Improving access to community-based chronic care through improved capacity allocation

This paper studies a model of community-based health care delivery for chronic diseases in a non-profit setting. In this setting, patients periodically access the health care delivery system, which influences their disease progression and consequently their health outcomes. We investigate how the provider can maximize community-level health outcomes through better operational decisions pertaining to capacity allocation across different patients. To do so, we develop an integrated capacity allocation model that incorporates clinical (disease progression) and operational (capacity constraint) aspects. Specifically, we model the provider’s problem as a finite horizon stochastic dynamic program, where the provider decides which patients to schedule at the beginning of each period. Therapy is provided to scheduled patients, which may improve their health states. Patients that are not seen follow their natural disease progression. We derive a quantitative measure for comparison of patients’ health states and use it to design an easy-to-implement myopic heuristic that is provably optimal in a simple setting of the problem. We employ the myopic heuristic in a more general setting and test its performance using operational and clinical data obtained from Mobile C.A.R.E. Foundation, a community-based provider of pediatric asthma care in Chicago. We find that the myopic heuristic provides a performance that is very close to that of the optimal policy with an average optimality gap of 0.4%. Furthermore, the myopic heuristic can improve the health gains at the community-level by up to 15% over the current policy with the potential benefit being greater when capacity is tighter. The benefit is driven by the integrated nature of our myopic heuristic that ranks patients based on our quantitative measure of relative health states and dynamically alters the duration between visits for patients with different states depending on the tightness of capacity and health state of the entire patient population.