A constraint satisfaction approach to data-driven implementation of clinical practice guidelines.
Despite significant research efforts, the implementation of computerized clinical practice guidelines (CPG) in practice remains problematic for a number of reasons. In particular most guideline representation models do not deal adequately with incomplete or inconsistent clinical data. We present a constraint satisfaction approach to address such shortcomings by focusing on CPG data rather than CPG representation. We model a CPG as a set of data-driven constraints which are used to generate complete solutions for describing a patient state from incomplete clinical data, where the patient state is confirmed by the user. Inconsistent input data can be temporarily eliminated and final feasible solutions (permitted complete solutions from a CPG) can pinpoint inconsistencies in original input data alongside allowable guideline data. We demonstrate a sample implementation of the approach for a pediatric asthma CPG.
Kuziemsky
Craig
C
Telfer School of Management, University of Ottawa, Ottawa, Canada.
O'Sullivan
Dympna
D
Michalowski
Wojtek
W
Wilk
Szymon
S
Farion
Ken
K
D000465:Algorithms; D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D020000:Decision Support Systems, Clinical; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D017410:Practice Guidelines as Topic; D013358:Subject Headings; D014481:United States