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Home Research Research Library Improving quality of care for diabetes through a maintenance of certification activity: family physicians’ use of the chronic care model Improving quality of care for diabetes through a maintenance of certification activity: family physicians’ use of the chronic care model 2014 Topic(s) Achieving Health System Goals, Family Medicine Certification, and Role of Primary Care INTRODUCTION: Improving the care of patients with diabetes is a health care priority. Through Part 4 of Maintenance of Certification for Family Physicians (MC-FP), American Board of Family Medicine (ABFM) diplomates participate in quality improvement (QI) modules for diabetes. Our objective was to determine associations between physician characteristics and actions taken during Part 4 diabetes modules with quality of care outcomes. METHODS: The study sample was all Part 4 modules completed by family physicians from 2005 to 2012. Descriptive statistics were used to characterize the physicians and their behavior in the module. We used linear regression to test for associations between choice of intervention, mode of intervention, and chronic care model domain with improvement in quality measures. RESULTS: There were 7924 modules completed by family physicians, whose mean age was 48.2 years; 61.9% were male, and 76.9% lived in urban areas. All physician and patient quality measures improved over the course of the Part 4 module. Regression models found that only baseline performance was consistently associated with quality outcomes. No other consistent association was seen between intervention type, mode, or chronic care model domain and greater likelihood of improvements; however, every quality measure improved. DISCUSSION: Through MC-FP, family physicians improved the quality of care they delivered to diabetic patients. Improvement of care across nearly all measures, despite no consistent associations between processes of care or physician characteristics with improvement, suggests that participation in QI itself may lead to higher quality health care and this may be achieved through MC-FP. Read More ABFM Research Read all 2014 States Can Transform Their Health Care Workforce Go to States Can Transform Their Health Care Workforce 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2020 Trends in the Gender Ratio of Authorship at the Robert Graham Center Go to Trends in the Gender Ratio of Authorship at the Robert Graham Center 2024 Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research Go to Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research
ABFM Research Read all 2014 States Can Transform Their Health Care Workforce Go to States Can Transform Their Health Care Workforce 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2020 Trends in the Gender Ratio of Authorship at the Robert Graham Center Go to Trends in the Gender Ratio of Authorship at the Robert Graham Center 2024 Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research Go to Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research
2014 States Can Transform Their Health Care Workforce Go to States Can Transform Their Health Care Workforce
2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning
2020 Trends in the Gender Ratio of Authorship at the Robert Graham Center Go to Trends in the Gender Ratio of Authorship at the Robert Graham Center
2024 Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research Go to Celebrating 25 Years of High-Quality Family Medicine and Primary Care Policy Research