Measuring the Quality of Diabetes Care Using Administrative Data: Is There Bias?


  • Nancy L. Keating,

    Search for more papers by this author
    • Address correspondence to Nancy L. Keating, M.D., M.P.H., Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115. Dr. Keating is also with the Division of General Internal Medicine (Section on Health Services and Policy Research), Department of Medicine, Brigham and Women's Hospital, as is John Z. Ayanian, M.D., M.P.P. With the Department of Health Care Policy, Harvard Medical School, are Drs. Keating and Ayanian, as well as Mary Beth Landrum, Ph.D., Bruce E. Landon, M.D., M.Sc., and Edward Guadagnoli, Ph.D. Dr. Landon is also with the Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center. Catherine Borbas, Ph.D., M.P.H., is with the Healthcare Evaluation and Research Foundation, Inc., St. Paul, MN.

  • Mary Beth Landrum,

  • Bruce E. Landon,

  • John Z. Ayanian,

  • Catherine Borbas,

  • Edward Guadagnoli

  • This work was supported by grant no. HS09936 from the Agency for Healthcare Research and Quality and grant no. HS98-005 from the American Association of Health Plans. Additional support was provided by the Marshall J. Seidman Center for Studies in Health Economics and Health Policy.


Objectives. 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.

Data Sources/Study Setting. 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.

Study Design. 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.

Data Collection/Extraction Methods. The health plans provided administrative data, and trained abstractors collected medical records data.

Principal Findings. 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.