Effective Preventive Care Management of Multiple Chronic Conditions

Abstract

Clinical practice guidelines do not sufficiently address the needs of patients with multiple chronic conditions (MCC) as these guidelines focus on single disease management and ignore unique patient-specific conditions. As a result, a non-personalized approach for the management of the patients with MCC leads to adverse events and increases the financial burden on the health care system as over 150 million Americans experience MCC. To this end, we develop a stochastic modeling framework to personalize the management of MCC and provide an exact solution algorithm. We consider the optimal management of preventive care for an index disease (e.g., breast cancer, colorectal cancer, HIV, etc.) while accounting for the existence of a chronic condition (e.g., hypertension, diabetes, Alzheimer's disease, etc.) Our modeling framework is particularly useful for the cases where the chronic condition affects the risk of the index disease. In a case study using real breast cancer epidemiology data, we demonstrate how our modeling framework can be used to personalize breast cancer screening for women with type 2 diabetes. In addition to providing a personalized breast cancer screening schedule for diabetic women, we find some important policy insights that were not previously recognized by the medical community. More specifically, we find that compared to non-diabetic women, diabetic women should be screened less aggressively but screening should end at similar ages. We also find that adherence to the optimal screening policy is more crucial for diabetic women compared to non-diabetic women. Our main insight on screening recommendations also has important resource implications as it leads to fewer screening mammograms. That is, compared to the current national breast cancer screening guidelines, the optimal breast cancer screening policy for diabetic women could save the health care system approximately 2.6 million mammograms annually which translates to $405 million of annual cost savings.