Introduction
What is new?
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Identification of diabetics in Canada at a population level has historically relied on physician billing and hospitalization administrative data. With the uptake of electronic medical records (EMRs), more detailed clinical information is available, but methods to identify patients with particular disease conditions are needed.
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Using a combination of prescriptions for antidiabetic medications and laboratory tests results, diabetics can be identified within an EMR with accuracy similar to administrative data.
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Use of these structured variables within an EMR will allow for easy identification of diabetics within a large population with an EMR record.
In Canada, the identification of diabetics within a population has traditionally relied on administrative data, namely, a combination of physician billing data and hospital discharge abstracts [1], [2]. Although administrative data is useful for monitoring prevalence and incidence within large populations, the lack of availability of comprehensive clinical information limits its utility in measuring more detailed aspects of diabetes management and care. With the increasing use of electronic medical records (EMRs), identification of diabetics within an EMR could greatly aid physicians in monitoring the management of this complex chronic disease as prescriptions and laboratory results are readily available.
The use of EMRs in Canada, although growing rapidly, is only in its infancy compared with many other countries. With approximately 26% uptake of EMRs in Canada [3], dozens of vendors and no physician incentives to enter or code data in a standardized format, the use of EMR data for research purposes may be challenging [4]. Thus methods to identify patients with particular disease conditions within an EMR need to be developed.
Searching the cumulative patient profile of a primary care EMR to identify diabetics theoretically should be a straightforward process. However, physicians vary in the degree and manner in which they populate their EMRs, and they often use different acronyms. This inconsistency can increase the complexity of identifying diabetics through simple text searching. Diabetes is a condition with discrete laboratory value tests that can make a definitive diagnosis, and medications for treatment are fairly specific. Thus, the nature of this condition may lend itself well to identification of affected patients through analysis of structured data within an EMR.
Using EMR data that is linked to administrative data, we set out to determine if diabetics could be identified through structured EMR data and/or more accurately than administrative data.