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
Diplomate Spotlight “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 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models 2023 Author(s) Fouladvand, Sajjad, Talbert, Jeffery, Dwoskin, Linda P, Bush, Heather, Meadows, Amy L, Peterson, Lars E, Mishra, Yash R, Roggenkamp, Steven K, Wang, Fei, Kavuluru, Ramakanth, and Chen, Jin Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Practice Innovations Volume IEEE Journal of Biomedical and Health Informatics Source IEEE Journal of Biomedical and Health Informatics Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least one diagnosis of OUD or at least one prescription for buprenorphine or methadone. Controls are prescription opioid users with no OUD diagnoses or buprenorphine or methadone prescriptions. On 100 randomly selected test sets including 47,396 patients, our proposed transformer-based AI model can predict OUD more efficiently (AUC=0.742 ±0.021) compared to logistic regression (AUC=0.651 ±0.025), random forest (AUC=0.679 ±0.026), xgboost (AUC=0.690 ±0.027), long short-term memory model (AUC=0.706 ±0.026), transformer (AUC=0.725 ±0.024), and unweighted ORT model (AUC=0.559 ±0.025). Our results show that embedding AI algorithms into clinical care may assist clinicians in risk stratification and management of patients receiving opioid therapy. Read More ABFM Research Read all 2016 Access to Primary Care in US Counties Is Associated with Lower Obesity Rates Go to Access to Primary Care in US Counties Is Associated with Lower Obesity Rates 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 Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices. Go to Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices. 2025 The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project Go to The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project
Author(s) Fouladvand, Sajjad, Talbert, Jeffery, Dwoskin, Linda P, Bush, Heather, Meadows, Amy L, Peterson, Lars E, Mishra, Yash R, Roggenkamp, Steven K, Wang, Fei, Kavuluru, Ramakanth, and Chen, Jin Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Practice Innovations Volume IEEE Journal of Biomedical and Health Informatics Source IEEE Journal of Biomedical and Health Informatics
ABFM Research Read all 2016 Access to Primary Care in US Counties Is Associated with Lower Obesity Rates Go to Access to Primary Care in US Counties Is Associated with Lower Obesity Rates 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 Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices. Go to Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices. 2025 The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project Go to The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project
2016 Access to Primary Care in US Counties Is Associated with Lower Obesity Rates Go to Access to Primary Care in US Counties Is Associated with Lower Obesity Rates
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 Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices. Go to Meaningful Use And Medical Home Functionality In Primary Care Practice: Study examines the implementation of patient-centered medical home model and the adoption and meaningful use of health information technology in US physician practices.
2025 The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project Go to The Association Between Residency Characteristics and Graduates Caring for Children: A Family Medicine Residency Outcomes Project