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

该学院早期职业发展(CALEAR)奖的目标是创建能力规划和分配模型，以改善初级保健提供中的及时访问、患者与临床医生的连续性和护理协调。为此，将使用两阶段和多阶段随机优化方法。具体地说，这些模型将优化不同合并症和慢性病患者(病例混合类别)患者的护理需求分配给医生-护士团队。拟议框架的一个关键部分是模拟非探视护理时间的影响(通过电子邮件、电话和/或其他手段，在面对面的办公室探视之外提供的护理)。非探视护理很重要，因为它为提供者和患者提供了一种增强访问、连续性和协调性的替代方法，也可以取代常规的办公室探视。所有项目任务将与两个学术医疗中心(梅奥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执行。该项目包括传播计划(医疗中心的年度讲习班)和实际成果的实施计划(马萨诸塞州综合医院的常驻诊所)。如果成功，随机模型和经验估计将提供严格的量化支持，为全国正在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，患者群体的获取、连续性和护理协调将受到积极影响。为解决具有计算挑战性的分配问题而开发的方法和由此产生的见解将适用于其他服务环境。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研究课程的教学将通过使用真实世界的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.