The VA has incorporated extensive diabetes measures in their External Peer Review Program (EPRP) quality measurement system, which is based on medical record abstraction.
While EPRP is VA’s gold standard and provides the most reliable diabetes quality information currently available, chart abstraction is very expensive. Certain process and intermediate outcome data (for example, whether a lab test was performed and its value) are available from the Veteran’s Integrated Health Systems Technology and Architecture (VISTA) system. However, the validity and reliability of these data are unknown.
The specific goals of the project include: (1) to examine the correlation of diabetes measures derived from VISTA measures with those derived from medical record and patient survey, and (2) to assess the ability of each of the measures to accurately identify facilities with higher or lower quality.
The project compares diabetic quality measures in the VA obtained from three existing data sources: VISTA, medical records and patient surveys (using data already collected by the VA Office of Quality and Performance (OQP) as part of a quality assurance activity). We have compare the quality measures derived from each data source to determine how they correlate and how each contributes to the variation in quality scores across facilities.
Success rates were higher for process measures derived from medical record versus automated data (e.g., 78% vs. 68% for LDL measured; 84% vs. 78% for A1c measured). This difference narrowed for intermediate outcome measures (e.g., 79% vs. 76% for LDL<130; 86% vs. 88% for A1c<9.5%). Agreement for measures derived from the medical record compared to automated data was moderate for process measures (e.g.,A1c measured, kappa=0.61) but high for intermediate outcome measures (e.g., A1c<9.5%, kappa=0.92). Hybrid measures, which use automated data supplemented with medical record data, yielded success rates similar to those of medical record based measures. Hybrid process measures would require medical record review in only 50% of cases. Process measures, but no intermediate outcome measures, showed significant variation attributable to the facility, regardless of the data source.
We found that agreement between medical record and automated data was generally high. Nonetheless automated data tended to underestimate the success rate in process measures for diabetes. Applying hybrid methodology yielded results consistent with the medical record but required less data to come from medical record reviews. Despite the high rates in overall performance, further research should examine the underlying reasons for facility level variation in diabetes process measures in order to craft appropriate quality improvement programs.
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- Kerr EA. Comparing Data Sources: Examining Diabetes Quality of Care. [Cyberseminar]. 2002 Dec 4.
- Kerr EA. Building a Better Quality Measure: Are some patients with 'poor quality' actually getting good care. Paper presented at: VA HSR&D National Meeting; 2003 Feb 13; Washington, DC.
- Kerr EA. Building a Better Quality Measure: How Can Measurement Help Us Close the Quality Gap? Paper presented at: AcademyHealth Annual Research Meeting; 2002 Jun 23; Washington, DC.
- Kerr EA. Comparing Diabetes Quality Measures Derived from Different Data Sources. Paper presented at: Society of General Internal Medicine Annual Meeting; 2002 May 3; Atlanta, GA.
- Kerr EA. What Do Our Veterans Tell Us About Their Care. Paper presented at: Diabetes National Annual Symposium; 2002 Mar 21; Alexandria, VA.
- Kerr EA. The influence of health status of veterans with diabetes: Results from the VA Diabetes Quality Improvement Project. Paper presented at: VA HSR&D National Meeting; 2000 Feb 1; Washington, DC.
Technology Development and Assessment
Diabetes, Quality assessment, Risk adjustment