Measuring the quality of diabetes care using administrative data: is there bias?
OBJECTIVE
Health care organizations often measure processes of care using only administrative data. We assessed whether measuring processes of diabetes care using administrative data without medical record data is likely to underdetect compliance with accepted standards for certain groups of patients.


METHODS
Assessment of quality indicators during 1998 using administrative and medical records data for a cohort of 1,335 diabetic patients enrolled in three Minnesota health plans.


METHODS
Cross-sectional retrospective study assessing hemoglobin A1c testing, LDL cholesterol testing, and retinopathy screening from the two data sources. Analyses examined whether patient or clinic characteristics were associated with underdetection of quality indicators when administrative data were not supplemented with medical record data.


METHODS
The health plans provided administrative data, and trained abstractors collected medical records data.


RESULTS
Quality indicators that would be identified if administrative data were supplemented with medical records data are often not identified using administrative data alone. In adjusted analyses, older patients were more likely to have hemoglobin A1c testing underdetected in administrative data (compared to patients <45 years, OR 2.95, 95 percent CI 1.09 to 7.96 for patients 65 to 74 years, and OR 4.20, 95 percent CI 1.81 to 9.77 for patients 75 years and older). Black patients were more likely than white patients to have retinopathy screening underdetected using administrative data (2.57, 95 percent CI 1.16 to 5.70). Patients in different health plans also differed in the likelihood of having quality indicators underdetected.


CONCLUSIONS
Diabetes quality indicators may be underdetected more frequently for elderly and black patients and the physicians, clinics, and plans who care for such patients when quality measurement is based on administrative data alone. This suggests that providers who care for such patients may be disproportionately affected by public release of such data or by its use in determining the magnitude of financial incentives.
Keating
Nancy L
NL
Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA.
Landrum
Mary Beth
MB
Landon
Bruce E
BE
Ayanian
John Z
JZ
Borbas
Catherine
C
Guadagnoli
Edward
E
D000328:Adult; D000368:Aged; D015982:Bias; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000628:therapy*; D005260:Female; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods; D006801:Humans; D008297:Male; D008499:Medical Records; D008875:Middle Aged; D008910:Minnesota; D017064:Process Assessment, Health Care / Q000379:methods*; D019984:Quality Indicators, Health Care; D012189:Retrospective Studies