AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence

TL;DR

This paper discusses the need for safer and more equitable AI triage systems in primary care by evaluating real-world outcomes and addressing disparities.

Contribution

The paper introduces a sociotechnical approach to evaluate AI triage in primary care with a focus on equity and real-world safety.

Findings

Current AI triage evidence lacks real-world outcome evaluations and equity-stratified safety data.

Deployment risks include algorithmic bias, workflow misalignment, and governance gaps.

Real-world evaluations and postmarket surveillance are essential to prevent worsening health inequalities.

Abstract

Artificial intelligence triage in general practice is developing rapidly within the primary care digital transformation, promising efficiency gains and safety standardization in overwhelmed primary care systems. However, current evidence is drawn from retrospective validations, emergency settings, or vignettes, with scant evaluation of real-world outcomes and almost no equity-stratified safety data, despite known disparities across age, ethnicity, language, and deprivation. From a sociotechnical standpoint, which considers the fit between people, tasks, technology, and organizational context, risks arise not only from algorithmic bias and undertriage but also from human factors, workflow misalignment, governance gaps, and inadequate postdeployment monitoring. We argue that ensuring artificial intelligence triage is safe and equitable requires real-world evaluations in primary care settings, equity-focused performance reporting using theoretically informed frameworks, and rigorous postmarket surveillance. Without these, deployment may widen existing health inequalities rather than moderate them.

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