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

2017年，美国的医疗支出达到3.5万亿美元，比2016年增长4.6%。它有 已认识到延长逗留时间(LOS)和计划外重新入院是两个主要原因 更高的医疗成本的原因。确定哪些因素与长时间的LOS和 意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。 医疗研究和质量机构(AHRQ)已经认识到，在一项费用下，医疗保健分散- 以服务为中心的制度会导致各种问题，包括服务协调性差和不必要 测试和程序，所有这些都有可能延长LOS和计划外重新入院。有效 护理协调，已被提议解决其中许多问题，并是国家优先事项 质量战略，由AHRQ领导。 然而，在衡量护理协调的有效性方面存在许多挑战。尤其是，有 缺乏与患者预后的明确关系(例如，长时间的失血或计划外的再入院)。 AHRQ强调了基于电子病历(EMR)的护理协调措施已有三年 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 观察。然而，当前基于电子病历的措施侧重于对电子病历系统的评估(例如， 有意义的使用)，并比较粗粒度级别的护理效果(例如， 有意义地使用电子病历系统和减少损失或计划外再住院率)。不幸的是，这样的 衡量标准忽略了特定的驱动因素(例如，医疗保健专业人员之间的互动变化) 洛杉矶和计划外再住院率的变异性。在这个项目中，我们将开发一个基于EMR的框架 为了在细粒度水平上表征护理协调，这解释了 两名或更多医疗保健专业人员(例如，医生、护士、社会工作者、护理经理和支持人员 工作人员)参与病人的护理，并衡量其对LOS和计划外再入院的影响。 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作模式 并分析了一家大型学术机构的约230万名急诊室患者的LOS和计划外再入院情况 电子病历使用历史悠久的医疗中心；ii)量化关系的假设驱动的方法 在学习模式和LOS和计划外重新入院之间，其中患者的人口统计(例如，年龄， 种族和性别)将被视为混淆变量；以及iii)翻译过程 将推断的模式转化为可操作的HCO标准。这项研究之所以值得注意，是因为 项目可以作为自动I)学习野生动物护理协调模式的科学基础 医疗服务和健康状况的范围；以及ii)通过以下方式衡量这些模式的有效性 与各种患者结局的关系(例如，LOS和计划外再入院)。

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).