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
基本信息
- 批准号:9902190
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-06-01 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:AccountabilityAddressAdministratorAdverse eventAffectBenchmarkingCare given by nursesCaregiversCaringClimateClinicalComplexComputing MethodologiesDataData DisplayDecision MakingDecubitus ulcerDimensionsDiscipline of NursingEcological BiasEducationEmployeeEnsureEquationEvaluationFeedbackFosteringGoalsHealth StatusHealth systemHealthcareHealthcare SystemsHospital UnitsHospitalsHumanIndustrial PsychologyIndustrializationInfrastructureInpatientsInterventionKnowledgeLeadershipLearningLiteratureMeasurementMeasuresMethodsModelingNeeds AssessmentNoiseNosocomial InfectionsNursesOutcomeOutcome MeasurePatient-Focused OutcomesPatientsPatternPerformanceProcessProcess MeasureProductivityPsychologyQuality of CareReportingResearchResourcesScienceServicesSignal TransductionSiteSourceStatistical MethodsStatistical ModelsStructureStudentsSystemTechniquesTestingTimeTranslatingValidity and ReliabilityVariantVeteransWorkbaseburden of illnesscomputerized data processingdata resourcedata warehousedesignevidence baseexperiencefallshealth care qualityhospital readmissionimprovedinsightiterative designmortalitymultilevel analysisnoveloutcome predictionpeerperformance testspredict clinical outcomepredictive modelingprototypereadmission ratessatisfactionstemtoolusability
项目摘要
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.
为了开发学习医疗保健系统 (LHCS),VHA 领导层必须了解质量在哪里
需要通过有效且可操作的绩效衡量和报告来进行改进。
作为全系统学习的有效工具的测量是基于经验证据的
支持每个决策层面措施的可靠性和有效性,一个数据仓库
提供对多个级别和跨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
此外,明确关注结果可以避免出现问题。
问题源于减少“信号”比率的过程措施的扩散(重要
结果)到“噪音”(边际价值的过程测量)。
VHA 开发了多种方法和措施来捕获临床信息并评估
2012 年推出了医疗保健质量改进和学习价值战略分析 (SAIL)。
报告提供了 28 项绩效指标的设施绩效信息。
然而,越来越多的证据表明,不同绩效指标之间存在设施层面的差异。
在该应用程序的初步工作中,我们在卫生系统的较低级别对患者的治疗效果影响最大。
在员工数据中发现了类似的模式,我们发现护理单位级别的工作组解释了一个问题。
同时,员工满意度也存在显着的比例差异。
设施水平几乎为零,这意味着医院内单位级别的满意度差异很大。
由于对设施级别作为分析和报告单位的关注而被掩盖,因此,无法对站点进行分析。
根据医疗保健和其他领域的文献,以员工平均满意度为基础进行区分。
在教育等领域,我们预计我们将分析的结果也会出现同样的现象。
相比之下,SAIL 报告依赖于设施层面的成果和措施,假设设施
水平变异性是可靠的,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 VHA 需要一个能够提供有效的分析模型
通过评估卫生系统多个层面的变化来获取绩效信息。
我们这个项目的目标是推进多层次医疗保健绩效衡量的科学和
我们将建立一个分析模型,提供有效且可靠的评估。
卫生系统多个层面的住院结果及其结构预测因素,我们将介绍
反馈报告中的数据针对那些可以使用结果的一线战士和管理员
为了提高护理质量,我们将 1) 建立一个多级结构方程模型。
(ML-SEM) 使用住院患者结果(死亡率、再入院、不良事件)及其预测因素(例如患者
疾病负担、人员配备水平),同时评估单位级别和设施级别的变化;以及 2)
开发用于显示设施绩效数据的模板,这些模板适合利益相关者的需求并促进
构建一个模型来评估多个级别的变化(目标 1)将首先使用
接下来,我们将使用预测模型来检查结果和预测变量的变化。
最后,使用我们的分析模型结果开发报告(目标 2)。
将使用混合方法,包括利益相关者需求评估和迭代设计
我们的目标是推动测量科学超越整体的粗略测量。
设施和 VISN 绩效,以获得有关绩效可变性来源的更具可操作性的反馈。
这项工作将通过利用庞大的 VHA 数据基础设施来生成有效且可靠的数据,从而满足 LHCS 的需求。
可行的知识并将其有效地传达给最终用户,以提高退伍军人的护理质量。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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LAURA A PETERSEN其他文献
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{{ truncateString('LAURA A PETERSEN', 18)}}的其他基金
Medicaid Expansion and Quality, Utilization and Coordination of Health Care for Veterans with Chronic Kidney Disease
慢性肾病退伍军人医疗补助的扩展以及医疗保健的质量、利用和协调
- 批准号:
10335803 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Medicaid Expansion and Quality, Utilization and Coordination of Health Care for Veterans with Chronic Kidney Disease
慢性肾病退伍军人医疗补助的扩展以及医疗保健的质量、利用和协调
- 批准号:
10833998 - 财政年份:2021
- 资助金额:
-- - 项目类别:
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 - 财政年份:2017
- 资助金额:
-- - 项目类别:
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 - 财政年份:2017
- 资助金额:
-- - 项目类别:
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:
9287114 - 财政年份:2017
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