Authors:   Rafael Diaz, Mandar Tulpule, Joshua G. Behr

Abstract

Medical treatment for chronic conditions forms a major portion of the US healthcare expenditure. Chronic diseases are generally associated with ailments without any permanent cure which significantly affect the health status, lifestyle, mobility and longevity of patients. A variety of chronic disease management interventions have been deployed to help patients better manage their medical condition. The main purpose of such interventions is to improve their health condition while achieving cost savings through a reduced healthcare utilization rate. While these interventions are desirable from the point of view of relevant clinical outcomes, the monetary outcomes in terms of costs and savings are uncertain. Further, most studies rely on short term savings and do not consider future healthcare costs. This study presents a system dynamics model representing the key cost factors involved in implementing a disease management intervention, and the dynamics associated with those factors. A simple goal seeking structure is embedded in the model as a simulation based optimization routine. The functionality of the model is demonstrated by means of hypothetical scenarios implemented via sensitivity analysis. The model provides useful insights into how the initial estimates of the cost of intervention and the resulting savings would change depending on the uncertainties, feedbacks and the targeted savings in the system. The model is designed to be used as a learning and decision support tool for implementing chronic disease management interventions.