Beyond the Clinic Family Medicine on a Mission Part 1: How Air Force Physicians Achieve Humanitarian Goals Read Family Medicine on a Mission Part 1: How Air Force Physicians Achieve Humanitarian Goals
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
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 2024 Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology Go to Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology 2016 The Generation in Between: A Perspective from the Keystone IV Conference Go to The Generation in Between: A Perspective from the Keystone IV Conference 2016 Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All? Go to Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All? 2013 The rise of electronic health record adoption among family physicians Go to The rise of electronic health record adoption among family physicians
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 2024 Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology Go to Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology 2016 The Generation in Between: A Perspective from the Keystone IV Conference Go to The Generation in Between: A Perspective from the Keystone IV Conference 2016 Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All? Go to Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All? 2013 The rise of electronic health record adoption among family physicians Go to The rise of electronic health record adoption among family physicians
2024 Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology Go to Primary Care Physicians’ Satisfaction With Interoperable Health Information Technology
2016 The Generation in Between: A Perspective from the Keystone IV Conference Go to The Generation in Between: A Perspective from the Keystone IV Conference
2016 Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All? Go to Reducing Burden, Assuring Competence, Improving Quality, and Enhancing Professionalism: How Can Abfm Contribute to All?
2013 The rise of electronic health record adoption among family physicians Go to The rise of electronic health record adoption among family physicians
