Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation. Issue 1 (28th January 2022)

• Record Type: Journal Article
• Authors: Kueper, Jacqueline K; Terry, Amanda; Bahniwal, Ravninder; Meredith, Leslie; Beleno, Ron; Brown, Judith Belle; Dang, Janet; Leger, Daniel; McKay, Scott; Pinto, Andrew; Ryan, Bridget L; Zwarenstein, Merrick; Lizotte, Daniel J
• Is Part Of: BMJ health & care informatics. Volume 29:Issue 1(2022)
• Publication Date: 2022-01-28
• Subjects: artificial intelligence -- primary health care -- machine learning; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.1136/bmjhci-2021-100493
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings. Objectives: To identify priority areas for AI and PC in Ontario, Canada. Methods: A collaborative consultation event engaged multiple stakeholders in a nominal group technique process to generate, discuss and rank ideas for how AI can support Ontario PC. Results: The consultation process produced nine ranked priorities: (1) preventative care and risk profiling, (2) patient self-management of condition(s), (3) management and synthesis of information, (4) improved communication between PC and AI stakeholders, (5) data sharing and interoperability, (6-tie) clinical decision support, (6-tie) administrative staff support, (8) practitioner clerical and routine task support and (9) increased mental healthcare capacity and support. Themes emerging from small group discussions about barriers, implementation issues and resources needed to support the priorities included: equity and the digital divide; system capacity and culture; data availability and quality; legal and ethical issues; user-centred design; patient-centredness; and proper evaluation of AI-driven tool implementation. Discussion: Findings provide guidance for future work on AI and PC. There are immediate opportunities to use existing resources to develop and test AI for priority areas at the patient, provider and system level. For larger scale, sustainable innovations, there is a need for longer-term projects that lay foundations around data and interdisciplinary work. Conclusion: Study findings can be used to inform future research and development of AI for PC, and to guide resource planning and allocation.