Enabling digital multifactorial risk assessment in primary care: an umbrella review and recommendations for design and implementation.

Abstract

Objectives: To develop recommendations to inform development and integration of predictive digital health and artificial intelligence tools in primary care.

Methods: Recommendation development involved two stages. The initial scoping phase comprised an umbrella review to identify barriers to implementation for risk prediction tools in primary care. The consensus phase involved a stakeholder workshop with 22 stakeholders. The draft recommendations were then refined via a stakeholder survey completed by 13 participants and three online meetings attended by 14 individuals to generate the final output.

Results: The umbrella review included 12 reviews and identified 15 barriers to implementation of risk prediction models, including lack of integration with electronic health records and poor interoperability across them. The final recommendations include 14 core features of risk prediction models and tools, including the need for codesign with clinicians and the public and integration with digital infrastructure and workflows.

Discussion: These findings particularly emphasise the value of early engagement with key stakeholders and health record system providers, and a need for shared understanding of the needs of end-users.

Conclusions: We have developed recommendations detailing 14 key characteristics for a digital risk prediction model to be successfully used in primary care settings. This profile should be used to guide development of new risk prediction tools and is also applicable more widely to other digital health innovations within primary care. Future research should work to resolve the identified system-level barriers to implementation.