Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system.
Clinical decision support is essential for achieving the maximum value from electronic medical records. Content for decision support systems is usually developed manually, and developing good content is difficult. In this paper, we propose an alternate method to develop decision support content automatically through data mining of past ordering behaviors. We present two data mining methods from computer science: frequent itemset mining, which we use to learn order sets, and association rule mining, which we use to learn corollary orders. We successfully applied these techniques to a database of 156,756 orders from an ambulatory computerized physician order entry system. This analysis yielded a large pool of clinically relevant decision support content. Compared to manual development, these methods are more efficient, account more fully for local preferences and practice variations, and yield content more readily integrated into clinical systems.
Wright
Adam
A
Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Sittig
Dean F
DF
D000555:Ambulatory Care Information Systems; D020000:Decision Support Systems, Clinical; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D050316:Medical Order Entry Systems / Q000592:standards; D016347:Medical Records Systems, Computerized