Use of EHR Metadata to Assess Hospital Discharge Planning for Post-Acute Transitions

项目摘要/摘要 每年有600万老年人住院，然后过渡到急性后护理服务。 尽管多年来进行了大量的政策干预，但这些过渡仍然协调不力，具有破坏性。 近四分之一患有心力衰竭和肺炎等常见疾病的患者仍在经历 出院后的不稳定足够严重，以至于他们最终回到了医院。强健放电 规划对过渡期护理质量至关重要-临床医生需要准备和沟通高质量 支持以下内容的信息(例如，未决结果摘要、药物和治疗需求的变化) 随访。不幸的是，由排放计划产生的排放文件的质量 众所周知，行动是高度可变和容易出错的，并使患者面临更高的缝隙风险和 护理上的错误。卫生系统需要可操作的数据来评估出院计划流程在哪里 如果他们要集中改进，实现并维持更强有力的过渡性护理做法，就会出现崩溃。 我们的长期目标是为卫生系统提供监测和加强 优化急性后过渡的特定放电工作流行为。在这份提案中，我们分析了 电子病历元数据，帮助卫生系统识别不一致之处并磨练准备工作的最佳做法 病人要求出院。电子病历元数据是通过临床医生的交互生成的数字“指纹” 使用电子病历，例如登录和注销、点击以及查看或修改患者数据所花费的时间。这些 数据被描述为研究的潜在金矿。有了元数据，我们可以重建和 描述排放计划活动中的重要过程变化，最终评估 特定的行为与避免重新入院和其他过渡性护理结果有关。 使用具有大容量、高再入院风险条件(例如心力衰竭)的患者样本 和肺炎)，我们首先使用混合方法过程挖掘和模式分析技术与EHR 元数据，用于定义、测量和评估关键排放计划任务和 工作流程。例如，出院人员参考社会工作记录的频率 出院命令，或药物与出院时间相一致的时间。我们 然后描述性地分析导致这种变化的关键患者和背景因素。接下来，我们使用 用于评估与哪些排污计划任务和工作流相关联的通用线性模型 进程(如及时出院和后续护理)和基于结果(如重新入院)的措施 过渡期护理质量。我们的工作测试了支持健康的执行评估的新方法 系统学习和改进，并直接与AHRQ健康IT优先事项保持一致，以开发数据驱动 帮助提供商组织改进和推进有效、可扩展的护理交付变化的解决方案。

Project Summary/Abstract Six million older adults every year are hospitalized and then transition to post-acute care services. Despite years of substantial policy intervention, these transitions remain poorly coordinated and disruptive. Nearly one in four patients with common conditions like heart failure and pneumonia continue to experience post-discharge destabilization severe enough that they end up back in the hospital. Robust discharge planning is critical to transitional care quality - clinicians need to prepare and communicate high-quality information (e.g. summary of pending results, changes in medication and therapy needs) that supports follow-up care. Unfortunately, the quality of discharge documentation produced by discharge planning actions is known to be highly variable and error-prone and puts patients at increased risk of gaps and errors in care. Health systems need actionable data to assess where the discharge planning process is breaking down if they are to focus improvements that effect and sustain stronger transitional care practices. Our long-term objective is to provide health systems with the tools to monitor and strengthen specific discharge workflow behaviors that optimize post-acute transitions. In this proposal, we analyze EHR metadata to help health systems identify inconsistencies and hone best practices in preparing patients for discharge. EHR metadata are the digital “fingerprints” generated through clinicians’ interactions with the EHR, such as logging in and out, clicks, and time spent viewing or modifying patient data. These data have been described as a potential goldmine for research. With metadata, we can reconstruct and characterize important process variation in discharge planning activities, ultimately evaluating whether specific behaviors are associated with avoided readmissions and other transitional care outcomes. Using a sample of patients with high-volume, high-readmission risk conditions (e.g. heart failure and pneumonia), we first use mixed methods process mining and pattern analysis techniques with EHR metadata to define, measure, and assess the extent of variation in key discharge planning tasks and workflows. Examples might include how often discharging providers reference social work notes during discharge orders, or the timing of when medications are reconciled relative to the time of discharge. We then descriptively analyze key patient and contextual factors that drive this variation. Next, we use generalized linear models to assess which discharge planning tasks and workflows are associated with process (e.g. timely discharge and follow-up care) and outcome-based (e.g. readmissions) measures of transitional care quality. Our work tests novel methods of implementation evaluation that support health system learning and improvement, and aligns directly with AHRQ health IT priorities to develop data-driven solutions to help providers organizations refine and advance impactful, scalable changes in care delivery.