Learning Patterns of Collaboration to Optimize the Management of Care Providers

学习协作模式以优化护理提供者的管理

基本信息

批准号：
8820357
负责人：
You Chen
金额：
$ 8.6万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-01 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8820357
关键词：
Adoption Algorithms Behavior Big Data Blood Pressure Body mass index Caring Catheterization Cessation of life Clinical Code Collaborations Collection Communities Community Healthcare Complex Data Dependency Detection Diagnosis Diagnostic Disease Disease model Electronic Health Record Employee Environment Evaluation Event Goals Health Information System Health Personnel Healthcare Heart Diseases Hospitals Knowledge Lead Learning Length Length of Stay Medical Care Team Medical Errors Methodology Methods Mining Modeling Nature Outcome Pathway Analysis Patient Care Patients Pattern Patterns of Care Principal Component Analysis Procedures Provider Relative (related person)Research Resource Allocation Safety Series Social Network Source Surveys System Techniques Time Work base collaborative environment cost data mining demographics design diabetes management effective therapy health care quality health information technology improved member social

项目摘要

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.

描述（由申请人提供）：健康信息系统（HIS），尤其是电子健康记录（EHR）系统，可以显着提高医疗保健的效率和质量，因为它使医疗保健组织（HCOS）的员工能够大规模地协调和协作。同时，医疗环境由高度多样化和动态的工作流程组成，随着现代EHR系统的大小和范围的增加，它们可能会加剧HCO的复杂性。如果没有适当管理组织复杂性，它可能会限制EHR系统的好处，并显着造成负面影响，例如更长的等待时间，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，并且可以通过数据挖掘对EHR系统的利用来发现此类模式。这项工作之所以及时，是因为在过去的几年中，EHR的采用率显着增长，HCO员工越来越多地使用此类系统来记录患者状态并与其他提供商进行通信。此类数据的数量和细节为大数据挖掘技术提供了一个机会，以学习未明确记录的护理模式。我们建议通过利用EHR系统来学习协作模式，以确定如何优化护理提供者的管理。这将通过三个特定目的来完成：（1）通过分析利用数据来发现针对特定类型疾病的有效护理提供者团队。基于此类团队，HCO将能够通过更及时地准备团队成员来更有效地管理患者。（2）学习护理提供者之间的依赖性，这对于资源分配和护理团队的管理至关重要。（3）模型特异性的治疗工作流程，以评估哪些事件序列可为患者带来最有效的结果。在此过程中，该项目旨在减少患者住院时间的长度，并最终帮助患者（HCO）降低成本。