Risk-adjusted mortality rates as a potential outcome indicator for outpatient quality assessments.
ABSTRACT The quality of outpatient medical care is increasingly recognized as having an important impact on mortality. We examined whether a clinically credible risk adjustment methodology can be developed for outpatient quality assessments.
This study used data from the 1998 National Survey of Ambulatory Care Patients, a prospective monitoring system of outcomes of patients receiving ambulatory care in the Veterans Affairs (VA) integrated service networks.
Thirty-one thousand eight hundred twenty-three patients were followed for 18 months.
The main study outcome measures were observed and risk-adjusted mortality rates.
Of the 31,823 patients, 1559 (5%) died during the 18-months of follow-up. Observed mortality rates across the 22 VA integrated service networks varied significantly from 3.3% to 6.7% (P <0.001). Age, gender, comorbidities (Charlson Index), physical health, and mental health were significant predictors of dying. The resulting risk-adjusted mortality model performed well in cross-validated tests of discrimination (c-statistic = 0.768; 95% CI, 0.749-0.788) and calibration. Analysis of variance confirmed that the 22 integrated service networks differed in their average level of expected risk (P <0.001). Risk-adjusted rates and ranks of the networks differed considerably from unadjusted ratings.
Risk-adjusted mortality rates may be a useful outcome measure for assessing quality of outpatient care. We have developed a clinically credible risk adjustment model with good performance properties using sociodemographics, diagnoses, and functional status data. The resulting risk adjustment model altered assessments of the performance of the integrated service networks when compared with the unadjusted mortality rates.
- SourceAvailable from: Alfons Palangkaraya
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- "The first is to restrict the types of patients in the data, e.g., patients with acute myocardial infarction (AMI) are likely to be admitted to the nearest hospital rather than a hospital of choice due to the urgency of their conditions (Shen, 2003). A variant to this strategy is to account for the severity of patients' conditions in the sample using various medical status variables (Selim et al., 2002). "
ABSTRACT: This paper proposes a method of deriving a quality indicator for hospitals using mortality outcome measures. The method aggregates any number of mortality outcomes into a single indicator via a two-stage procedure. In the first stage, mortality outcomes are risk-adjusted using a system of seemingly unrelated regression equations. These risk-adjusted mortality rates are then aggregated into a single quality indicator in the second stage via weighted least squares. This method addresses the dimensionality problem in measuring hospital quality, which is multifaceted in nature. In addition, our method also facilitates further analyses of determinants of hospital quality by allowing the resulting quality estimates be associated with hospital characteristics. The method is applied to a sample of heart-disease episodes extracted from hospital administrative data from the state of Victoria, Australia. Using the quality estimates, we show that teaching hospitals and large regional hospitals provide higher quality of care than other hospitals and this superior performance is related to hospital case-load volume.Health Economics 01/2008; 19(12):1404-24. DOI:10.1002/hec.1560 · 2.14 Impact Factor