Abstract
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.
Highlights
Chronic diseases remain one of the major public health problems in the world, imposing a high cost burden on healthcare systems and reducing the quality of life of patients [1,2,3]
Because we focused on primary diagnosis, we removed unrelated records, which included 399 patients who were in states of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), and 59 patients with acute myocardial infarction
We constructed a partially observable Markov decision process (POMDP) model for chronic diseases to optimize diagnostic policies incorporating individual characteristics of patients and derived some structural properties, including the existence of the diagnostic threshold and the optimal diagnosis age
Summary
Chronic diseases remain one of the major public health problems in the world, imposing a high cost burden on healthcare systems and reducing the quality of life of patients [1,2,3]. There are many advanced medical examinations for screening and diagnosing chronic diseases, including non-invasive and invasive tests, which makes it possible to make personalized diagnostic policies based on individual characteristics to detect chronic diseases early [7]. When a patient undergoes a non-invasive test for basic screening, a physician will decide whether he needs further invasive tests based on the screening results, such as coronary angiography (CAG), prostate biopsy, or breast biopsy. Such policies for invasive tests made following basic screening are called diagnostic policies [8]
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