{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T08:48:43Z","timestamp":1793522923904,"version":"4.5.0"},"reference-count":55,"publisher":"American Medical Association (AMA)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity.<\/jats:p>\n <\/jats:sec>\n \n Evidence Review<\/jats:title>\n The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n The panel developed a conceptual framework to apply guiding principles across an algorithm\u2019s life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.45050","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T11:31:15Z","timestamp":1702639875000},"page":"e2345050","source":"Crossref","is-referenced-by-count":281,"title":["Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care"],"prefix":"10.1001","volume":"6","author":[{"given":"Marshall H.","family":"Chin","sequence":"first","affiliation":[{"name":"University of Chicago, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasim","family":"Afsar-Manesh","sequence":"additional","affiliation":[{"name":"Oracle Health, Austin, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arlene S.","family":"Bierman","sequence":"additional","affiliation":[{"name":"Agency for Healthcare Research and Quality, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Chang","sequence":"additional","affiliation":[{"name":"Agency for Healthcare Research and Quality, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caleb J.","family":"Col\u00f3n-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"US Department of Health and Human Services Office of Minority Health, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashila","family":"Dullabh","sequence":"additional","affiliation":[{"name":"NORC at the University of Chicago, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah Guadalupe","family":"Duran","sequence":"additional","affiliation":[{"name":"National Institute on Minority Health and Health Disparities, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malika","family":"Fair","sequence":"additional","affiliation":[{"name":"Association of American Medical Colleges, Washington, DC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tina","family":"Hernandez-Boussard","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maia","family":"Hightower","sequence":"additional","affiliation":[{"name":"Equality AI, Park City, Utah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjali","family":"Jain","sequence":"additional","affiliation":[{"name":"Agency for Healthcare Research and Quality, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William B.","family":"Jordan","sequence":"additional","affiliation":[{"name":"American Medical Association, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Konya","sequence":"additional","affiliation":[{"name":"Office of the National Coordinator for Health Information Technology, Washington, DC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roslyn Holliday","family":"Moore","sequence":"additional","affiliation":[{"name":"US Department of Health and Human Services Office of Minority Health, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamra Tyree","family":"Moore","sequence":"additional","affiliation":[{"name":"Prudential Financial, Arlington, Virginia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"NORC at the University of Chicago, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gauher","family":"Shaheen","sequence":"additional","affiliation":[{"name":"Elevance Health, Indianapolis, Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lynne Page","family":"Snyder","sequence":"additional","affiliation":[{"name":"NORC at the University of Chicago, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mithuna","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"NORC at the University of Chicago, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig A.","family":"Umscheid","sequence":"additional","affiliation":[{"name":"Agency for Healthcare Research and Quality, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucila","family":"Ohno-Machado","sequence":"additional","affiliation":[{"name":"Yale School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"issue":"9","key":"zsc230007r4","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1056\/NEJMms2004740","article-title":"Hidden in plain sight\u2014reconsidering the use of race correction in clinical algorithms.","volume":"383","author":"Vyas","year":"2020","journal-title":"N Engl J Med"},{"issue":"6464","key":"zsc230007r5","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","year":"2019","journal-title":"Science"},{"issue":"5","key":"zsc230007r8","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1016\/j.chest.2022.01.009","article-title":"Equity challenges for artificial intelligence algorithms in health care.","volume":"161","author":"Makhni","year":"2022","journal-title":"Chest"},{"issue":"6","key":"zsc230007r11","doi-asserted-by":"publisher","DOI":"10.1001\/jamahealthforum.2023.1197","article-title":"Awareness of racial and ethnic bias and potential solutions to address bias with use of health care algorithms.","volume":"4","author":"Jain","year":"2023","journal-title":"JAMA Health Forum"},{"issue":"7","key":"zsc230007r14","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1093\/jamia\/ocab065","article-title":"Enhancing trust in AI through industry self-governance.","volume":"28","author":"Roski","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"10077","key":"zsc230007r15","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1016\/S0140-6736(17)30569-X","article-title":"Structural racism and health inequities in the USA: evidence and interventions.","volume":"389","author":"Bailey","year":"2017","journal-title":"Lancet"},{"issue":"11","key":"zsc230007r16","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1038\/s41591-022-01993-y","article-title":"The AI life cycle: a holistic approach to creating ethical AI for health decisions.","volume":"28","author":"Ng","year":"2022","journal-title":"Nat Med"},{"issue":"13","key":"zsc230007r24","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1056\/NEJMra2212850","article-title":"Where medical statistics meets artificial intelligence.","volume":"389","author":"Hunter","year":"2023","journal-title":"N Engl J Med"},{"issue":"6429","key":"zsc230007r25","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1126\/science.aaw0029","article-title":"Regulation of predictive analytics in medicine.","volume":"363","author":"Parikh","year":"2019","journal-title":"Science"},{"key":"zsc230007r26","doi-asserted-by":"publisher","first-page":"l6927","DOI":"10.1136\/bmj.l6927","article-title":"Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.","volume":"368","author":"Vollmer","year":"2020","journal-title":"BMJ"},{"issue":"9","key":"zsc230007r27","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1038\/s41591-020-1041-y","article-title":"Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.","volume":"26","author":"Norgeot","year":"2020","journal-title":"Nat Med"},{"issue":"24","key":"zsc230007r28","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.1001\/jama.2019.18058","article-title":"Addressing bias in artificial intelligence in health care.","volume":"322","author":"Parikh","year":"2019","journal-title":"JAMA"},{"key":"zsc230007r29","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1146\/biodatasci.2021.4.issue-1","article-title":"Ethical machine learning in healthcare.","volume":"4","author":"Chen","year":"2021","journal-title":"Annu Rev Biomed Data Sci"},{"issue":"1","key":"zsc230007r30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","article-title":"Explainable AI: a review of machine learning interpretability methods.","volume":"23","author":"Linardatos","year":"2020","journal-title":"Entropy (Basel)"},{"issue":"10","key":"zsc230007r33","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1038\/s41591-019-0603-3","article-title":"Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed.","volume":"25","author":"CONSORT-AI and SPIRIT-AI Steering Group","year":"2019","journal-title":"Nat Med"},{"issue":"1","key":"zsc230007r34","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2020-100289","article-title":"Equity in essence: a call for operationalising fairness in machine learning for healthcare.","volume":"28","author":"Wawira Gichoya","year":"2021","journal-title":"BMJ Health Care Inform"},{"issue":"17","key":"zsc230007r35","doi-asserted-by":"publisher","first-page":"9357","DOI":"10.3390\/ijerph18179357","article-title":"Improving social justice in COVID-19 health research: interim guidelines for reporting health equity in observational studies.","volume":"18","author":"Antequera","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"zsc230007r36","doi-asserted-by":"publisher","first-page":"j5085","DOI":"10.1136\/bmj.j5085","article-title":"CONSORT-Equity 2017 extension and elaboration for better reporting of health equity in randomised trials.","volume":"359","author":"Welch","year":"2017","journal-title":"BMJ"},{"issue":"12","key":"zsc230007r37","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1097\/ACM.0000000000000794","article-title":"Community engagement studios: a structured approach to obtaining meaningful input from stakeholders to inform research.","volume":"90","author":"Joosten","year":"2015","journal-title":"Acad Med"},{"issue":"6","key":"zsc230007r40","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.6.061104","article-title":"Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.","volume":"10","author":"Drukker","year":"2023","journal-title":"J Med Imaging (Bellingham)"},{"key":"zsc230007r41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104250","article-title":"Algorithmic fairness in computational medicine.","volume":"84","author":"Xu","year":"2022","journal-title":"EBioMedicine"},{"issue":"6","key":"zsc230007r42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning.","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput Surv"},{"issue":"12","key":"zsc230007r43","doi-asserted-by":"publisher","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity.","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann Intern Med"},{"key":"zsc230007r45","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s12599-023-00787-x","article-title":"Algorithmic fairness in AI.","volume":"65","author":"Pfeiffer","year":"2023","journal-title":"Bus Inf Syst Eng"},{"issue":"10","key":"zsc230007r47","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1377\/hlthaff.2023.00553","article-title":"Mitigating racial and ethnic bias and advancing health equity in clinical algorithms: a scoping review.","volume":"42","author":"Cary","year":"2023","journal-title":"Health Aff (Millwood)"},{"issue":"2","key":"zsc230007r49","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1162\/152651601300168834","article-title":"Justice, health, and healthcare.","volume":"1","author":"Daniels","year":"2001","journal-title":"Am J Bioeth"},{"issue":"6","key":"zsc230007r50","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1016\/j.chest.2022.02.001","article-title":"Framework for integrating equity into machine learning models: a case study.","volume":"161","author":"Rojas","year":"2022","journal-title":"Chest"},{"issue":"9","key":"zsc230007r52","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1093\/jamia\/ocac078","article-title":"A framework for the oversight and local deployment of safe and high-quality prediction models.","volume":"29","author":"Bedoya","year":"2022","journal-title":"J Am Med Inform Assoc"},{"issue":"15","key":"zsc230007r53","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1007\/s11606-022-07517-1","article-title":"Predictive analytics programs at large healthcare systems in the USA: a national survey.","volume":"37","author":"Rojas","year":"2022","journal-title":"J Gen Intern Med"},{"issue":"12","key":"zsc230007r55","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1093\/jamia\/ocaa085","article-title":"Patient safety and quality improvement: ethical principles for a regulatory approach to bias in healthcare machine learning.","volume":"27","author":"McCradden","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"zsc230007r1","volume-title":"Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy","author":"O\u2019Neil","year":"2016"},{"key":"zsc230007r23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/SDS49233.2020.00015","volume-title":"Proceedings of the 2020 7th Swiss Conference on Data Science (SDS)","author":"Loi","year":"2020"},{"key":"zsc230007r31","volume-title":"Four Principles of Explainable Artificial Intelligence. NIST Interagency\/Internal Report (NISTIR)","author":"Phillips","year":"2021"},{"key":"zsc230007r2","unstructured":"The White House. Executive Order on further advancing racial equity and support for underserved communities through the federal government. Updated February 16, 2023. Accessed August 31, 2023. https:\/\/www.whitehouse.gov\/briefing-room\/presidential-actions\/2023\/02\/16\/executive-order-on-further-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government\/"},{"key":"zsc230007r3","unstructured":"Obermeyer? Z, Nissan? R, Stern? M, Eaneff? S, Bembeneck? EJ, Mullainathan? S. Algorithmic Bias Playbook. Chicago Booth Center for Applied Artificial Intelligence; 2021. Accessed November 10, 2023. https:\/\/www.chicagobooth.edu\/research\/center-for-applied-artificial-intelligence\/research\/algorithmic-bias\/playbook"},{"key":"zsc230007r6","unstructured":"Organization for Economic Cooperation and Development. Recommendation of the Council on Artificial Intelligence, OECD\/LEGAL\/0449. 2019. Accessed November 10, 2023. https:\/\/legalinstruments.oecd.org\/en\/instruments\/OECD-LEGAL-0449#mainText"},{"key":"zsc230007r7","unstructured":"World Health Organization. Ethics and Governance of Artificial Intelligence for Health. World Health Organization; 2021. Accessed November 10, 2023. https:\/\/www.who.int\/publications\/i\/item\/9789240029200"},{"key":"zsc230007r9","unstructured":"Marchesini? K, Smith? J, Everson? J. Increasing the transparency and trustworthiness of AI in health care. HealthITbuzz blog. April 13, 2023. Accessed August 31, 2023. https:\/\/www.healthit.gov\/buzz-blog\/health-innovation\/transparent-and-trustworthy-ai-in-health-care"},{"key":"zsc230007r10","unstructured":"Office of the National Coordinator for Health Information Technology. Health data, technology, and interoperability: certification program updates, algorithm transparency, and information sharing (HTI-1) proposed rule. HealthIT.gov. June 22, 2023. Accessed August 31, 2023. https:\/\/www.healthit.gov\/topic\/laws-regulation-and-policy\/health-data-technology-and-interoperability-certification-program"},{"key":"zsc230007r12","unstructured":"Agency for Healthcare Research and Quality. Meetings examine impact of healthcare algorithms on racial and ethnic disparities in health and healthcare. Accessed August 31, 2023. https:\/\/effectivehealthcare.ahrq.gov\/news\/meetings"},{"key":"zsc230007r13","unstructured":"Matheny? M, Thadaney? IS, Ahmed? M, Whicher? D, eds. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. National Academy of Medicine; 2022. Accessed November 10, 2023. https:\/\/nam.edu\/artificial-intelligence-special-publication\/"},{"key":"zsc230007r17","unstructured":"World Health Organization. Health equity. Accessed August 31, 2023. https:\/\/www.who.int\/health-topics\/health-equity#tab=tab_1"},{"key":"zsc230007r18","unstructured":"Braveman? P, Arkin? E, Orleans? T, Proctor? D, Plough? A. What is health equity? Robert Wood Johnson Foundation. May 1, 2017. Accessed November 10, 2023. https:\/\/www.rwjf.org\/en\/insights\/our-research\/2017\/05\/what-is-health-equity-.html"},{"key":"zsc230007r19","unstructured":"Health Care Payment Learning & Action Network. Advancing health equity through APMs: guidance for equity-centered design and implementation. 2021. Accessed November 10, 2023. http:\/\/hcp-lan.org\/workproducts\/APM-Guidance\/Advancing-Health-Equity-Through-APMs.pdf"},{"key":"zsc230007r20","unstructured":"US Department of Health and Human Services. Trustworthy AI (TAI) Playbook. US Department of Health and Human Services; 2021. Accessed November 10, 2023. https:\/\/www.hhs.gov\/sites\/default\/files\/hhs-trustworthy-ai-playbook.pdf"},{"key":"zsc230007r21","unstructured":"Zuckerman? BL, Karabin? JM, Parker? RA, Doane? WEJ, Williams? SR. Options and Opportunities to Address and Mitigate the Existing and Potential Risks, as well as Promote Benefits, Associated With AI and Other Advanced Analytic Methods. OPRE Report 2022-253. US Department of Health and Human Services Office of Planning, Research, and Evaluation, Administration for Children and Families; 2022. Accessed November 10, 2023. https:\/\/www.acf.hhs.gov\/opre\/report\/options-opportunities-address-mitigate-existing-potential-risks-promote-benefits"},{"key":"zsc230007r22","unstructured":"Gonzalez? R. The spectrum of community engagement to ownership. Movement Strategy Center. 2019. Accessed November 10, 2023. https:\/\/movementstrategy.org\/resources\/the-spectrum-of-community-engagement-to-ownership\/"},{"key":"zsc230007r32","unstructured":"Vasse\u2019i? RM, McCrosky? J. AI Transparency in Practice. Mozilla; 2023. Accessed August 31, 2023. https:\/\/foundation.mozilla.org\/en\/research\/library\/ai-transparency-in-practice\/ai-transparency-in-practice\/"},{"key":"zsc230007r38","unstructured":"The White House. Fact sheet: Biden-Harris administration announces key actions to advance tech accountability and protect the rights of the American public. October 4, 2022. Accessed November 10, 2023. https:\/\/www.whitehouse.gov\/ostp\/news-updates\/2022\/10\/04\/fact-sheet-biden-harris-administration-announces-key-actions-to-advance-tech-accountability-and-protect-the-rights-of-the-american-public\/"},{"key":"zsc230007r39","unstructured":"National Congress of American Indians. Tribal nations & the United States: an introduction. Accessed November 27, 2023. https:\/\/archive.ncai.org\/about-tribes"},{"key":"zsc230007r44","unstructured":"Weinkauf? D. When worlds collide\u2014the possibilities and limits of algorithmic fairness (part 1). Privacy Tech-Know blog. April 5, 2023. Accessed August 31, 2023. https:\/\/www.priv.gc.ca\/en\/blog\/20230405_01\/"},{"key":"zsc230007r46","unstructured":"Caton? S, Haas? C. Fairness in machine learning: a survey.? arXiv. Preprint posted online October 4, 2020. doi:10.48550\/arXiv.2010.04053"},{"key":"zsc230007r48","unstructured":"Jung? S, Park? T, Chun? S, Moon? T. Re-weighting based group fairness regularization via classwise robust optimization.? arXiv. Preprint posted online March 1, 2023. doi:10.48550\/arXiv.2303.00442"},{"key":"zsc230007r51","unstructured":"Weinkauf? D. When worlds collide\u2014the possibilities and limits of algorithmic fairness (part 2). Privacy Tech-Know blog. Office of the Privacy Commissioner of Canada. April 5, 2023. Accessed August 31, 2023. https:\/\/www.priv.gc.ca\/en\/blog\/20230405_02\/"},{"key":"zsc230007r54","unstructured":"Eggers? W, Walsh? S, Joergensen? C, Kishnani? P. Regulation that enables innovation. Deloitte Insights. March 23, 2023. Accessed November 10, 2023. https:\/\/www2.deloitte.com\/us\/en\/insights\/industry\/public-sector\/government-trends\/2023\/regulatory-agencies-and-innovation.html"}],"container-title":["JAMA Network Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/articlepdf\/2812958\/chin_2023_sc_230007_1702050468.82841.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T11:31:36Z","timestamp":1702639896000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2812958"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":55,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12,1]]}},"URL":"https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.45050","relation":{},"ISSN":["2574-3805"],"issn-type":[{"value":"2574-3805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,15]]}}}