Difference between revisions of "CDS"

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== History of decision support ==
 
== History of decision support ==
  
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This is a timeline of the early history of clinical decision support, from 1959 through 1993.
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''Main article: [[History of clinical decision support]]''
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* 1959: [http://pir.georgetown.edu/nbrf/rslbio.html Ledley] and Lusted propose a mathematical model for diagnosis in their article "Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason", published in Science. This article has been called the first work in medical informatics.
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* 1961: Homer Warner, of the University of Utah, develops a mathematical model for diagnosing congenital heart disease. Their approach uses a contingency table with diagnoses as rows and symptoms as columns. The system predicts the diagnosis with the highest conditional probability given a set of symptoms.
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* 1964: [http://ckp.kp.org/kpindepth/archive/indepth_road.html Morris Collen] of Kaiser develops a system for automated multiphasic diagnosis. Patients were given a stack of computer punch cards containing symptoms and questions, and they sorted them into Yes and No piles. A computer used these cards to develop a preliminary diagnosis.
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* 1969: Howard Bleich develops a system to suggest therapy for acid-base disorders. It is the first decision support system which proposes a management plan in addition to a diagnosis.
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* 1972: F.T. de Dombal builds a probabilistic model to diagnose abdominal complaints. The computer predicted the correct surgical diagnosis based on initial findings 91.8% of the time. On average, senior clinician predicted the diagnosis correctly 79.6% of the time.
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* 1972: The [[Health Evaluation through Logical Programming (HELP)]] system forms the basis of many research projects in clinical decision support, including the COMPAS ventilator management system by Dean Sittig and an antibiotic advisor by Scott Evans.
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* 1975: Ted Shortliffe of Stanford develops MYCIN, an expert system for antibiotic dosing. MYCIN consisted of three modules: a consultation system which collects information, applies the rules in its knowledge base, and recommends therapy; an explainer system, which explains these recommendations; and a rule acquisition syhstem, which an expert uses to build rules for the knowledge base. In early evaluation, Mycin suggested acceptable therapy 75% of the time, but it got better as more rules were added.
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* 1976: Clem McDonald publishes "Protocol-based computer reminders, the quality of care and the non-perfectability of man", which looks at the clinical decision support suspten in the [[Regenstrief Medical Record System (RMRS)|Regenstrief Medical Record System]]. In an experimental trial, McDonald found that physicians responded to 51% of the alerts they received, but provided the corresponding care only 21% of the time when alerts were not provided.
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* 1981: INTERNIST-I is developed by Randy Miller, Harry Pople and Jack Meyers. INTERNEST, a diagnostic decision support system, was the first decision support system to span all of internal medicine. Users would enter the findings for a case into INTERNIST, and the system would develop a differential diagnosis, and ask questions to improve this diffential. The system contained 500 disease profiles, and 3,550 clinical manifestations.
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* 1983: Perry Miller develops the ATTENDING system for anesthesia management. ATTENDING is the first medical [[Critiquing|critiquing system]].
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* 1987: Octo Barnett develps DXPlain, a diagnostic decision support system similar to INTERNIST. A web version of [http://www.lcs.mgh.harvard.edu/projects/dxplain.html DXPlain] is still available today.
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* 1993: Brigham and Women's Hospital releases [[Brigham Integrated Computing System (BICS)]], developed by Jonathan Teich.
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[[category:blogposium]]
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== References ==
 
== References ==

Revision as of 16:26, 6 December 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.

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

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

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

Success criteria estimates

To estimate the success of the system we should look at the following points[3]:

  1. System quality.
  2. Information quality
  3. Usage
  4. User satisfaction
  5. Individual impact
  6. Organizational impact.

Information Resources

History of decision support

Main article: History of clinical decision support

References

  1. Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [6]
  2. Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [7]
  3. 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]
  4. Southern Ohio Medical Center, [9]
  5. Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009
  6. Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392
  7. 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
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