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Jennifer Shang: Personalized Chronic Diseased Follow-Up Appointment--Risk-Stratified Care Through Big Data and Machine Learning
发布日期:2019-11-08  字号:   【打印

报告时间:2019年11月9日(星期六)10:00

报告地点:管理学院五楼1502会议室

  Jennifer Shang 教授

工作单位美国匹兹堡大学

举办单位管理学院

报告人简介

Jennifer Shang是美国匹兹堡大学Katz商学院运作管理专业教授,目前担任学科领域主任。Shang教授是运营管理和营销领域的国际知名专家,她的研究方向包括医疗服务管理、生产和服务运作管理、收益管理等,在管理和营销学领域的国际顶级期刊《Management Science》、《Marketing Science》、《Information Systems Research》、《Journal of Marketing》等发表论文90余篇。

报告简介

Managing patients with chronic conditions is challenging. It requires timely care adjustments based on patient health status. We leverage big data to optimize patient monitoring frequencies and improve treatment. Our research is motivated by the need to improve patient care at the Veterans Affairs (VA) hospitals. We propose an integrated model to better serve patients and effectively manage hospital resources for chronic kidney disease (CKD) care. CKD is prevalent, complex, and costly. The demand for kidney care has steadily increased, however there is a decline in the availability of nephrologists. We propose a Markov Decision Process (MDP) model, which identifies the best follow-up appointment schedule for patients. The MDP model helps attain an optimal dynamic treatment plan to enhance patient's quality of life. Our analysis reveals comorbidities, CKD severity, age and their interactions impact patients' needs. There exists a significant discrepancy between our recommendations and current VA practice. This difference may be attributed to limited capacity. By applying the newsvendor model to the MDP outcomes, we estimate the optimal staffing levels for hospitals under study. Our approach can provide more accurate forecast and better match supply and demand, which could mitigate uncertainty, improve provider retention, and attain higher patient satisfaction.

(姜元春/文)  
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