A four-phase model of the evolution of clinical decision support architectures.

Adam Wright, Dean F Sittig

Clinical Informatics Research and Development, Partners HealthCare, Boston, MA, United States; Division of General Medicine, Brigham & Women's Hospital, Boston, MA, United States; Department of Medicine, Harvard Medical School, Boston, MA, United States.

Journal Article: International Journal of Medical Informatics (impact factor: 3.13). 04/2008; DOI: 10.1016/j.ijmedinf.2008.01.004

Abstract

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.

Source: PubMed

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Keywords

chronological history
 
clinical decision support
 
clinical decision support literature
 
clinical decision support systems
 
clinical systems
 
clinical workflows
 
complete view
 
computer systems
 
decision support architecture
 
extensive review
 
four evolutionary approaches
 
integrating clinical decision support systems
 
integrating decision support systems
 
knowledge representation systems
 
relative difficulty
 
service models
 
sharing clinical decision support content
 
single source
 
standalone decision support systems
 
successful interventions