Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient Outcomes

发现电子病历中的护理协调实践模式：解释及其对患者结果的影响

基本信息

批准号：
10460162
负责人：
You Chen
金额：
$ 36.98万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-10 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10460162
关键词：
Academic Medical Centers Adoption Age Algorithms Cardiology Caring Case Manager Clinic Clinical Complex Computerized Medical Record Confounding Factors (Epidemiology)Data Data Collection Data Engineering Data Store Disease Disease Management Effectiveness Ensure Environment Exertion Exhibits Expenditure Fee-for-Service Plans Goals Grain Health Health Care Costs Health Expenditures Health Professional Healthcare Hospital Costs Individual Interview Intuition Knowledge Lead Learning Length of Stay Manuals Measures Methodology Methods Nature Nurses Outcome Patient Care Patient observation Patient-Focused Outcomes Patients Pattern Patterns of Care Procedures Process Quality of Care Race Recording of previous events Research Services Social Workers Statistical Methods Statistical Models Surveys System Testing Time Translating Trauma United States United States Agency for Healthcare Research and Quality Variant base care coordination care delivery care fragmentation care providers care systems comorbidity compare effectiveness cost data mining demographics design effectiveness evaluation effectiveness measure health care delivery health care service health care service organization health care settings hospital readmission improved neglect payment readmission rates sex stem

项目摘要

Healthcare expenditures in the United States reached $3.5 trillion in 2017, up 4.6 percent from 2016. It has been recognized that prolonged length of stay (LOS) and unplanned readmission are two of the primary causes of higher healthcare costs. Determining which factors are associated with prolonged LOS and unplanned readmission will provide valuable knowledge about how to reduce costs and improve care delivery. The Agency for Healthcare Research and Quality (AHRQ) has recognized that care fragmentation under a fee- for-service system can lead to various problems, including poor harmonization of services and unnecessary testing and procedures, all of which have the potential to extend LOS and unplanned readmissions. Effective care coordination, has been proposed to resolve many of these problems, and is a priority of the National Quality Strategy, which is led by AHRQ. Yet, there are numerous challenges to measuring the effectiveness of care coordination. In particular, there is a lack of a clear relationship with a patient’s outcome (e.g., prolonged LOS or unplanned readmission). Electronic medical record (EMR)-based care coordination measures have been highlighted by AHRQ for three potential advantages: i) minimal data collection burden, ii) rich clinical context and iii) longitudinal patient observation. However, current EMR-based measures focus on an assessment of EMR systems (e.g., meaningful use) and compare effectiveness of care at a coarse-grained level (e.g., the relation between meaningful use of an EMR system and reduction in LOS or unplanned readmission rates). Unfortunately, such measures neglect the specific drivers (e.g., variations of interactions between healthcare professionals) of variability in LOS and unplanned readmission rates. In this project, we will develop an EMR-based framework to characterize care coordination at a fine-grained level, which accounts for the interaction network between two or more healthcare professionals (e.g., doctors, nurses, social workers, care managers, and supporting staff) involved in a patient’s care - and measure its impact on LOS and unplanned readmission. To achieve the goal, we will design i) data mining algorithms to automatically learn care coordination patterns and analyze LOS and unplanned readmission from the EMRs of ~2.3 million patients at a large academic medical center with a long history of EMR use; ii) hypothesis-driven approaches to quantify the relationship between a learned pattern and LOS and unplanned readmission, where a patient’s demographics (e.g., age, race and sex) will be considered as confounding variables; and iii) an interpretation process to translate the inferred patterns into actionable criteria for HCOs. This research is notable because methods created in the project can be served as a scientific basis to automatically i) learn care coordination patterns across a wild range of healthcare services and health conditions; and ii) measure the effectiveness of these patterns via their relationships with various patient outcomes (e.g., LOS and unplanned readmission).

2017年美国医疗保健支出达到3.5万亿美元，比2016年增长4.6%。人们认识到，延长住院时间（LOS）和计划外再入院是两个主要的原因。确定哪些因素与长期 LOS 相关，以及导致医疗费用增加的原因。计划外再入院将提供有关如何降低成本和改善护理服务的宝贵知识。医疗保健研究和质量局 (AHRQ) 已经认识到，收费下的护理分散化服务体系可能会导致各种问题，包括服务协调不力和不必要的服务。测试和程序，所有这些都有可能延长 LOS 和意外再入院。护理协调已被提议解决许多此类问题，并且是国家的优先事项质量战略，由 AHRQ 领导。然而，衡量护理协调的有效性仍面临许多挑战。与患者的结果缺乏明确的关系（例如，长期住院或计划外再入院）。 AHRQ 连续三年强调了基于电子病历 (EMR) 的护理协调措施潜在优势：i) 最小的数据收集负担，ii) 丰富的临床背景和 iii) 纵向患者然而，当前基于 EMR 的措施侧重于对 EMR 系统的评估（例如，有意义的使用）并在粗粒度水平上比较护理的有效性（例如，不幸的是，有效地使用 EMR 系统并减少 LOS 或计划外再入院率）。措施忽略了具体的驱动因素（例如，医疗保健专业人员之间互动的变化）在这个项目中，我们将开发一个基于 EMR 的框架。细粒度地描述护理协调的特征，这说明了护理协调之间的相互作用网络两名或两名以上医疗保健专业人员（例如医生、护士、社会工作者、护理经理和支持人员）工作人员）参与患者的护理 - 并衡量其对 LOS 和意外再入院的影响。为了实现这一目标，我们将设计 i) 数据挖掘算法来自动学习护理协调模式并根据大型学术机构约 230 万名患者的 EMR 分析 LOS 和计划外再入院具有长期使用 EMR 历史的医疗中心；ii) 量化关系的假设驱动方法；学习模式与 LOS 和计划外再入院之间的关系，其中患者的人口统计数据（例如年龄、种族和性别）将被视为混杂变量；以及 iii）翻译的解释过程这项研究值得注意，因为在 HCO 中创建了方法。项目可以作为自动学习护理协调模式的科学基础医疗保健服务和健康状况的范围；以及 ii) 通过其衡量这些模式的有效性与各种患者结果的关系（例如 LOS 和计划外再入院）。