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 Factors influencing family physician adoption of electronic health records (EHRs) Factors influencing family physician adoption of electronic health records (EHRs) 2013 Topic(s) Role of Primary Care Keyword(s) Health Information Technology (HIT) Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine BACKGROUND: Physician and practice characteristics associated with family physician adoption of electronic health records (EHRs) remain largely unexplored but may be important for tailoring policies and interventions. METHODS: This was a cross-sectional study of EHR adoption using American Board of Family Medicine certification census data (2006-2011) for over 41,000 family physicians to test associations between demographic, geographic, and practice characteristics and EHR adoption. RESULTS: EHR adoption rates for family physicians grew from 37% in 2006 to 68% in 2011. No significant association was found with rural status (odds ration [OR], 0.985; 95% confidence interval [CI], 0.932-1.042). Practicing in a medically underserved location (OR, 0.868; 95% CI, 0.822-0.917) or geographic health professional shortage areas (OR, 0.904; 95% CI, 0.831-0.984), or being an international medical graduate (OR, 0.769; 95% CI, 0.748-0.846) were negatively associated with adoption. Compared with physicians in group practices, physicians in solo practices (OR, 0.465; 95% CI, 0.439-0.493) and small practices (OR, 0.769; 95% CI, 0.720-0.820) were less likely to adopt EHRs, whereas those in health maintenance organizations (OR, 5.482; 95% CI, 4.657-6.454) or with faculty status (OR, 1.527; 95% CI, 1.386-1.684) were more likely. CONCLUSIONS: Variation in EHR adoption is associated with physician and practice characteristics that may help guide intervention. These findings may be important to other specialties and could instruct interventions to improve adoption. Certification boards could play an important role in tracking EHR adoption and help target resources and facilitation. Read More ABFM Research Read all 2020 WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM Go to WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM 2020 Oral corticosteroid use, obesity, and ethnicity in children with asthma Go to Oral corticosteroid use, obesity, and ethnicity in children with asthma 2018 Adherence to clinical guidelines for monitoring diabetes in primary care settings. Go to Adherence to clinical guidelines for monitoring diabetes in primary care settings. 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
Topic(s) Role of Primary Care Keyword(s) Health Information Technology (HIT) Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2020 WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM Go to WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM 2020 Oral corticosteroid use, obesity, and ethnicity in children with asthma Go to Oral corticosteroid use, obesity, and ethnicity in children with asthma 2018 Adherence to clinical guidelines for monitoring diabetes in primary care settings. Go to Adherence to clinical guidelines for monitoring diabetes in primary care settings. 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
2020 WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM Go to WORKING TO ADVANCE THE HEALTH OF RURAL AMERICANS: AN UPDATE FROM THE ABFM
2020 Oral corticosteroid use, obesity, and ethnicity in children with asthma Go to Oral corticosteroid use, obesity, and ethnicity in children with asthma
2018 Adherence to clinical guidelines for monitoring diabetes in primary care settings. Go to Adherence to clinical guidelines for monitoring diabetes in primary care settings.
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