Intelligent decision support for medication review

Authors:
Ivan Bindoff;Peter Tenni;Byeong Ho Kang;Gregory Peterson
Affiliations:
School of Computing, University of Tasmania;Unit for Medical Outcomes and Research Evaluations, University of Tasmania;School of Computing, University of Tasmania;Unit for Medical Outcomes and Research Evaluations, University of Tasmania
Venue:
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Year:
2006

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Abstract

This paper examines an implementation of a Multiple Classification Ripple Down Rules system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 84 genuine cases by an expert in the field; over the course of 15 hours the system had learned 197 rules and was considered to encompass around 60% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically.