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Data-Driven Management of CKD: Predicting Disease Progression and Optimizing Follow-Up Schedules
发布日期:2024-06-25  来源:信誉最好的20个网投网站   查看次数:

报告时间:2024年6月29日(星期六)上午9:00-11:00

报告地点:信誉最好的20个网投网站新大楼925会议室

人:Jennifer Shang

工作单位:The Katz Graduate School of Business of University of Pittsburgh

举办单位:信誉最好的20个网投网站

报告简介:

This research aims to optimize the management of chronic kidney disease (CKD) using big data at Veterans Affairs (VA) hospitals. It utilizes electronic health records to predict CKD progression and recommends personalized follow-up appointment schedules. The proposed model incorporates factors such as CKD severity, comorbidities, age, and distance to nephrologist. By leveraging data from 11 VA hospitals and 68,513 CKD patients, the model outperforms other methods and enhances patient care. Furthermore, this approach can be adapted for managing other chronic diseases beyond CKD.

报告人简介:

Jennifer Shang's research focuses on healthcare analytics, operations management, and e-commerce. She applies data analytics to improve patient care and operational efficiency in hospitals. She develops theoretical and heuristic approaches to enhance productivity and quality in business operations. She utilizes multi-criteria decision-making techniques and combines subjective judgment with objective data to rank options and predict outcomes. She has published numerous papers in top journals such as POM, JMR, MSOP, EJOR, DSS, etc.


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