Journal
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Volume 77, Issue 10, Pages 641-649Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2008.01.004
Keywords
decision support systems, clinical; decision making, computer-assisted; decision support techniques; hospital information systems; medical records systems, computerized; models, theoretical
Categories
Funding
- NLM Training Grant [1T15LM009461]
- NLM Research Grant [R56-LM006942-07A1]
- NATIONAL LIBRARY OF MEDICINE [R56LM006942, T15LM007088] Funding Source: NIH RePORTER
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Background: A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems. Purpose: To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems. Methods: The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model. Results: The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems. Conclusions: Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: (1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, (2) there are serious terminological issues, (3) patient data may be spread across several sources with no single source having a complete view of the patient, and (4) major difficulties exist in transferring successful interventions from one site to another. (c) 2008 Elsevier Ireland Ltd. All rights reserved.
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