Adaptive Appointment Systems with Patient Preferences

卫生保健系统在一个动态的环境中运作。 卫生服务提供者需要首先了解这种环境的技术，而不增加数据收集的负担，以及随后以最佳方式对动态输入作出反应的技术。该项目的主要目标是确定时间戳数据是否可以用于可靠地估计动态演变的患者偏好，并确定在何种程度上动态偏好信息的预约系统可以优于静态信息的类似系统。该项目的智力优势包括开发数学技术，用于从计算机化的预约数据中推断和更新患者偏好类别，并开发利用这些信息来改善访问和诊所收入的预订控制政策。该项目将加强现有的方法，并开发新的技术，将动态学习纳入非静态环境中，更广泛的影响包括：（a）实用工具，用于从诊所定期收集的数据中识别和更新患者的偏好，（B）工程教育，（c）改进医疗保健管理实践。该项目将作为在医疗保健管理中使用动态和适应性方法的好处的概念证明。这些技术可以应用于其他类似的努力中，例如，基于部分观察到的患者数据和医生输入来开发个性化治疗和健康计划。该项目将帮助为一名学生的博士研究提供部分资金，并为本科生参与研究提供机会。 此外，在该项目过程中获得的数据集将用于通过专业后课程培训保健专业人员。除了存档期刊出版物和会议报告外，本研究的结果将通过在PI的研究网页http：//www.me.umn.edu/labs/scorlab/上发布以及与当地卫生服务提供者的互动进行传播。

Health care systems operate in a dynamic environment. Health service providers need techniques to first understand this environment, without increasing the data-collection burden, and subsequently techniques to respond to the dynamic inputs in an optimal fashion. The key objective of this project is to determine if time-stamp data can be used to reliably estimate dynamically evolving patient preferences, and to determine the extent to which an appointment system with dynamic preference information can outperform a similar system with static information. Intellectual merits of this project include the development of mathematical techniques for inferring and updating patient-preference categories from computerized appointments data, and developing booking control policies that utilize this information to improve access as well as clinic revenue. The project will enhance existing methods and develop new techniques for incorporating dynamic learning in non-stationary environments.Broader impacts include (a) practical tools for identifying and updating patient preferences from data routinely collected by clinics, (b) engineering education, and (c) improved health care management practices. This project will serve as a proof of concept of the benefits of using dynamic and adaptive methods in health care management. These techniques can be applied in other similar endeavors, for example, in developing individualized treatment and wellness programs based on partially-observed patient data and physician inputs. The project will help to partially fund doctoral studies of a student and provide opportunities for involving undergraduate students in research. Moreover, the data set obtained during the course of this project will be used to train health care professionals through post-professional courses. In addition to archival journal publications and conference presentations, results of this research will be disseminated by postings on the PI's research web page at http://www.me.umn.edu/labs/scorlab/, and by interactions with local health service providers.