Difference between revisions of "CDS"
(→Medication-related Clinical Decision Support) |
(→CDS Implementation) |
||
| Line 109: | Line 109: | ||
== CDS Implementation == | == 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]. | ||
Benefits and roles of CDS | Benefits and roles of CDS | ||
| Line 118: | Line 119: | ||
* Promote preventive screening and use of evidence based recommendations. | * Promote preventive screening and use of evidence based recommendations. | ||
* Cost reduction and increased patient convenience. | * Cost reduction and increased patient convenience. | ||
| + | |||
The overall results indicate the potential of CDS to improve the quality of care. | 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". | 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". | ||
| Line 128: | Line 130: | ||
* Prioritize and filter recommendations to the user. | * Prioritize and filter recommendations to the user. | ||
* Create an architecture for sharing executable CDS modules and services. | * Create an architecture for sharing executable CDS modules and services. | ||
| − | * | + | * Combine recommendations for patients with co* morbidities. |
* Create internet* accessible clinical decision support repositories. | * Create internet* accessible clinical decision support repositories. | ||
* Use free text information to drive clinical decision support. | * Use free text information to drive clinical decision support. | ||
* Mine large clinical database to create new CDS. | * 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]. | Those are important points that are critical for achievement of the potential of CDS and improve the quality, safety, and efficiency of healthcare[2]. | ||
Success criteria estimates | Success criteria estimates | ||
| − | To estimate the success of the system we should look at the following points[3] : | + | 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. | ||
| − | References | + | === References === |
# Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009 | # 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 | # 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 | # 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 | ||
| − | |||
== References == | == References == | ||
Revision as of 03:35, 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
- 1 Clinical Decision Support overview
- 2 Overview
- 2.1 Interaction models for clinical decision support
- 2.2 Order set
- 2.3 Order set
- 2.4 Information Resources
- 2.5 Artificial intelligence
- 2.6 Business Intelligence and Data Warehousing
- 2.7 Medication-related Clinical Decision Support
- 2.8 Non-Medication-Based Safety Rules
- 2.9 Validation and Verification of Clinical Decision Support
- 2.10 Sample Decision Support Content
- 3 CDS Implementation
- 4 References
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
Overview
For an overview of the process that healthcare organizations can use to begin, or improve, a clinical decision support (CDS) initiative interested parties can follow the guidelines described in Improving Outcomes with Clinical Decision Suppport: An Implementer's Guide to measurably improve key healthcare outcomes such as the quality, safety, and cost-effectiveness of care delivery.
Interaction models for clinical decision support
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. [1][2] [3]
Benefits
There are many reported benefits of order sets. Order sets represent a potential solution to the time constraints of busy physicians and may even improve quality and safety. Obstacles to overcome would include physician acceptance, costs of creation and maintenance, and user interface issues. [4]
- Reduction of transcription errors.
- Promotion of adherence to consistent standards of care
- Focus attention upon unique features of a patient.
- Quicker order entry
- Reduction in delays due to inconsistent or incomplete orders
Personal order sets
Individuals may create their own personal order sets by a variety of methods, and some are even available on the internet. In addition, some institutions have developed modifiable templates that allow physicians to customize their own order sets. [5] Mostly, however, order sets are developed by a group of physicians or a department with a particular clinical focus. They then come up with a set of diagnostic and treatment options that encompass current best practices. This latter approach results in a more limited number of order sets, and is easier to manage. [6] [7]
Order set
- Personal order set
- Functional specifications
- Criteria for creating new order sets
- A Process for Creating and Maintaining Order Sets
- Most commonly used Order Sets in In-patient Setting
Information Resources
- [The HIMSS Clinical Decision Support (CDS) Task Force wiki]
- Alerts and Reminders
- 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
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
Basic Medication-Related Decision Support
- Adverse drug reactions
- Basic Dosing Guidance for medications in CPOE
- Formulary decision support
- Duplicate Therapy Checking
Advanced Medication-Related Decision Support
- Advanced Dosing Guidance in CPOE
- Patient Characteristic dosing support
- Advanced Checking of Drug-Disease Interactions and Contraindications
- 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
Adverse Drug Events
Non-Medication-Based Safety Rules
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") [8]
- 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].
Benefits and roles of CDS
- 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".
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].
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.
References
- 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
References
- Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [9]
- Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [10]
- 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. [11]
- Southern Ohio Medical Center, [12]
