Artificial intelligence and health inequities in primary care: a systematic scoping review and framework. (30th November 2022)

• Record Type: Journal Article
• Authors: d'Elia, Alexander; Gabbay, Mark; Rodgers, Sarah; Kierans, Ciara; Jones, Elisa; Durrani, Irum; Thomas, Adele; Frith, Lucy
• Is Part Of: Family medicine and community health. Volume 10(2022)Supplement 1
• Publication Date: 2022-11-30
• Subjects: Health Equity -- General Practice -- Healthcare Disparities; Family medicine -- Periodicals; Public health -- Periodicals; Family medicine; Public health; Family Practice; Community Health Services; General Practice; Electronic journals; Periodicals; Periodical; 610.5
• DOI: 10.1136/fmch-2022-001670
• Languages: English
• ISSNs: 2305-6983

Abstract

Abstract : Objective: Artificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest challenges. Primary care is both a driver and a mitigator of health inequities and with AI gaining traction in primary care, there is a need for a holistic understanding of how AI affect health inequities, through the act of providing care and through potential system effects. This paper presents a systematic scoping review of the ways AI implementation in primary care may impact health inequity. Design: Following a systematic scoping review approach, we searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. In addition, articles from primary exploratory searches were added, and through reference screening. The results were thematically summarised and used to produce both a narrative and conceptual model for the mechanisms by which social determinants of health and AI in primary care could interact to either improve or worsen health inequities. Two public advisors were involved in the review process. Eligibility criteria: Peer-reviewed publications and grey literature in English and Scandinavian languages. Information sources: PubMed, SCOPUS and JSTOR. Results: A total of 1529 publications were identified, of which 86 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: (1) access, (2) trust, (3) dehumanisation, (4) agency for self-care, (5) algorithmic bias and (6) external effects. The five first domains cover aspects of the interface between the patient and the primary care system, while the last domain covers care system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI. Conclusion: AI has the potential to affect health inequities through a multitude of ways, both directly in the patient consultation and through transformative system effects. This review summarises these effects from a system tive and provides a base for future research into responsible implementation.