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 Community Vital Signs: Taking the Pulse of the Community While Caring for Patients Community Vital Signs: Taking the Pulse of the Community While Caring for Patients 2016 Author(s) Hughes, Lauren S, Phillips, Robert L, DeVoe, Jennifer E, and Bazemore, Andrew W Topic(s) Achieving Health System Goals Keyword(s) Population Health Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine In 2014 both the Institute of Medicine and the National Quality Forum recommended the inclusion of social determinants of health data in electronic health records (EHRs). Both entities primarily focus on collecting socioeconomic and health behavior data directly from individual patients. The burden of reliably, accurately, and consistently collecting such information is substantial, and it may take several years before a primary care team has actionable data available in its EHR. A more reliable and less burdensome approach to integrating clinical and social determinant data exists and is technologically feasible now. Community vital signs-aggregated community-level information about the neighborhoods in which our patients live, learn, work, and play-convey contextual social deprivation and associated chronic disease risks based on where patients live. Given widespread access to “big data” and geospatial technologies, community vital signs can be created by linking aggregated population health data with patient addresses in EHRs. These linked data, once imported into EHRs, are a readily available resource to help primary care practices understand the context in which their patients reside and achieve important health goals at the patient, population, and policy levels. Read More ABFM Research Read all 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 2020 Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine Go to Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine 2020 Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD Go to Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD 2021 Family Physician Burnout Does Not Differ With Rurality Go to Family Physician Burnout Does Not Differ With Rurality
Author(s) Hughes, Lauren S, Phillips, Robert L, DeVoe, Jennifer E, and Bazemore, Andrew W Topic(s) Achieving Health System Goals Keyword(s) Population Health Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 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 2020 Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine Go to Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine 2020 Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD Go to Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD 2021 Family Physician Burnout Does Not Differ With Rurality Go to Family Physician Burnout Does Not Differ With Rurality
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
2020 Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine Go to Advancing bibliometric assessment of research productivity: an analysis of US Departments of Family Medicine
2020 Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD Go to Family Leave for Family Residency Residents: Time for a Way Forward, from ABFM & AFMRD
2021 Family Physician Burnout Does Not Differ With Rurality Go to Family Physician Burnout Does Not Differ With Rurality