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

使用 EHR 元数据评估急性期后过渡的出院计划

• 批准号: 10598578
• 负责人: Dori Cross
• 金额: $ 16.23万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10598578
• 关键词: Use; EHR; Metadata; Assess; Hospital

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.

项目总结/摘要 每年有600万老年人住院，然后过渡到急性期后护理服务。 尽管多年来进行了大量的政策干预，但这些过渡仍然缺乏协调和破坏性。 近四分之一的患有心力衰竭和肺炎等常见疾病的患者继续经历 出院后的不稳定足以让他们回到医院。鲁棒放电 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的 支持的信息（例如，待定结果的总结、药物和治疗需求的变化） 后续护理。不幸的是，出院计划产生的出院文件的质量 众所周知，行动是高度可变的，容易出错，使患者面临更大的差距风险， 护理中的错误卫生系统需要可操作的数据来评估出院规划流程 如果他们要把重点放在影响和维持更强有力的过渡性护理做法的改进上， 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出院工作流程行为。在这篇文章中，我们分析 电子健康记录元数据帮助卫生系统识别不一致之处， 患者出院。EHR元数据是通过临床医生的交互产生的数字“指纹” 与EHR相关的信息，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 描述排放计划活动中的重要工艺变化，最终评估是否 特定的行为与避免再入院和其他过渡性护理结果相关。 使用高容量、高再入院风险疾病（如心力衰竭）患者样本 和肺炎），我们首先使用混合方法流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规划任务的变化程度， 工作流程。例子可能包括出院时提供者参考社会工作笔记的频率 出院命令，或相对于出院时间核对药物的时间。我们 然后连续分析驱动这种变化的关键患者和背景因素。接下来，我们使用 广义线性模型，以评估与哪些出院计划任务和工作流相关联 过程（如及时出院和后续护理）和基于结果（如再入院）的措施， 过渡期护理质量。我们的工作测试了支持健康的实施评估的新方法 系统学习和改进，并直接与AHRQ卫生IT优先事项保持一致，以开发数据驱动的 解决方案，以帮助提供者组织完善和推进医疗服务中有影响力的、可扩展的变化。