TY - JOUR
T1 - What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
AU - Young, Richard A.
AU - Martin, Carmel M.
AU - Sturmberg, Joachim P.
AU - Hall, Sally
AU - Bazemore, Andrew
AU - Kakadiaris, Ioannis A.
AU - Lin, Steven
N1 - Publisher Copyright:
© 2024 American Board of Family Medicine. All rights reserved.
PY - 2024/3
Y1 - 2024/3
N2 - Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.
AB - Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.
KW - Artificial Intelligence
KW - Clinical Decision-Making
KW - Complexity Science
KW - Information Technology
KW - Machine Learning
KW - Medical Informatics
KW - Primary Care Physicians
KW - Primary Health Care
KW - Quality Improvement
UR - http://www.scopus.com/inward/record.url?scp=85192913891&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192913891&partnerID=8YFLogxK
U2 - 10.3122/jabfm.2023.230219R1
DO - 10.3122/jabfm.2023.230219R1
M3 - Article
C2 - 38740483
AN - SCOPUS:85192913891
SN - 1557-2625
VL - 37
SP - 332
EP - 345
JO - Journal of the American Board of Family Medicine
JF - Journal of the American Board of Family Medicine
IS - 2
ER -