What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care

Richard A. Young, Carmel M. Martin, Joachim P. Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A. Kakadiaris, Steven Lin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)332-345
Number of pages14
JournalJournal of the American Board of Family Medicine
Volume37
Issue number2
DOIs
StatePublished - Mar 2024

Keywords

  • Artificial Intelligence
  • Clinical Decision-Making
  • Complexity Science
  • Information Technology
  • Machine Learning
  • Medical Informatics
  • Primary Care Physicians
  • Primary Health Care
  • Quality Improvement

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Family Practice

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