Collaborative Research: An Allocation Model with Dynamic Updates for Balanced Workload Distribution on Patient Centered Medical Homes

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The goal of this collaborative Advancing Health Services through System Modeling Research project is to lay a scientific foundation for new business rules and operation sciences useful for the patient centered medical home model (PCMH). The PCMH model is an emerging team-based approach for primary care aimed at improving timely access to care, continuity of care, care cost and comprehensiveness for patient wellness. This three-year project has the following tasks: 1) to develop a comprehensive health information technology data preparation strategy to provide healthcare demand and supply portfolio data 2) to develop an adaptive discrete-cluster-based statistical estimation model that can predict healthcare workload based on key patient attributes, such as diagnosis and treatment characteristics, and 3) to create stochastic optimization models to aid in managing patient panels and staffing levels for all PCMH teams. Such models would provide dynamic updating rules for patient and staffing allocation with a stochastically migrating patient population in medical facilities; and would provide a real time appointment scheduling system to improve daily operations through optimal patient allocation and staffing under stochastic patient demand for service. With the development of a healthcare workload portfolio estimation model, patient allocation model and dynamic scheduling strategy, this research will directly support PCMH practices by assigning patients to provider team members so that that providers' time can be fully utilized and patients receive prompt, adequate and economical healthcare. The project will be in collaboration with the largest healthcare system in U.S., the Veterans Health Administration. We expect the results of this research to be adopted and implemented in hundreds of medical facilities. The research will be brought into classrooms to inspire and engage undergraduate and graduate students, especially underrepresented groups. This research will contribute to and enhance our graduate level courses and seminars. In addition workshops will be developed on campus and in various conferences.