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 The Generation in Between: A Perspective from the Keystone IV Conference The Generation in Between: A Perspective from the Keystone IV Conference 2016 Author(s) Chen, Frederick M, Bliss, Erika B, Dunn, A, Edgoose, J, Elliott, T C, Maxwell, L C, Morris, C G, and Phillips, Robert L Topic(s) Achieving Health System Goals Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine Keystone IV affirmed the value of relationships in family medicine, but each generation of family physicians took away different impressions and lessons. “Generation III,” between the Baby Boomers and Millennials, reported conflict between their professional ideal of family medicine and the realities of current practice. But the Keystone conference also helped them appreciate core values of family medicine, their shared experience, and new opportunities for leadership. Read More ABFM Research Read all 2015 A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care Go to A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care 2021 How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care Go to How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care 2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by 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
Author(s) Chen, Frederick M, Bliss, Erika B, Dunn, A, Edgoose, J, Elliott, T C, Maxwell, L C, Morris, C G, and Phillips, Robert L Topic(s) Achieving Health System Goals Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2015 A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care Go to A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care 2021 How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care Go to How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care 2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by 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
2015 A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care Go to A Family Medicine Health Technology Strategy for Achieving the Triple Aim for US Health Care
2021 How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care Go to How Comprehensive Medication Management Contributes to Foundational Elements of Primary Care
2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by 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
