A Description and Functional Taxonomy of Rule-based Decision Support Content at a Large Integrated Delivery Network
- Affiliations of the authors: Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (AW), Portland, OR; Clinical Informatics Research and Development, Partners Healthcare System (AW, HG, TH, BM); Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School (BM), Boston, MA
- Correspondence: Adam Wright, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, 3181 Sam Jackson Park Road, Portland, OR 97239; e-mail: <wrightad{at}ohsu.edu>. Reprints: Blackford Middleton, Clinical Informatics Research and Development, Partners Healthcare, 93 Worcester Street, Wellesley, MA 02418;e-mail: <bmiddleton1{at}partners.org>
- Received 2 January 2007
- Accepted 10 April 2007
Abstract
Objective This study sought 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 laboratory result were the most common triggers; laboratory 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.
