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First published April 25, 2007 as JAMIA PrePrint; doi:10.1197/jamia.M2364
Journal of the American Medical Informatics Association 2007;14(4):489-496
© 2007 American Medical Informatics Association

A more recent version of this article appeared on July 1, 2007
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Submitted on January 2, 2007
Accepted on April 10, 2007

A Description and Functional Taxonomy of Rule-Based Decision Support Content at a Large Integrated Delivery Network

Adam Wright1*, Howard Goldberg MD2, Tonya Hongsermeier MD2, and Blackford Middleton MD, MPH, MSc3

Affiliation of the authors: 1 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR; Clinical Informatics Research and Development, Partners HealthCare System, Wellesley, MA ; 2 Clinical Informatics Research and Development, Partners HealthCare System, Wellesley, MA ; 3 Clinical Informatics Research and Development, Partners HealthCare System, Wellesley, MA; Department of General Internal Medicine and Primary Care, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA

* To whom correspondence should be addressed.

Objective To develop a functional taxonomy of rule-based clinical decision support.

Design The rule-based clinical decision support content of a large integrated delivery network with a long history of computer-based, point-of-care decision support was reviewed and analyzed along four functional dimensions: trigger, input data elements, interventions and offered choices.

Results A total of 181 rule types were reviewed, comprising 7,120 different instances of rule usage. A total of 42 taxa were identified across the four categories. Many rules fell into multiple taxa in a given category. Entered order and stored lab result were the most common triggers; lab result, drug list and hospital unit were the most frequent data elements used. Notify and log were the most common interventions, and write order, defer warning and override rule were the most common offered choices.

Conclusion A relatively small number of taxa successfully described a large body of clinical knowledge. These taxa can be directly mapped to functions of clinical systems and decision support systems, providing feature guidance for developers, implementers and certifiers of clinical information systems.






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