Spring 2014 Seminar Series

DATE: 4/24/2014
LOCATION: E62-276
TIME: 4:15pm
Reception immediately following

SPEAKER:
Karen Smilowitz

TITLE
Improving Access to Community-based Chronic Care Through Allocation and Scheduling Models

ABSTRACT
This talk will present capacity allocation and appointment scheduling models for community-based health care delivery for a chronic disease. In this setting, patients periodically visit the health care delivery system, which influences their disease progression and consequently their health outcomes. We investigate how the provider can improve access to care, thus improving community-level health outcomes, through better operational decisions pertaining to capacity allocation and appointment scheduling 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 special cases 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. Our extensive computational experiments suggest that the myopic heuristic can improve the health gains at the community-level by up to 15% over the current policy. The benefit is driven by the ability of our myopic heuristic to alter the duration between visits for patients with different health states depending on the tightness of the capacity and the health states of the entire patient population. If time permits, appointment scheduling models will be presented as well.