force wiki]
- Alert fatigue
- Alert placement in clinical workflow
- Initial Selection of What to Alert on...
- Alerts versus on-demand CDS
- Sources of clinical decision support content
References
- Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [6]
- Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [7]
- 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. [8]
- Southern Ohio Medical Center, [9]
- Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009
- Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392
- 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