Integrating Artificial Intelligence (AI) in Primary Health Care (PHC) Systems: A Framework-Guided Comparative...

Area of Science:

• Health Services Research
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Primary health care (PHC) systems globally are exploring artificial intelligence (AI) integration to improve efficiency, equity, and decision-making.
• Implementation of AI in PHC is inconsistent, influenced by diverse systemic, contextual, and governance factors.
• Understanding these determinants is crucial for effective AI adoption in PHC.

Purpose of the Study:

• To identify systemic, contextual, and governance determinants of AI readiness in PHC.
• To compare AI readiness factors in two distinct health systems: Quebec (Canada) and Iran.
• To inform tailored strategies for AI implementation in PHC.

Main Methods:

• Qualitative comparative design utilizing semi-structured interviews and focus group discussions.
• Data analysis guided by the Primary Care Evaluation Tool (PCET) framework (stewardship, financing, resource generation, service delivery).
• Exploration of shared and context-specific challenges and requirements for AI in PHC.

Main Results:

• AI readiness is driven by systemic coherence, not solely technological availability.
• Common challenges include governance, financing, and data interoperability issues.
• Quebec faced operational/ethical concerns (workflow, trust), while Iran highlighted foundational governance, financing, and infrastructure deficits.

Conclusions:

• AI readiness in PHC is a multidimensional, context-dependent process requiring alignment with system maturity.
• High-resource systems should focus on ethical integration and workflow alignment.
• Middle-resource settings need foundational investments in governance and infrastructure for AI adoption.