Reimagining Primary Care With AI: A Future Within Reach
In this new era of artificial intelligence (AI), primary care is poised to spearhead the innovation frontier in healthcare. As the largest healthcare delivery platform responsible for more care than all other specialties combined, primary care is uniquely positioned to harness AI’s potential to reimagine care delivery, streamline workflows, enhance clinical decision–making, and reduce administrative burdens.1
Choosing AI use cases for primary care
Within primary care, AI has the potential to revolutionize four key areas: clinical decision support, patient triage, care coordination, and workflow automation. By harnessing the latest clinical guidelines and evidence, AI can provide more sophisticated clinical decision support. AI-enabled tools for patient triage categorize patients by level of risk, enabling targeted clinical interventions. The time-consuming nature of care coordination can be eased with AI, which can create personalized after-visit instructions, providing patients with more relevant, comprehensive, and actionable intra-visit guidance. Moreover, with the exponential growth of data in the electronic health record and the challenge of integrating disparate data sources, AI can help busy clinicians automate burdensome tasks like chart summarization and clinical documentation.
When integrating AI into primary care, it’s crucial to adopt a quality-improvement approach,2 focusing on clearly understanding the problems that AI solutions can address, choosing AI applications that are high-value yet low-risk, and ensuring AI use cases support the quintuple aim3 to enhance healthcare delivery. One example that fits these criteria is AI-generated clinical documentation, which holds the promise of improving the clinician and patient experience by streamlining clinical workflows, reducing administrative burden and thereby allowing more direct interactions between patients and primary care providers.
Navigating challenges
While AI offers significant opportunities for enhancing primary care, it also poses challenges, including safeguarding data privacy, addressing health disparities, mitigating the risk of bias,4 and evaluating the impact of real-world deployments.5 Primary care providers play an important role in educating their peers, patients, and the broader healthcare community about the nuances of AI implementation in medicine. This includes an ethical responsibility to inform, educate, and navigate patients through AI in clinical care, which is essential in building and maintaining therapeutic trust in these emerging technologies.
Envisioning the future
We are approaching a future where primary care teams can proactively pinpoint patients in need of guideline-directed interventions, review chart summaries designed specifically for primary care, refine AI-generated drafts of personalized patient instructions and visit notes, and prescribe follow-ups with an AI-powered evidence-based care program. The goal of these technologies is not to supplant human providers but to augment them with the aim of fostering the deeper human-to-human connections at the core of care delivery.
Many of these technologies exist today, but implementation has lagged behind innovation. Primary care providers and researchers play a critical role in ensuring that these technologies are fit for purpose. Primary care must lead the charge in integrating AI to mold the future of healthcare delivery. By embracing AI, primary care can pioneer a more proactive, personalized, and efficient approach to patient care.
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Additional Info
- Lin S. A Clinician’s Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution. J Am Board Fam Med. 2022;35(1):175-184.
- Smith M, Sattler A, Hong G, Lin S. From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods. J Gen Intern Med. 2021;36(4):1061-1066.
- Nundy S, Cooper LA, Mate KS. The Quintuple Aim for Health Care Improvement: A New Imperative to Advance Health Equity. JAMA. 2022;327(6):521-522.
- National Academy of Medicine; National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Committee on Creating a Framework for Emerging Science, Technology, and Innovation in Health and Medicine. Toward Equitable Innovation in Health and Medicine: A Framework. Washington (DC): National Academies Press (US); 2023 Aug 22.
- Shah NH, Entwistle D, Pfeffer MA. Creation and Adoption of Large Language Models in Medicine. JAMA. 2023;330(9):866-869.
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