{"resourceId":"jmir-harrison-ai-public-health-practice-scoping-review-20261029","versions":[{"version":"external-ae2e992b4040bd4733fe79b5602074a1d25f9b0a6e9e73e171dc9d6097130b4c","resource":{"id":"jmir-harrison-ai-public-health-practice-scoping-review-20261029","title":"JMIR scoping review finds only eight practice reports of AI in high-income public health outside outbreaks, with mixed effectiveness and a large gap versus speculative use cases","organization":"University of Exeter Medical School et al. (NIHR-funded)","sector":"Public health practice research","geography":"High-income countries (UK-focused relevance; included US WIC chatbot and other international practice reports)","publishedAt":"2026-10-29","publicationDate":"2026-10-29","eventDate":"2026-10-29","sourceName":"Journal of Medical Internet Research","sourceLabel":"Peer-reviewed scoping review","sourceUrl":"https://www.jmir.org/2026/1/e108250/","evidenceClass":"academic-research","outcomeClass":"cautionary","topics":["governance-procurement","operating-model","knowledge-work"],"finding":"After screening 5,400 records and 155 full texts, Harrison et al. included only eight reports of AI tools actually used in public health practice in high-income contexts excluding outbreaks. Uses spanned chatbots, demand forecasting, symptom checking and evidence synthesis; effectiveness evidence was mixed (helpful WIC chatbot vs WHO S.A.R.A.H judged potentially trust-eroding). The authors conclude the thin practice evidence base makes it hard to know which AI uses to adopt and urge local evaluation and accessible dissemination—highlighted again in Nov 5 public-health AI roundups.","sledRelevance":"State and local public health AI procurements often cite promise more than practice evidence. This review is a cautionary control for SLED buyers comparing vendor pipelines to evaluated deployments, including U.S. WIC chatbot experience among the few practice reports.","evidence":"J Med Internet Res 2026;28:e108250, published Oct 29, 2026. Searches to July 24, 2025 across MEDLINE, Embase, Web of Science, Scopus. Eight included reports: chatbots (3), ML demand forecasting (1), symptom checking (2), evidence synthesis (2). Mixed outcomes explicitly noted; CareCall expanded reach but increased worker callbacks; S.A.R.A.H criticized; Maya WIC chatbot viewed as helpful. Authors contrast 8 practice reports with 44 potential uses in a linked review. NIHR funded; AI not used in the review process.","architectureImplications":"Prefer narrowly scoped tools with evaluation hooks (logging, human comparison, stop criteria) over broad 'AI-ready' platforms lacking practice evidence outside outbreaks.","governanceImplications":"Treat 'AI-ready' as an evidence question: require prospective evaluation plans, comparable-population performance and operational outcome metrics before scaling.","securityPrivacyImplications":"Practice reports include chatbots handling benefits and health concerns; the review does not audit security but underscores trust risk when tools fail publicly.","caveats":"UK-led relevance filter; outbreaks excluded by design so surveillance AI is out of scope. Publication Oct 29 precedes this edition's last HHS run but was not archived; included for scrutiny pass and fresh Nov 5 secondary attention. Effectiveness assessment was out of scope for the review itself.","streamIds":["health-human-services"],"roles":{"sales":"Use this as a discovery asset with public health buyers who are being pitched many tools: ask for practice evidence excluding demos. Do not claim the review endorses any vendor. Position evaluation design and evidence-gap closure as the engagement.","engineering":"Architecture should assume weak external evidence—instrument pilots for accuracy, workload and trust metrics. Prerequisites include publishable evaluation methods and comparison baselines.","delivery":"Delivery success is a completed local evaluation others can reuse, not feature go-live alone. Risks include scaling chatbots that undermine institutional trust."},"roleDetails":{"sales":{"customerProblem":"Public health leaders face vendor and political pressure to adopt AI without transferable practice evidence outside outbreaks.","stakeholders":["State/local public health directors","evidence/evaluation units","procurement officers"],"discoveryQuestions":["What practice evidence exists for this tool in a comparable agency?","What outcome will you stop the pilot on?","How will results be shared beyond the vendor?"],"valueHypothesis":"Structured local evaluation against operational outcomes reduces costly scale-ups of tools that look strong only in benchmarks.","potentialEngagement":"Evidence-gap assessment and pilot evaluation design for one candidate use case.","unsupportedClaims":["That eight reports prove AI fails in public health","That outbreak AI evidence transfers automatically","Vendor performance guarantees from this paper"]},"engineering":{"fit":"Evaluation instrumentation and narrow-scope pilots rather than enterprise AI suites justified only by potential-use catalogs.","architectureIntegration":"Logging of AI outputs vs human decisions, sampling for quality review, dashboards for workload and escalation.","prerequisites":["Baseline metrics","Ethics/privacy review","Dissemination plan"],"constraints":["Outbreak tools out of scope for this evidence base","High-income focus may miss LMI lessons","Publication lag vs fast model change"],"security":"Evaluation datasets and chatbot logs need the same protections as production PHI/PII.","proofOfValue":["Pre-registered pilot outcomes","Trust/workload measures alongside accuracy","Public or shared after-action"]},"delivery":{"work":"Select one use case, run time-boxed pilot with comparison, publish accessible findings for peer agencies.","dependencies":["Academic or epi evaluation partners","Vendor cooperation on logs","Leadership air cover to stop bad pilots"],"ownership":"Public health science/evaluation lead with program ops co-owner.","skillsAdoption":"Staff need appraisal skills for AI-specific harms and equity effects.","governanceCheckpoints":["Protocol registration","Interim futility review","Scale/stop decision gate"],"acceptanceCriteria":["Documented effectiveness and cost signals","Harm/trust monitoring","Reusable methods note"],"risks":["Pilot theater without power to stop","Overgeneralizing UK findings","Ignoring outbreak-excluded domains incorrectly"]}},"retrievedAt":"2026-11-06T22:11:40-06:00","enrichedAt":"2026-11-06T22:12:55-06:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Thin evidence and mixed chatbot results imply workforce training and non-digital alternatives remain essential; CareCall showed reach gains can raise callback load.","procurementImplications":"RFPs should demand practice evidence or funded evaluation, not only technical benchmarks or 'potential use' lists.","operatingModelImplications":"Build evaluate-before-scale into AI operating cadence for public health, especially outside outbreak tools.","peerReview":"peer-reviewed","sourceVerification":{"openedUrl":"https://www.jmir.org/2026/1/e108250/","referenceExcerpt":"even with a comprehensive search across multiple databases, there were few reports of how AI tools are being used in practice in public health contexts.","promptVersion":"sled-research-v4.0 HHS nightly brief","model":null,"basis":"agent-reported inspection"}}}]}