Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative...

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Primary Care Innovation

Background:

• Limited successful implementation of artificial intelligence (AI) in primary care (PC) settings.
• Significant advancements and envisioned uses for AI contrast with current real-world applications in PC.

Purpose of the Study:

• To identify and prioritize key areas for the integration of AI within primary care (PC) in Ontario, Canada.
• To guide future research, development, and resource allocation for AI in PC.

Main Methods:

• A collaborative consultation event utilized a nominal group technique.
• Multiple stakeholders engaged in generating, discussing, and ranking ideas for AI support in Ontario PC.

Main Results:

• Nine ranked priorities for AI in PC were identified, including preventative care, patient self-management, information synthesis, communication, data interoperability, clinical decision support, administrative support, practitioner task automation, and mental healthcare capacity.
• Emerging themes highlighted barriers such as equity, digital divide, system capacity, data quality, legal/ethical issues, and the need for user- and patient-centered design and evaluation.

Conclusions:

• Findings offer guidance for AI and PC development, with immediate opportunities for AI tool testing at patient, provider, and system levels.
• Sustainable AI innovations require longer-term projects focusing on data infrastructure and interdisciplinary collaboration.