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Objectives: We evaluated the influence of the introduction of a pay-for-performance program implemented in 2010 for family physicians on the glycemic control of patients with diabetes., Methods: Administrative data for all 583 eligible family physicians and 83,580 adult patients with diabetes in New Brunswick over 10 years were used. We compared the probability of receiving at least 2 tests for glycated hemoglobin (A1C) levels and achieving glycemic control before (2005-2009) and after (2010-2014) the implementation of the program and between patients divided based on whether a physician claimed the incentive or did not., Results: Patients living with diabetes showed greater odds of receiving at least 2 A1C tests per year if the detection of their diabetes occurred after (vs. before) the implementation of the program (OR, 99% CI=1.23, 1.18 to 1.28), if a physician claimed the incentive (vs. not claiming it) for their care (1.92, 1.87 to 1.96) in the given year, and if they were followed by a physician who ever (vs. never) claimed the incentive (1.24, 1.15 to 1.34). In a cohort-based analysis, patients for whom an incentive was claimed (vs. not claimed) had greater odds of receiving at least 2 A1C tests per year before implementation of the incentive, and these odds increased by 56% (1.49 to 1.62) following its implementation. However, there was no difference in A1C values among the various comparison groups., Conclusions: Introduction of the incentive was associated with greater odds of having a minimum of 2 A1C tests per year, which may suggest that it led physicians to provide better follow-up care for patients with diabetes. However, the incentive program has not been associated with differences in glycemic control., (Copyright © 2016 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

With the advent of the digitization of large amounts of information and the computer power capable of analyzing this volume of information, data mining is increasingly being applied to medical research. Datasets created for administration of the healthcare system provide a wealth of information from different healthcare sectors, and Canadian provinces' single-payer universal healthcare systems mean that data are more comprehensive and complete in this country than in many other jurisdictions. The increasing ability to also link clinical information, such as electronic medical records, laboratory test results and disease registries, has broadened the types of data available for analysis. Data-mining methods have been used in many different areas of diabetes clinical research, including classic epidemiology, effectiveness research, population health and health services research. Although methodologic challenges and privacy concerns remain important barriers to using these techniques, data mining remains a powerful tool for clinical research., (Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)