Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model

During the process of disease diagnosis, overdiagnosis can lead to potential health loss and unnecessary anxiety for patients as well as increased medical costs, while underdiagnosis can result in patients not being treated on time. To deal with these problems, we construct a partially observable Markov decision process (POMDP) model of chronic diseases to study optimal diagnostic policies, which takes into account individual characteristics of patients. The objective of our model is to maximize a patient’s total expected quality-adjusted life years (QALYs). We also derive some structural properties, including the existence of the diagnostic threshold and the optimal diagnosis age for chronic diseases. The resulting optimization is applied to the management of coronary heart disease (CHD). Based on clinical data, we validate our model, demonstrate how the quantitative tool can provide actionable insights for physicians and decision makers in health-related fields, and compare optimal policies with actual clinical decisions. The results indicate that the diagnostic threshold first decreases and then increases as the patient’s age increases, which contradicts the intuitive non-decreasing thresholds. Moreover, diagnostic thresholds were higher for women than for men, especially at younger ages.