CAREER: Stochastic Models for Designing the Patient Centered Medical Home in Primary Care

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The objective of this Faculty Early Career Development (CAREER) award is to create capacity planning and allocation models to improve timely access, patient-clinician continuity, and care coordination in primary care delivery. Two-stage and multi-stage stochastic optimization methods will be used for this purpose. Specifically, the models will optimize the allocation of care needs arising from patients with different comorbidities and chronic conditions (case-mix classes) to physician-nurse teams. A key part of the proposed framework is modeling the impact of time spent in non-visit care (care provided outside of the face-to-face office visit, through email, phone and/or other means). Non-visit care is important since it provides both providers and patients an alternate method to enhance access, continuity, and coordination, and can also substitute for regular office visits. All project tasks will be carried out in close collaboration with two academic medical centers (Mayo Clinic and Massachusetts General Hospital) and a small family medicine clinic. The project includes plans for dissemination (annual workshops at medical centers), and implementation of results in practice (residency clinics at Massachusetts General Hospital). If successful, the stochastic models and empirical estimations will provide rigorous quantitative support to inform the operational and financial design of the patient centered medical home that is being adopted nationwide. Ultimately access, continuity and care coordination for patient populations will be positively impacted. The methodologies developed to solve the computationally challenging allocation problems and the resulting insights will have relevance for other service environments. Through this project, graduate and undergraduate students will conduct interdisciplinary research in clinical settings. Additionally, pedagogy in graduate and undergraduate operations research courses will be enhanced through the use of real world healthcare data sets.