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Home Research Research Library Competencies for the Use of Artificial Intelligence in Primary Care 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. ABFM Research Read all 1990 Prenatal care–a serious national dilemma Go to Prenatal care–a serious national dilemma 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2013 The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training Go to The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
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 1990 Prenatal care–a serious national dilemma Go to Prenatal care–a serious national dilemma 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2013 The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training Go to The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care
2013 The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training Go to The redistribution of graduate medical education positions in 2005 failed to boost primary care or rural training
2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
