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

该学院早期职业发展（CAREER）奖的目标是创建能力规划和分配模型，以改善及时获取，患者-临床医生连续性和初级保健服务的护理协调。两阶段和多阶段随机优化方法将用于此目的。具体而言，该模型将优化分配的护理需求所产生的不同的合并症和慢性疾病（病例组合类）的医生，护士团队。拟议框架的一个关键部分是模拟非访问护理（通过电子邮件，电话和/或其他方式提供的面对面办公室访问之外的护理）所花费的时间的影响。非访问护理是重要的，因为它提供了提供者和患者的替代方法，以加强访问，连续性和协调，也可以取代定期的办公室访问。所有项目任务都将与两个学术医疗中心（马约诊所和马萨诸塞州总医院）和一个小型家庭医学诊所密切合作。该项目包括传播计划（在医疗中心举办年度讲习班）和在实践中落实成果计划（在马萨诸塞州总医院的住院医师诊所）。如果成功的话，随机模型和经验估计将提供严格的定量支持，为全国范围内采用的以患者为中心的医疗之家的运营和财务设计提供信息。最终，患者群体的访问，连续性和护理协调将受到积极影响。为解决计算上具有挑战性的分配问题而开发的方法以及由此产生的见解将与其他服务环境相关。通过这个项目，研究生和本科生将在临床环境中进行跨学科研究。此外，研究生和本科生运筹学课程的教学将通过使用真实的世界医疗保健数据集得到加强。

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.