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

> **NIH VA I01** · MICHAEL E DEBAKEY VA MEDICAL CENTER · 2021 · —

## Abstract

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 quali...

## Key facts

- **NIH application ID:** 10186492
- **Project number:** 5I01HX002034-04
- **Recipient organization:** MICHAEL E DEBAKEY VA MEDICAL CENTER
- **Principal Investigator:** LAURA A PETERSEN
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2017-06-01 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186492

## Citation

> US National Institutes of Health, RePORTER application 10186492, Improving the Measurement of VA Facility Performance to Foster a Learning Healthcare System (5I01HX002034-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10186492. Licensed CC0.

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