Improving the Measurement of VA Facility Performance to Foster a Learning Healthcare System

改进对 VA 设施绩效的衡量，以培育学习型医疗保健系统

• 批准号: 10186492
• 负责人: LAURA A PETERSEN
• 金额: --
• 依托单位: MICHAEL E DEBAKEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186492
• 关键词: Accountability; Address; Administrator; Adverse event; Affect; Benchmarking; Care given by nurses; Caregivers; Caring; Climate; Clinical; Complex; Computing Methodologies; Data; Data Display; Decision Making; Dimensions; Discipline of Nursing; Ecological Bias; Education; Employee; Ensure; Equation; Evaluation; Feedback; Fostering; Goals; Health Status; Health system; Healthcare; Healthcare Systems; Hospital Units; Hospitals; Human; Industrial Psychology; Industrialization; Inpatients; Intervention; Knowledge; Leadership; Learning; Literature; Measurement; Measures; Methods; Modeling; Needs Assessment; Noise; Nosocomial Infections; Nurses; Outcome; Outcome Measure; Patient-Focused Outcomes; Patients; Pattern; Performance; Process; Process Measure; Productivity; Psychology; Quality of Care; Reporting; Research; Resources; Science; Services; Signal Transduction; Site; Source; Statistical Methods; Statistical Models; Structure; Students; System; Techniques; Testing; Time; Translating; Validity and Reliability; Variant; Veterans; Work; base; burden of illness; computerized data processing; data infrastructure; data resource; data warehouse; decubitus ulcer; design; evidence base; experience; falls; health care quality; hospital readmission; improved; insight; iterative design; mortality; multilevel analysis; novel; outcome prediction; peer; performance tests; predict clinical outcome; predictive modeling; prototype; readmission rates; satisfaction; stem; tool; usability

In order to develop a learning health care system (LHCS), VHA leadership must understand where quality improvement is needed via valid and actionable performance measurement and reporting. Performance measurement that serves as an effective tool for systemwide-learning is based on empirical evidence supporting the reliability and validity of measures at each level of decision making, a data-warehouse that provides timely access to relevant data at multiple levels and across multiple different time spans, an analytics engine for processing data and generating actionable information, and an effective reporting system for delivering timely information to the appropriate stakeholders. In addition, a clear focus on outcomes avoids the problem stemming from the proliferation of process measures that reduce the ratio of “signal” (important outcomes) to “noise” (process measures of marginal value). The VHA has developed a variety of methods and measures to capture clinical information and to assess health care quality. Introduced in 2012, the Strategic Analytics for Improvement and Learning Value (SAIL) report provides facility performance information on 28 performance metrics. The SAIL report focuses on facility-level variability across diverse performance metrics. However, there is growing evidence that variation in patient outcomes is greatest at lower levels of the health system. In preliminary work for this application we found similar patterns in employee data. We found that workgroups at the nursing unit level explain a significant proportion of variation in employee satisfaction. At the same time, variability in satisfaction at the facility level was nearly zero. This means that important within-hospital unit-level differences in satisfaction are obscured by a focus upon the facility level as a unit of analysis and reporting. Therefore, sites cannot be distinguished in the basis of average employee satisfaction. Based upon the literature in health care and other fields such as education, we anticipate that this same phenomenon will hold for the outcomes we will analyze. In contrast, the SAIL report, with its reliance on facility-level outcomes and measures, assumes that facility- level variability is reliable while ignoring the contributions of unit-level variance. These assumptions reflect the concept of ecological fallacy and demonstrate a need in the VHA for an analytical model that can provide valid performance information by assessing variation at multiple levels of the health system. Our goal for this project is to advance the science of multi-level health care performance measurement and feedback to support a LHCS. We will build an analytical model that provides a valid and reliable assessment of inpatient outcomes and their structural predictors at multiple levels of the health system, and we will present this data in feedback reports targeted to those front-line clinicians and administrators who can use the results to improve the quality of care. To achieve this goal, we will 1) build a multi-level structural equations model (ML-SEM) using inpatient outcomes (mortality, readmissions, adverse events) and their predictors (e.g. patient disease burden, staffing levels) to simultaneously evaluate variation at the unit level and facility level; and 2) develop templates for displaying facility performance data that are tailored to stakeholder needs and facilitate quality improvement. Constructing a model to assess variation at multiple levels (Aim 1) will begin by using a mixed-effects model to examine variation in outcomes and predictors. Next, we will use a predictive model to identify significant predictors of outcomes. Finally, developing reports using our analytical model results (Aim 2) will use a mixed-methods approach encompassing stakeholder needs assessment and iterative design and usability pilot testing. Our goal is to advance the science of measurement beyond crude measures of overall facility and VISN performance, toward more actionable feedback about sources of variability in performance. This work will meet the needs of a LHCS by leveraging the vast VHA data infrastructure to generate valid and actionable knowledge and effectively conveying it to end users for improving the quality of care for Veterans.

为了开发一个学习型医疗保健系统(LHCs)，VHA领导层必须了解哪里有质量 需要通过有效和可操作的业绩衡量和报告进行改进。性能 作为全系统学习的有效工具的测量是基于经验证据的 支持每个决策级别的措施的可靠性和有效性，数据仓库 提供对多个级别和多个不同时间跨度的相关数据的及时访问、分析 用于处理数据和生成可操作信息的引擎，以及有效的报告系统 向适当的利益相关者提供及时的信息。此外，明确关注结果可以避免 问题源于过程措施的泛滥，这些措施降低了“信号”的比率(重要 结果)到“噪声”(边际价值的过程度量)。 VHA开发了各种方法和措施来捕获临床信息和评估 医疗保健质量。2012年推出的改进和学习价值战略分析(SAIL) 报告提供了有关28个性能指标的设施性能信息。SAIL报告的重点是 各种性能指标之间的设施级别可变性。然而，越来越多的证据表明，变异 在较低级别的卫生系统中，患者的结果最好。在此应用程序的前期工作中，我们 在员工数据中发现了类似的模式。我们发现，护理单位层面的工作组解释了 员工满意度差异较大的比例。与此同时，在满意度方面的变异性 设施水平几乎为零。这意味着医院内部单位层面满意度的重要差异是 将重点放在设施层面，作为分析和报告的一个单位而被掩盖。因此，站点不能 以员工平均满意度为基础进行区分。基于医疗保健和其他领域的文献 在教育等领域，我们预计，同样的现象也将适用于我们将分析的结果。 相比之下，SAIL报告依赖于设施一级的成果和措施，假设该设施- 水平变异是可靠的，而忽略了单位水平方差的贡献。这些假设反映了 生态谬误的概念，并证明在VHA中需要一个分析模型来提供有效的 通过评估卫生系统多个层面的差异来提供绩效信息。 我们这个项目的目标是推动多层次卫生保健绩效衡量的科学和 支持LHC的反馈。我们将构建一个分析模型，为 医疗系统多个层面的住院结果及其结构性预测因素，我们将介绍 反馈报告中的这些数据针对的是可以使用结果的一线临床医生和管理人员 以提高护理质量。为了实现这一目标，我们将1)建立一个多层结构方程模型 (ML-SEM)使用住院结果(死亡率、再入院、不良事件)及其预测因素(例如患者 疾病负担、工作人员水平)，以同时评估单位一级和设施一级的差异；以及2) 开发用于显示设施性能数据的模板，这些数据是根据利益相关者的需求量身定做的，便于 质量提升。构建在多个级别上评估变化的模型(目标1)将通过使用 检验结果和预测因素变化的混合效应模型。接下来，我们将使用预测模型来 确定对结果的重要预测因素。最后，使用我们的分析模型结果开发报告(目标2) 将使用包含利益相关者需求评估和迭代设计的混合方法，并 可用性试点测试。我们的目标是推动测量科学的发展，超越粗略的总体测量 设施和VISN性能，对性能变异性的来源提供更具可操作性的反馈。 这项工作将通过利用庞大的VHA数据基础架构来生成有效和 可操作的知识，并有效地将其传达给最终用户，以提高对退伍军人的护理质量。

项目成果

Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research.

超越医院级汇总数据：一种将电子健康记录中的临床数据改编用于护理单位级研究的方法。

• DOI: 10.1097/mlr.0000000000001972
• 发表时间: 2024
• 期刊: Medical care
• 影响因子: 3
• 作者: Yang,Christine;Kuebeler,MarkK;Jiang,Rebecca;Knox,MelissaK;Wong,JanineJ;Mehta,ParasD;Dorsey,LynetteE;Petersen,LauraA
• 通讯作者: Petersen,LauraA