Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
This addresses the challenge of implementing clinical guidelines for chronic disease management, specifically for heart failure, by providing targeted advice to physicians, though it appears incremental as it builds on existing answer set programming methods.
The paper tackles the problem of slow adoption of complex heart failure management guidelines by developing a physician advisory system that uses abductive reasoning to identify missing symptoms and conditions needed for a treatment to be effective, thereby improving adherence.
Management of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing HF using answer set programming(ASP). In this paper we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.