{"resourceId":"apha-2026-public-health-ai-how-not-if-astho-policy-gaps-20261109","versions":[{"version":"external-5614e6519c9a9067585c8bc74f4742253835b90b9c17e47b87b929ace0ee67c5","resource":{"id":"apha-2026-public-health-ai-how-not-if-astho-policy-gaps-20261109","title":"APHA 2026 session: about a third of health departments still use no AI, two-thirds of state health departments have AI-use policies, mostly imposed from above","organization":"American Public Health Association (session with ICF, WashU, Dallas County HHS)","sector":"State and local public health departments","geography":"United States","publishedAt":"2026-11-09","publicationDate":"2026-11-09","eventDate":"2026-11-04","sourceName":"APHA Public Health Newswire","sourceLabel":"Public-sector association reporting","sourceUrl":"https://www.apha.org/publications/public-health-newswire/public-health-newswire/articles/ai-use-in-public-health-should-be-a-how-not-an-if","evidenceClass":"public-sector-association","outcomeClass":"mixed","topics":["governance-procurement","accessibility-workforce","knowledge-work"],"finding":"At APHA 2026, panelists citing a 2025 ASTHO survey said about one-third of state and local health departments do not use AI at all, use elsewhere is mostly administrative or report writing, and about two-thirds of state health departments have an AI-use policy, most handed down at the state level. Dallas County HHS described uses from COVID-19 outbreak prediction to translation and raised concerns about mental health chatbots and data center effects on residents.","sledRelevance":"Public health departments are being asked to move from whether to how on AI, but policy often comes from the state CIO level and workforce training is the stated gap, which shapes what local health departments can buy and deploy.","evidence":"APHA Newswire (Nov. 9, 2026) reports panel remarks by John Auerbach (ICF), Jennifer Layden (WashU) and Philip Huang (Dallas County HHS), citing the ASTHO 2025 survey figures.","architectureImplications":"Administrative and reporting uses dominate, suggesting low-integration assistants first; outbreak prediction and translation need data pipelines and language-quality checks.","governanceImplications":"Health departments often inherit state AI policy rather than writing their own; ASTHO template policies are offered for under-resourced departments.","securityPrivacyImplications":"Health departments handle surveillance and identifiable health data; inherited enterprise policies may not address public health data-sharing specifics.","caveats":"Session reporting, not the ASTHO survey itself; figures are panelists’ summaries of a 2025 survey; the Dallas County uses are described without outcome data.","streamIds":["health-human-services"],"roles":{"sales":"The buyer problem is readiness, not tools: health departments say they need workforce training, leadership awareness and usable policy. Lead with discovery on what state-level policy already allows and where staff time goes in reporting and translation. Offer bounded enablement tied to that policy rather than a platform pitch, and do not claim outbreak-prediction results the source does not give.","engineering":"Most departments sit at administrative and report-writing use, so start with low-integration assistants inside approved enterprise environments. Check the inherited state policy for data classes allowed, then scope translation or reporting pilots with quality review. Outbreak prediction needs data feeds and validation that the source does not describe, so treat it as a later phase.","delivery":"Delivery is change management: train staff and leaders, adapt an ASTHO template policy where none exists, and measure time saved on named reports. Assign an owner for AI governance inside the department even when policy comes from the state, and log uses so leadership can report on equity, accuracy and environmental questions raised at the session."},"roleDetails":{"sales":{"customerProblem":"Health departments face pressure to adopt AI but lack training, leadership readiness and department-specific policy.","stakeholders":["State and local health officers","Health department informatics leads","State CIO AI governance offices"],"discoveryQuestions":["Which AI policy applies to your department, and who wrote it?","Which reports or translations take the most staff time today?","What training have staff and leaders received on AI use?"],"valueHypothesis":"Policy-aligned training plus one administrative pilot can move a department from no use to measured, governed use.","potentialEngagement":"Four-to-six-week readiness and policy-adaptation engagement with one reporting or translation pilot.","unsupportedClaims":["AI improves outbreak prediction accuracy","Every health department is ready to adopt AI","Inherited state policy covers public health data needs"]},"engineering":{"fit":"Administrative, reporting and translation assistance within approved environments; analytics later.","architectureIntegration":"Enterprise-approved assistant for drafting and translation, with document stores for templates; surveillance analytics only with defined data pipelines.","prerequisites":["Confirmed applicable AI-use policy","Approved tool environment","Staff training plan"],"constraints":["State-imposed policy may restrict tools and data","Limited department IT and data staff","Equity and hallucination concerns require review"],"security":"Keep identifiable surveillance and case data out of unapproved tools; apply state data classification to every pilot.","proofOfValue":["Staff hours per named report before and after","Translation quality reviewed by fluent staff","Policy compliance audit of pilot uses"]},"delivery":{"work":"Assess policy, train staff, run one administrative pilot, document results and update governance.","dependencies":["State CIO or AI governance approvals","ASTHO or peer policy templates","Department leadership sponsorship"],"ownership":"Health officer or deputy, with informatics lead as day-to-day owner.","skillsAdoption":"Training for both staff and leadership is the stated demand, so budget for it before tools.","governanceCheckpoints":["Policy gap check before pilot","Equity and accuracy review of outputs","Leadership report after pilot"],"acceptanceCriteria":["Pilot report shows measured time change on named tasks (proposed)","All pilot uses logged against policy (proposed)","Staff trained before access (proposed)"],"risks":["Falling further behind other sectors","Uneven adoption across departments","Public concern about chatbots and data centers"]}},"retrievedAt":"2026-11-09T22:03:20-06:00","enrichedAt":"2026-11-09T22:06:41-06:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Workforce and leadership training is the main stated need; translation uses may improve language access if quality-checked.","procurementImplications":"Departments may be limited to state-approved tools; template policies lower drafting costs.","operatingModelImplications":"Governance frequently sits above the department, so local leaders need a clear internal owner for AI use.","peerReview":"unknown","sourceVerification":{"openedUrl":"https://www.apha.org/publications/public-health-newswire/public-health-newswire/articles/ai-use-in-public-health-should-be-a-how-not-an-if","referenceExcerpt":"about two-thirds of state health departments had adopted an AI-use policy, even if the health departments were not currently using AI in their work.","promptVersion":"sled-research-v4.0 HHS nightly brief","model":null,"basis":"agent-reported inspection"}}}]}