SynthesisBMJ health & care informatics2026
Enabling digital multifactorial risk assessment in primary care: an umbrella review and recommendations for design and implementation.
Synthesis in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From bedside to bench: towards clinical predictive AI research that achieves real-world impact.BMJ digital health & AI · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop recommendations to inform development and integration of predictive digital health and artificial intelligence tools in primary care.
methodsRecommendation 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.
resultsThe 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.
conclusionsWe 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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.