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

2017年，美国的医疗保健支出达到3.5万亿美元，比2016年增长4.6%。它有 已经认识到，住院时间延长（LOS）和计划外再入院是两个主要的 导致医疗费用上涨。确定哪些因素与延长的服务水平相关， 计划外再入院将提供关于如何降低成本和改善护理提供的宝贵知识。 医疗保健研究和质量机构（AHRQ）已经认识到，在收费制度下， 服务系统可能会导致各种问题，包括服务协调性差和不必要的 所有这些都有可能延长LOS和计划外再入院。有效 护理协调，已被提议解决其中许多问题，是国家的优先事项， 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 缺乏与患者结果的明确关系（例如，延长的LOS或计划外再入院）。 AHRQ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 观察.然而，目前基于EMR的措施侧重于EMR系统的评估（例如， 有意义的使用）并在粗粒度级别比较护理的有效性（例如，的关系 EMR系统的有意义的使用和LOS或计划外再入院率的降低）。可惜这样 测量忽略了特定的驱动因素（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变异性。在这个项目中，我们将开发一个基于电子病历的框架 在细粒度级别上表征护理协调，这说明了 两个或多个健康护理专业人员（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调模式 并从一个大型学术机构的约230万患者的EMR中分析LOS和计划外再入院。 一个有着悠久EMR使用历史的医疗中心; ii）假设驱动的方法来量化关系 学习模式和LOS与计划外再入院之间的关系，其中患者的人口统计学（例如，年龄， 种族和性别）将被视为混杂变量;和iii）解释过程， 推断出的模式转化为HCO的可操作标准。这项研究是值得注意的，因为方法创建于 项目可以作为科学基础，以自动i）学习野生动物的护理协调模式， 一系列的医疗保健服务和健康状况;和ii）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）。

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).