{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:59:57Z","timestamp":1790197197173,"version":"4.1.0"},"reference-count":28,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>AI is increasingly embedded in digital public health systems, but model performance, system deployment, message delivery, alert volume, and user engagement do not establish whether an AI-supported workflow has changed public health practice. This viewpoint proposes a public health action end point\u2014a prespecified, auditable, AI output\u2013specific, actor-bound, time-bound, denominator-based way to specify and measure an existing proximal process or implementation end point. It begins with a defined AI output and records the corresponding action opportunity, accountable actor, action status, and evidence concerning the AI\u2019s role in the action. A complete specification includes rules for repeated outputs, completed action, justified nonaction, missed action, unresolved cases, denominator loss, and paired safety, workload, privacy, equity, and model drift monitoring. A public health action end point is not a new causal outcome, reporting guideline, or validated surrogate for a population health benefit. An observed action rate alone does not establish that AI initiated or caused the action; causal claims require a documented attribution strategy and an appropriate comparator or causal design. The framework applies to research and production deployments but complements rather than replaces ethics review, trial registration, and jurisdiction-specific regulatory and governance obligations. It is intended to help authors, implementers, reviewers, and editors align claims with what an evaluation has actually measured.<\/jats:p>","DOI":"10.2196\/106243","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:00:10Z","timestamp":1790193610000},"page":"e106243-e106243","source":"Crossref","is-referenced-by-count":0,"title":["Accountable Action End Points for Evaluating Artificial Intelligence in Digital Public Health"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3682-3634","authenticated-orcid":false,"given":"Xiang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3324-8243","authenticated-orcid":false,"given":"Hongyan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1732-1184","authenticated-orcid":false,"given":"Zhengdong","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"R1","unstructured":"Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization; Jun 21, 2021. URL: https:\/\/www.who.int\/publications\/i\/item\/9789240029200 [Accessed 04-07-2026]"},{"key":"R2","unstructured":"Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. World Health Organization; Mar 25, 2025. URL: https:\/\/www.who.int\/publications\/i\/item\/9789240084759 [Accessed 04-07-2026]"},{"key":"R3","unstructured":"Recommendations on digital interventions for health system strengthening: WHO guideline. World Health Organization; Jun 6, 2019. URL: https:\/\/www.who.int\/publications\/i\/item\/9789241550505 [Accessed 04-07-2026]"},{"issue":"5","key":"R4","doi-asserted-by":"publisher","first-page":"e428","DOI":"10.1016\/S2468-2667(25)00036-2","article-title":"Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions","volume":"10","author":"Panteli","journal-title":"Lancet Public Health"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","journal-title":"BMJ"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard","journal-title":"J Am Med Inform Assoc"},{"issue":"9","key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/11510","article-title":"Patient and consumer safety risks when using conversational assistants for medical information: an observational study of Siri, Alexa, and Google Assistant","volume":"20","author":"Bickmore","journal-title":"J Med Internet Res"},{"issue":"9","key":"R8","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.2105\/ajph.89.9.1322","article-title":"Evaluating the public health impact of health promotion interventions: the RE-AIM framework","volume":"89","author":"Glasgow","journal-title":"Am J Public Health"},{"issue":"2","key":"R9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10488-010-0319-7","article-title":"Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda","volume":"38","author":"Proctor","journal-title":"Adm Policy Ment Health"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i6795","article-title":"Standards for Reporting Implementation Studies (StaRI) statement","volume":"356","author":"Pinnock","journal-title":"BMJ"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.2196\/46694","article-title":"iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations","volume":"25","author":"Perrin Franck","journal-title":"J Med Internet Res"},{"issue":"4","key":"R12","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1923","article-title":"CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions","volume":"13","author":"Eysenbach","journal-title":"J Med Internet Res"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.1186\/s13012-016-0510-7","article-title":"Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update)","volume":"11","author":"Ross","journal-title":"Implement Sci"},{"issue":"e1","key":"R14","doi-asserted-by":"publisher","first-page":"e9","DOI":"10.1136\/amiajnl-2013-001684","article-title":"Ten key considerations for the successful implementation and adoption of large-scale health information technology","volume":"20","author":"Cresswell","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"R15","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.8775","article-title":"Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies","volume":"19","author":"Greenhalgh","journal-title":"J Med Internet Res"},{"issue":"5","key":"R16","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","journal-title":"Nat Med"},{"issue":"9","key":"R17","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","journal-title":"Nat Med"},{"issue":"9","key":"R18","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"Cruz Rivera","journal-title":"Nat Med"},{"key":"R19","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"issue":"1","key":"R21","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/1748-5908-4-50","article-title":"Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science","volume":"4","author":"Damschroder","journal-title":"Implement Sci"},{"issue":"4","key":"R22","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1111\/j.1468-0009.2005.00397.x","article-title":"Evaluating the quality of medical care","volume":"83","author":"Donabedian","journal-title":"Milbank Q"},{"issue":"Suppl 3","key":"R23","doi-asserted-by":"publisher","first-page":"i68","DOI":"10.1136\/qshc.2010.042085","article-title":"A new sociotechnical model for studying health information technology in complex adaptive healthcare systems","volume":"19","author":"Sittig","journal-title":"Qual Saf Health Care"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1186\/s13012-015-0342-x","article-title":"Outcomes for implementation science: an enhanced systematic review of instruments using evidence-based rating criteria","volume":"10","author":"Lewis","journal-title":"Implement Sci"},{"key":"R25","unstructured":"Tool: teach-back. Agency for Healthcare Research and Quality. May 2023. URL: https:\/\/www.ahrq.gov\/teamstepps-program\/curriculum\/communication\/tools\/teachback.html [Accessed 08-08-2026]"},{"key":"R26","unstructured":"Brooke J . SUS: a \u201cquick and dirty\u201d usability scale. In: Jordan PW Thomas B Weerdmeester BA McClelland IL , editors. Usability Evaluation in Industry. Taylor & Francis; 1996:189-194. ISBN: 9780748404605"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-4115(08)62386-9","article-title":"Development of NASA-TLX (Task Load Index): results of empirical and theoretical research","volume":"52","author":"Hart","journal-title":"Adv Psychol"},{"issue":"6464","key":"R28","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","journal-title":"Science"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:00:11Z","timestamp":1790193611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e106243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":28,"URL":"https:\/\/doi.org\/10.2196\/106243","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"article-number":"v28i5e106243"}}