Learning Patterns of Collaboration to Optimize the Management of Care Providers

描述(申请人提供)：健康信息系统(HIS)，特别是电子健康记录(EHR)系统，可以显著提高医疗保健的效率和质量，因为它使医疗保健组织(HCO)的员工能够更有效和大规模地协调和合作。与此同时，医疗保健环境由高度多样化和动态的工作流程组成，随着现代EHR系统规模和范围的增加，它们可能会加剧HCO的复杂性。如果组织复杂性没有得到适当的管理，可能会限制EHR系统的好处，并极大地造成负面影响，如等待护理的时间更长、重复诊断和医疗错误。我们假设协作模式可以帮助管理复杂性和促进患者管理，并且这种模式可以通过利用电子病历系统的数据挖掘来发现。这项工作是及时的，因为电子病历的采用在过去几年中显著增长，而且HCO员工越来越多地使用这种系统来记录患者状态并与其他提供者进行沟通。这些数据的数量和细节为大数据挖掘技术提供了一个机会，可以学习没有明确记录的护理模式。我们建议开发方法，通过利用电子病历系统来学习协作模式，以确定如何优化护理提供者的管理。这将通过三个具体目标来实现：(1)通过对利用数据的分析，发现针对特定疾病类型量身定做的有效护理提供者团队。在这些团队的基础上，通过更及时地为团队成员做好准备，HCO将能够更有效地管理患者。(2)了解护理提供者之间的依赖关系，这对护理团队的资源分配和管理至关重要。(3)模拟针对疾病的治疗工作流程，以评估哪些事件序列为患者带来最有效和最有效的结果。通过这样做，这个项目的目的是减少患者的住院时间，并最终帮助患者(医院管理人员)降低成本。

DESCRIPTION (provided by applicant): Health information systems (HIS), especially electronic health record (EHR) systems, can significantly improve efficiency and quality of healthcare because it enables the employees of Healthcare Organizations (HCOs) to coordinate and collaborate more effectively and on a large scale. At the same time, healthcare environments are composed of highly diverse and dynamic workflows and, as modern EHR systems increase in size and scope, they may exacerbate the complexity of HCOs. If organizational complexity is not managed appropriately, it could limit the benefits of EHR systems and significantly contribute to negative effects, such as longer waiting times for care, replication of diagnostics, and medical errors. We hypothesize that patterns of collaboration can assist in managing complexity and facilitating patient management and that such patterns can be discovered by data mining on the utilization of EHR systems. This work is timely because EHR adoption has grown significantly over the past several years and HCO employees are increasingly using such systems to document patient status and communicate with other providers. The quantity and detail of such data provides an opportunity for big data mining techniques to learn patterns of care that are not explicitly documented. We propose to develop methods to learn patterns of collaboration through the utilization of EHR systems to determine how the management of care providers can be optimized. This will be accomplished through three specific aims: (1) Discover effective teams of care providers tailored to specific types of disease through the analysis of utilization data. Based on such teams, HCO will be able to manage patients more efficiently by prepping team members in a more timely manner. (2) Learn dependencies between care providers, which will be critical for resource allocation and management of care teams. (3) Model disease-specific treatment workflows to assess which sequences of events lead to the most efficient and effective outcomes for patients. In doing so, this project aims to reduce the length of patients' hospital stay and, ultimately help patients (an HCOs) reduce costs.