Address correspondence to Baiju R. Shah, M.D., Ph.D., Institute for Clinical Evaluative Sciences, G106–2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5. Janet E. Hux, M.D., S.M., Andreas Laupacis M.D., M.Sc., Karen Cauch-Dudek, B.A., and Gillian L. Booth, M.D., M.Sc., are with the Institute for Clinical Evaluative Sciences, Toronto, ON, Canada. Bernard Zinman M.D.C.M., is with the Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, University Avenue Toronto, ON, Canada.
Administrative Data Algorithms Can Describe Ambulatory Physician Utilization
Article first published online: 24 JAN 2007
Health Services Research
Volume 42, Issue 4, pages 1783–1796, August 2007
How to Cite
Shah, B. R., Hux, J. E., Laupacis, A., Zinman, B., Cauch-Dudek, K. and Booth, G. L. (2007), Administrative Data Algorithms Can Describe Ambulatory Physician Utilization. Health Services Research, 42: 1783–1796. doi: 10.1111/j.1475-6773.2006.00681.x
- Issue published online: 24 JAN 2007
- Article first published online: 24 JAN 2007
- Validation studies;
- specialist care;
- chronic disease care;
- administrative data;
- primary care;
- diabetes mellitus
Objective. To validate algorithms using administrative data that characterize ambulatory physician care for patients with a chronic disease.
Data Sources. Seven-hundred and eighty-one people with diabetes were recruited mostly from community pharmacies to complete a written questionnaire about their physician utilization in 2002. These data were linked with administrative databases detailing health service utilization.
Study Design. An administrative data algorithm was defined that identified whether or not patients received specialist care, and it was tested for agreement with self-report. Other algorithms, which assigned each patient to a primary care and specialist physician, were tested for concordance with self-reported regular providers of care.
Principal Findings. The algorithm to identify whether participants received specialist care had 80.4 percent agreement with questionnaire responses (κ=0.59). Compared with self-report, administrative data had a sensitivity of 68.9 percent and specificity 88.3 percent for identifying specialist care. The best administrative data algorithm to assign each participant's regular primary care and specialist providers was concordant with self-report in 82.6 and 78.2 percent of cases, respectively.
Conclusions. Administrative data algorithms can accurately match self-reported ambulatory physician utilization.