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: 9904156
• 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 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904156
• 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领导层必须了解质量 需要通过有效和可采取行动的业绩衡量和报告加以改进。性能 作为全系统学习的有效工具的测量是以经验证据为基础的 支持在决策的每一个层次的措施的可靠性和有效性，一个数据仓库， 分析可及时访问多个级别和多个不同时间段的相关数据 用于处理数据和生成可操作信息的引擎，以及用于 及时向相关利益攸关方提供信息。此外，明确注重成果可避免 问题源于减少“信号”比率的过程措施的扩散（重要的 结果）到“噪音”（边际价值的过程测量）。 VHA已经开发了各种方法和措施来获取临床信息并评估 保健质量。2012年推出的战略分析改进和学习价值（SAIL） 报告提供了28个性能指标的设施性能信息。SAIL报告的重点是 不同绩效指标之间的设施级可变性。然而，越来越多的证据表明， 在较低水平的卫生系统中，病人的结果最大。在此应用程序的初步工作中，我们 在员工数据中发现了类似的模式我们发现，工作组在护理单位一级解释了一个 员工满意度的变化比例很大。与此同时， 设施水平几乎为零。这意味着重要的医院内单位水平的满意度差异是 由于将重点放在设施一级作为分析和报告的单位而被掩盖。因此，网站不能 以平均员工满意度为基础。根据卫生保健和其他方面的文献， 在教育等领域，我们预计同样的现象也会适用于我们将要分析的结果。 相比之下，SAIL报告依赖于设施一级的结果和措施，假设设施- 水平变异性是可靠的，而忽略单位水平方差的贡献。这些假设反映了 生态谬误的概念，并证明需要在VHA的分析模型，可以提供有效的 通过评估卫生系统多个层面的差异来评估绩效信息。 我们这个项目的目标是推进多层次医疗保健绩效测量的科学， 反馈以支持LHCS。我们将建立一个分析模型，提供有效和可靠的评估， 住院病人的结果和他们的结构预测在多个层次的卫生系统，我们将提出 反馈报告中的这些数据针对那些可以使用结果的一线临床医生和管理员 来提高医疗质量。为了实现这一目标，我们将1）建立一个多层次结构方程模型 （ML-SEM）使用住院结局（死亡率、再入院、不良事件）及其预测因素（例如，患者 疾病负担、人员配备水平），以同时评估单位级别和设施级别的变化;和2） 开发用于显示设施绩效数据的模板，这些模板根据利益相关者的需求量身定制，并促进 质量改进。构建模型以评估多个水平的变异（目标1）将开始使用 混合效应模型，以检查结果和预测因素的变化。接下来，我们将使用预测模型来 确定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） 将采用混合方法，包括利益相关者需求评估和迭代设计， 可用性试点测试。我们的目标是推进测量科学，超越对整体的粗略测量。 设施和VISN的性能，朝着更可行的反馈有关的来源，在性能的变化。 这项工作将满足LHCS的需求，利用庞大的VHA数据基础设施， 可操作的知识，并有效地传达给最终用户，以提高护理退伍军人的质量。