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

• 批准号: 10217257
• 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
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217257
• 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

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).

美国的医疗保健支出在2017年达到3.5万亿美元，比2016年增长4.6％。 被认识到长时间的住院时间（LOS）和计划外的再读是主要的两个 增加医疗费用的原因。确定哪些因素与延长LOS和 计划外的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。 医疗保健研究与质量机构（AHRQ）认识到，在费用下的护理分裂 - 服务系统可能导致各种问题，包括服务不良和不必要 测试和程序，所有这些都有可能扩展LOS和计划外的再入院。有效的 已经提出了护理协调，以解决许多此类问题，并且是民族的优先事项 质量策略，由AHRQ领导。 然而，衡量护理协调的有效性存在许多挑战。特别是 与患者的结果缺乏明确的关系（例如，长时间的LOS或计划外的再读）。 基于电子病历（EMR）的护理协调措施已由AHRQ突出显示了三个 潜在优势：i）最小数据收集伯恩，ii）丰富的临床环境和iii）纵向患者 观察。但是，当前基于EMR的措施集中于对EMR系统的评估（例如， 有意义的用途）并比较粗粒水平的护理有效性（例如， 有意义地使用EMR系统并降低LOS或计划外的再入院率）。不幸的是，这样 措施忽略了特定驱动因素（例如，医疗保健专业人员之间的相互作用的变化） LOS和计划外再入院率的可变性。在这个项目中，我们将开发一个基于EMR的框架 在细粒度的水平上表征护理协调，这说明了相互作用网络 两个或多个医疗保健专业人员（例如，医生，护士，社会工作者，护理经理和支持 工作人员参与患者的护理 - 并衡量其对LOS和计划外再入院的影响。 为了实现目标，我们将设计i）数据挖掘算法以自动学习护理协调模式 并分析约有230万患者的EMR的LOS和计划外的重新入学 EMR使用历史悠久的医疗中心； ii）假设驱动的方法来量化关系 在学识渊博的模式和LOS和计划外再读之间 种族和性别）将被视为混杂变量； iii）翻译的解释过程 将模式推断为HCO的可行标准。这项研究值得注意，因为在 项目可以作为科学依据，以自动i）学习野外的护理协调模式 医疗服务和健康状况范围； ii）通过它们的这些模式来衡量这些模式的有效性 与各种患者结果的关系（例如，LOS和计划外的再入院）。