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BACKGROUND/RATIONALE:
The VHA has been a leader in using quality measures to provide incentives and improve quality of care, and is expanding this with "pay-for- performance" programs. Current measures of quality include measures of processes of care or dichotomized intermediate clinical outcomes. However, there is no metric for comparing the impact of performance across measures, as there is no consideration of the magnitude of the link between measures and long-term outcomes. Further, the dichotomization of measures provides an incentive to make small improvements in those close to goals rather than focusing on those who are farthest from goal and most likely to benefit. Additionally, provider panel sizes for a condition are a detriment to reliable provider profiling; for example, hemoglobin A1c, a measure of diabetes control, is not reliable for profiling at the typical patient panel sizes within VHA. There is a significant need for a composite metric of quality that can assign appropriate weights to individual measures and can be compared across and within disease processes.
OBJECTIVE(S):
We propose a study to develop a measure to examine the use of quality-adjusted life years (QALYs) as a quality measure. The specific aims are as follows:
1)To use administrative and profiling data to model individual level QALYs across a spectrum of VA patients, and to develop a metric that allows a more refined measure of quality of care
2)To examine provider and site level variation in QALYs lost to examine its utility as a profiling tool
3)To examine the relative benefits and possible weights for individual quality measures
METHODS:
We will use VHA administrative and EPRP data to examine quality of care for several key conditions: ischemic heart disease (including hypertension and hyperlipidemia); diabetes; colon cancer screening; and depression screening. Values for each condition will be used as inputs into previously published simulation models that will generate patient level QALYs. We will then run the same simulations using idealized goals to QALYs under ideal care. The difference between observed and ideal QALYs ("lost QALYs") will be the primary outcome measure. We will use the lost QALYs as dependent variables in a series of regression analyses. First, we will examine, using linear regression, the within-disease predictors of QALYs lost; for example, in diabetes, we can compare the effects of blood pressure vs. glycemic control. We will develop risk-adjustment models using age and administrative comorbidity measures to control for effects outside of provider control. Coefficients from this regression can provide a metric to weight the effects of each measure. Second, we will examine, using hierarchical regression modeling, levels of clustering and proportion of explained variance in lost QALYs at the regional, site, and provider level. This will allow examination of the reliability of profiling using lost QALYs.
FINDINGS/RESULTS:
No results at this time.
IMPACT:
Enter text here.
PUBLICATIONS:
None at this time.
DRA:
Health Services and Systems
DRE:
Quality of Care
Keywords:
Quality assessment, Quality of life, Research measure
MeSH Terms:
none
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