Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue

Towards abundant intelligences: Considerations for Indigenous perspectives in adopting AI technology

Artificial Intelligence (AI) applications in healthcare are evolving rapidly. The integration of AI into the Canadian healthcare system has demonstrated significant potential for enhancing the efficiency of care and improving patient outcomes. However, as this transformative technology continues to advance, it is crucial to take into account the unique perspectives and requirements of Indigenous Peoples in Canada. This article delves into the political, ethical, and practical considerations associated with introducing AI into Indigenous healthcare, emphasizing the paramount importance of equity and inclusion, which are rooted in the Two-Eyed AI framework. It also underscores the significance of co-creating AI technology in collaboration with Indigenous communities and multidisciplinary development teams. To illustrate these principles, this commentary spotlights an international AI epistemology-focused working group example. Healthcare professionals who engage with AI, whether it be through research, management, development, or leadership are implicated with this contemporary paradigm shift in decolonizing novel AI technology.

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals

INTRODUCTION

Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.

METHODS AND ANALYSIS

A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method.

ETHICS AND DISSEMINATION

This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.