How Should Artificial Intelligence Be Used in Primary Care?

Artificial intelligence (AI) allows computers to imitate human cognitive functions, such as deep learning, problem solving, and creativity. In recent years, AI has prompted a series of innovations in the medical field. Clinical applications of AI are most advanced in image- and signal-intensive disciplines, including radiology, dermatology, and critical care. It's in these contexts that the performance of AI algorithms for many tasks now meets or exceeds that of individual clinicians.

Primary care supports the health of all members of society and is primed to realize the benefits of AI on a broad scale. Primary care electronic health records contain longitudinal data that span diseases, care settings, socioeconomic circumstances, and life experiences. Applications of AI to these and other linked data (eg, from wearable devices) can enable proactive care and clinical decision support in primary care.

Examples of primary care–targeted AI applications under development in the public sector include automating checks on clinical decisions in real time against chronic disease guidelines, detecting signs of dementia, and predicting outcomes such as nonelective hospitalizations. Despite optimism for the use of AI in primary care, there has been no comprehensive review of the contribution made by AI so far, and there is little guidance on how it should proceed.

Which Priorities?

A qualitative study gathered a diverse group of primary care physicians, patients, and other healthcare professionals, including health system leaders, to set out the priorities of AI when applied to the primary care setting. The study used deliberative dialogue, a participatory method initially developed to enhance deliberative democracy by gathering people affected by an issue to advise decision-makers. The method has been adapted for setting agendas in various contexts, including health system planning.

The authors identified the following three themes: (1) priority applications of AI in primary care, (2) the impact of AI on the roles of primary care providers, and (3) considerations for training healthcare providers in AI.

Shared values included health equity, patient-centered care, patient safety, accessibility, and care continuity. Patients and providers identified strikingly similar priority applications for AI and similar concerns about the impact of AI on care.

Priority Applications

Patients and providers agreed that AI applications that are of the highest priority in primary care involve support for clinical documentation, practice operations, and triage, as well as support for clinical decision-making.

However, patients and providers perceived applications in these areas as posing a high and immediate risk to patient safety, owing to unresolved problems of algorithmic bias and the current scarcity of evidence concerning the safety and efficacy of AI.

There was a prevailing sense of fear regarding triage tools, which, if designed and implemented without input from key stakeholders, could disrupt continuity or limit access to those unable to use technology.

Affecting Provider Roles

Regarding the impact of AI on the roles of healthcare providers, most patients and providers were unconvinced that AI will ever fully replace providers, especially in the context of clinical decision-making. This skepticism arose from the idea that the patient-provider relationship is intrinsically human and is at once the defining feature and enabling mechanism of patient-centered primary care.

Several participants were particularly pessimistic that AI tools could fairly or comprehensively consider social and economic factors that affect care.

Provider Training

Regarding AI and its impact on doctor training and skills instruction, there was widespread concern about the fact that future generations of providers could fall victim to deskilling (ie, declining proficiency over time resulting from automation of tasks) if either the design of AI applications or AI training for healthcare professionals does not preserve the core skills that promote patient safety or patient centrality.

Providers and system leaders identified the following three priority areas for formative and continuing professional education: basic AI literacy, critical appraisal of algorithms, and workflow integration.

The overall findings offer an agenda for applying AI in primary care that is grounded in the shared values of patients and providers. The authors of the research propose a new paradigm in which, from the conception phase, AI developers work with interdisciplinary teams that engage primary-care end users as design partners to develop AI-driven tools that respond to patients' and providers' most pressing, unmet needs.

This article was translated from Univadis Italy, which is part of the Medscape Professional Network.

Comments

Commenting is limited to medical professionals. To comment please Log-in.

Comments on Medscape are moderated and should be professional in tone and on topic. You must declare any conflicts of interest related to your comments and responses. Please see our Commenting Guide for further information. We reserve the right to remove posts at our sole discretion.