Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?

Abstract

Background Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. Methods This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. Results Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality—denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don’t Matter: Just Another Tool in the Toolbox– reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword—the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care—broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care—elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. Conclusion The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.

Artificial Intelligent Tools: Evidence-Mapping on the Perceived Positive Effects on Patient-Care

“…Therefore, it is incumbent on healthcare managers to develop health information management systems that guarantee uninterrupted supply of accurate and reliable patient data in real-time, for both administrative and clinical decision-making [34-36, 43, 46]. According to the reviewed articles [37,38,44], AI tools hold enormous potentials to process large volumes of patients' data and make timely and accurate inferences. Apart from providing rich and accurate data for decision-making in typically clinical settings, AI tools provide expeditious and reliable data for a quick action in the epidemiological and public health elds [33,45].…”

“…According to van der Zander et al [38], Visram et al [39], and Wittal et al [40], given that AI tools on datasets, they are better at diagnosing far more diseases in relatively shorter time than clinicians. This looks very promising, considering the ability of AI tools to leverage algorithms that help to predict accurately future outbreaks of diseases within speci c populations [36,37,44]. Although the public is worried about the ability of AI tools to act independently, they are cautiously optimistic that these intelligent machines could still be controlled to act responsibly [35,46].…”

Artificial Intelligence in Healthcare: A Scoping Review of Perceived Threats to Patient Rights and Safety

“…We report that the public gravely concerned about the breach of patient privacy and data security by AI tools. Of the 14 articles reviewed, 5(36%) [2,27,34,36,37] argued that AI tools have the potential to gather humongous volumes of patient data in a split second, sometimes at the blind side of the patients or their legal agents. As argued by Morgenstern et al [36] and Richardson et al [2], given their sheer complexity and automated abilities, it is di cult to foretell when and how a speci c patient data is acquired and used by AI, presenting a 'black box' situation.…”

“…The public is also seriously concerned of the lack of adequate regulations, speci cally on AI use in healthcare, that de ne the legal and ethical standards of practice [28][29][30][31][33][34][35][36][37][38]. As with all machines, AI could get it terribly wrong, through malfunction, with potentially terrible consequences to the health and well-being of patients.…”

Analysis of the medical residency matching algorithm to validate and improve equity

“…Algorithmic bias is widespread across a variety of contexts, and will increase with the growing prevalence of algorithm use for analysis of big data, creating an increasing need for their validation There have been calls for algorithms to be validated with respect to whether or not they meet an organizations clinical needs [ 7 ], whether or not they deliver on their intended purpose; the extent to which they match the organization’s education, skills, and regulatory competencies [ 8 , 9 ]; data inputs [ 10 ], cost budget constraints [ 11 ]; their prediction accuracy [ 12 , 13 ], performance when applied to diverse subgroups of the population [ 13 , 14 ], having continuous monitoring and recalibration [ 14 ], fairness trust and transparency [ 15 ], and whether or not they meet the larger goals of fairness, ethics, social equity, inclusion, access, and diversity [ 16 ]. The United Nations Sustainable Development Goal 10 seeks to reduce societal inequalities, within and between nations [ 17 ].…”