Original articlesThe Use of Automated Data to Identify Complications and Comorbidities of Diabetes: A Validation Study
Introduction
Diabetes imposes a tremendous burden on individuals and health care systems owing in part to increased morbidity and mortality from a wide range of complications [1]. Automated data hold promise as a cost-effective and efficient mechanism for identifying these complications [2]. The ability to identify complications and comorbidity using administrative data would provide an important resource for tracking disease burden related to these conditions, selecting high-risk patients for intensive intervention, and evaluating the effect of changes in clinical management strategies and medication regimens on outcomes over time.
Automated data can also be a valuable resource for diabetes research [3]. Identifying complications and comorbidities for epidemiologic and health services research often requires time-intensive and costly efforts involving medical records review and individual patient follow-up. The use of automated data to identify patients with selected complications or outcomes would be a cost-effective approach for use in case-control and other studies [4]. Although automated data have been utilized to identify complications and outcomes of diabetes [5], with the exception of coronary heart disease [6] little is known about the validity of this approach. Having established a registry of enrollees with diabetes at a large staff-model health maintenance organization (HMO) in western Washington, we sought to determine whether administrative data sources could be used to identify validly the complications of diabetes.
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Methods
Group Health Cooperative of Puget Sound (GHC) is a staff-model HMO in western Washington with approximately 400,000 enrollees. We identified a cohort of 8905 patients aged 18 and older, with type I or type II diabetes, who were continuously enrolled from January 1, 1992, through March 31, 1996, or until their death. The algorithm for identifying enrollees as having diabetes was adapted from that used for the Diabetes Patient Outcomes Research Team (PORT) Study [7]. Patients were defined as
Results
Almost all requested medical records sampled for the validation study (97.7%) were located. The majority of enrollees in the validation sample were older than 65 years of age, white, and had a duration of diabetes greater than 10 years (Table 2). Diabetes was confirmed in the medical record for 90.4% of reviewed charts. The laboratory data of the 37 subjects identified from the algorithm, without documentation of diabetes in the medical record, were reviewed (KMN). In 22 cases (59.5%), there
Discussion
Computerized databases maintained by HMOs include an enormous variety of information including administrative data for billing, accounting and utilization of services such as office visits and hospitalizations with coded diagnoses, and data including laboratory and pathology test results and pharmacy utilization. These data are a rich resource for program and policy analyses and for health services, economic, outcomes, and epidemiologic research. The advantages of such data include ready
Acknowledgements
The funding source for this work was an unrestricted grant from Parke-Davis, Inc.
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