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
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 Expansion of coverage under the Patient Protection and Affordable Care Act and primary care utilization Competencies for the Use of Artificial Intelligence in Primary Care 2022 Author(s) Liaw, Winston R, Kueper, Jacqueline K, Lin, Steven, Bazemore, Andrew W, and Kakadiaris, Ioannis A Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Cost Of Care, Health Information Technology (HIT), Physician Experience (Burnout / Satisfaction), and Quality Of Care Volume Annals of Family Medicine Source Annals of Family Medicine The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician wellbeing), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the “side effects” of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits. Read More ABFM Research Read all 2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS 1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries
Author(s) Liaw, Winston R, Kueper, Jacqueline K, Lin, Steven, Bazemore, Andrew W, and Kakadiaris, Ioannis A Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Cost Of Care, Health Information Technology (HIT), Physician Experience (Burnout / Satisfaction), and Quality Of Care Volume Annals of Family Medicine Source Annals of Family Medicine
ABFM Research Read all 2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS 1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries
2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS
1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries
