research Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination Read Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination
Phoenix Newsletter - March 2025 President’s Message: ABFM’s Unwavering Commitment to Diplomates and the Specialty Read President’s Message: ABFM’s Unwavering Commitment to Diplomates and the Specialty
A Conversation with Dr. Phillip Wagner “Family Medicine Was All I Ever Wanted to Do” Dr. Phillip Wagner Read “Family Medicine Was All I Ever Wanted to Do”
Home Research Research Library Adherence to clinical guidelines for monitoring diabetes in primary care settings. Adherence to clinical guidelines for monitoring diabetes in primary care settings. 2018 Author(s) Dai, Mingliang, Peabody, Michael R, Peterson, Lars E, and Mainous, Arch G III Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Quality Of Care Volume Family Medicine and Community Health Source Family Medicine and Community Health Objective: Adherence to clinical guidelines is key to improving diabetes care. Contemporary knowledge of guideline adherence is lacking. This study sought to produce a national snapshot of primary care physicians’ (PCPs) adherence to the American Diabetes Association guidelines for monitoring diabetes and determine whether continuity of care promotes adherence. Methods: Using the 2013 National Ambulatory Medical Care Survey, we examined adherence to ordering hemoglobin A1c (HbA1c) and lipid profile tests as recommended by the American Diabetes Association for monitoring diabetes in 2379 primary care visits of patient with diabetes. Results: In the preceding 12 months, less than 60.0% of the patients were given a test recommended for monitoring diabetes (58.0% for HbA1c and 57.0% for lipid profile). Continuity of care with PCPs increased the odds of adhering to diabetes monitoring guidelines by 36.0% for the HbA1c test (P=0.06) and by 76.0% for the lipid profile test (P=0.0006). Conclusion: A substantial gap exists in achieving optimal monitoring for diabetes in primary care settings in the United States. While PCPs are ideally positioned to ensure that guidelines are closely followed, we found that even in primary care settings, patient-provider continuity of care was associated with guideline adherence. Read More ABFM Research Read all 2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research 2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care 2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings 2016 Care Coordination for Primary Care Practice Go to Care Coordination for Primary Care Practice
Author(s) Dai, Mingliang, Peabody, Michael R, Peterson, Lars E, and Mainous, Arch G III Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Quality Of Care Volume Family Medicine and Community Health Source Family Medicine and Community Health
ABFM Research Read all 2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research 2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care 2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings 2016 Care Coordination for Primary Care Practice Go to Care Coordination for Primary Care Practice
2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research
2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings
