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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：
9287114
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
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：
https://reporter.nih.gov/project-details/9287114
关键词：
Accountability Accountability Address Address Administrator Administrator Adverse event Adverse event Affect Affect Benchmarking Benchmarking Care given by nurses Care given by nurses Caregivers Caregivers Caring Caring Climate Climate Clinical Clinical Complex Complex Computing Methodologies Computing Methodologies Data Data Data Display Data Display Decision Making Decision Making Decubitus ulcer Decubitus ulcer Dimensions Dimensions Discipline of Nursing Discipline of Nursing Ecological Bias Ecological Bias Education Education Employee Employee Ensure Ensure Equation Equation Evaluation Evaluation Feedback Feedback Fostering Fostering Goals Goals Health Services Accessibility Health Status Health Status Health system Health system Healthcare Healthcare Healthcare Systems Healthcare Systems Hospital Units Hospital Units Hospitals Hospitals Human Human Industrial Psychology Industrial Psychology Industrialization Industrialization Infrastructure Inpatients Inpatients Intervention Intervention Knowledge Knowledge Leadership Leadership Learning Learning Literature Literature Measurement Measurement Measures Measures Methods Methods Modeling Modeling Needs Assessment Needs Assessment Noise Noise Nosocomial Infections Nosocomial Infections Nurses Nurses Outcome Outcome Outcome Measure Outcome Measure Patient-Focused Outcomes Patient-Focused Outcomes Patients Patients Pattern Pattern Performance Performance Process Process Process Measure Process Measure Productivity Productivity Psychology Psychology Quality of Care Quality of Care Reporting Reporting Research Research Research Infrastructure Resources Resources Science Science Services Services Signal Transduction Signal Transduction Site Site Source Source Statistical Methods Statistical Methods Statistical Models Statistical Models Structure Structure Students Students System System Techniques Techniques Testing Testing Time Time Translating Translating Validity and Reliability Validity and Reliability Variant Variant Veterans Veterans Work Work base base burden of illness burden of illness computerized data processing computerized data processing data resource data resource data warehouse design design evidence base evidence base experience experience falls falls health care quality health care quality hospital readmission improved improved insight insight iterative design iterative design mortality mortality multilevel analysis multilevel analysis novel novel outcome prediction outcome prediction peer peer performance tests performance tests predict clinical outcome predict clinical outcome predictive modeling predictive modeling prototype prototype readmission rates satisfaction satisfaction stem stem tool tool usability usability

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.

为了发展学习型医疗保健系统（LHCS）， VHA领导层必须了解质量