Journal of the American Medical Informatics Association最新文献

{"title":"Optimizing the efficiency and effectiveness of data quality assurance in a multicenter clinical dataset.","authors":"Anne Fu, Trong Shen, Surain B Roberts, Weihan Liu, Shruthi Vaidyanathan, Kayley-Jasmin Marchena-Romero, Yuen Yu Phyllis Lam, Kieran Shah, Denise Y F Mak, Fahad Razak, Amol A Verma","doi":"10.1093/jamia/ocaf042","DOIUrl":"10.1093/jamia/ocaf042","url":null,"abstract":"<p><strong>Objectives: </strong>Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories.</p><p><strong>Materials and methods: </strong>The GEMINI database collected data from 462 226 patient admissions across 32 hospitals from 2021 to 2023. We report data quality issues identified through semi-automated and manual data quality assessments completed during the data collection phase. We conducted a simulation experiment to evaluate the relationship between the number of records reviewed manually, the detection of true data errors (true positives) and the number of manual chart abstraction errors (false positives) that required unnecessary investigation.</p><p><strong>Results: </strong>The semi-automated data quality assessments identified 79 data quality issues requiring correction, of which 14 had a large impact, affecting at least 50% of records in the data. After resolving issues identified through semi-automated assessments, manual validation of 2676 patient encounters at 19 hospitals identified 4 new meaningful data errors (3 in transfusion data and 1 in physician identifiers), distributed across 4 hospitals. There were 365 manual chart abstraction errors, which required investigation by data analysts to identify as \"false positives.\" These errors increased linearly with the number of charts reviewed manually. Simulation results demonstrate that all 3 transfusion data errors were identified with 95% sensitivity after manual review of 5 records, whereas 18 records were needed for the physician's table.</p><p><strong>Discussion and conclusion: </strong>The GEMINI approach represents a scalable framework for data quality assessment and improvement in multisite EHR research databases. Manual data review is important but can be minimized to optimize the trade-off between true and false identification of data quality errors.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"835-844"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance automated abstract screening with large language model ensembles.","authors":"Rohan Sanghera, Arun James Thirunavukarasu, Marc El Khoury, Jessica O'Logbon, Yuqing Chen, Archie Watt, Mustafa Mahmood, Hamid Butt, George Nishimura, Andrew A S Soltan","doi":"10.1093/jamia/ocaf050","DOIUrl":"10.1093/jamia/ocaf050","url":null,"abstract":"<p><strong>Objective: </strong>screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract screening.</p><p><strong>Materials and methods: </strong>LLMs (GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, Llama 3 70B, Gemini 1.5 Pro, and Claude Sonnet 3.5) were trialed across 23 Cochrane Library systematic reviews to evaluate their accuracy in zero-shot binary classification for abstract screening. Initial evaluation on a balanced development dataset (n = 800) identified optimal prompting strategies, and the best performing LLM-prompt combinations were then validated on a comprehensive dataset of replicated search results (n = 119 695).</p><p><strong>Results: </strong>On the development dataset, LLMs exhibited superior performance to human researchers in terms of sensitivity (LLMmax = 1.000, humanmax = 0.775), precision (LLMmax = 0.927, humanmax = 0.911), and balanced accuracy (LLMmax = 0.904, humanmax = 0.865). When evaluated on the comprehensive dataset, the best performing LLM-prompt combinations exhibited consistent sensitivity (range 0.756-1.000) but diminished precision (range 0.004-0.096) due to class imbalance. In addition, 66 LLM-human and LLM-LLM ensembles exhibited perfect sensitivity with a maximal precision of 0.458 with the development dataset, decreasing to 0.1450 over the comprehensive dataset; but conferring workload reductions ranging between 37.55% and 99.11%.</p><p><strong>Discussion: </strong>Automated abstract screening can reduce the screening workload in systematic review while maintaining quality. Performance variation between reviews highlights the importance of domain-specific validation before autonomous deployment. LLM-human ensembles can achieve similar benefits while maintaining human oversight over all records.</p><p><strong>Conclusion: </strong>LLMs may reduce the human labor cost of systematic review with maintained or improved accuracy, thereby increasing the efficiency and quality of evidence synthesis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"893-904"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principles and implementation strategies for equitable and representative academic partnerships in global health informatics research.","authors":"Elizabeth Campbell, Oliver J Bear Don't Walk, Hamish Fraser, Judy Gichoya, Kavishwar B Wagholikar, Andrew S Kanter, Felix Holl, Sansanee Craig","doi":"10.1093/jamia/ocaf015","DOIUrl":"10.1093/jamia/ocaf015","url":null,"abstract":"<p><strong>Objective: </strong>Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle.</p><p><strong>Materials and methods: </strong>The American Medical Informatics Association (AMIA) GHI Working Group organized a collaborative workshop at the 2023 AMIA Annual Symposium that included the presentation of five case studies of how principles of health equity have been incorporated into projects situated in low-and-middle-income countries and with Indigenous communities in the U.S. and best practices for operationalizing these principles into other informatics projects.</p><p><strong>Results: </strong>We present five principles: (1) Inclusion and Participation in Ethical, Sustainable Collaborations; (2) Engaging Community-Based Participatory Research Approaches; (3) Stakeholder Engagement; (4) Scalability and Sustainability; (5) Representation in Knowledge Creation, along with strategies that informatics researchers may use to incorporate these principles into their work.</p><p><strong>Discussion: </strong>Presented case studies and subsequent focus groups yielded key concepts and strategies to promote health equity that may be operationalized across GHI projects.</p><p><strong>Conclusion: </strong>Equitable, sustainable, and scalable GHI projects require intentional integration of community and stakeholder perspectives in project development, implementation, and knowledge creation processes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"958-963"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of perceived discrimination with health outcomes and health disparities in the All of Us cohort.","authors":"Vincent Lam, Sonali Gupta, I King Jordan, Leonardo Mariño-Ramírez","doi":"10.1093/jamia/ocaf040","DOIUrl":"10.1093/jamia/ocaf040","url":null,"abstract":"<p><strong>Objectives: </strong>The goal of this study was to investigate the association of perceived discrimination with health outcomes and disparities.</p><p><strong>Materials and methods: </strong>The study cohort consists of 60 180 participants from the 4 largest self-identified race and ethnicity (SIRE) groups in the All of Us Research Program participant body: Asian (1291), Black (4726), Hispanic (5336), and White (48 827). A perceived discrimination index (PDI) was derived from participant responses to the \"Social Determinants of Health\" survey, and the All of Us Researcher Workbench was used to analyze associations and mediation effects of PDI and SIRE with 1755 diseases.</p><p><strong>Results: </strong>The Black SIRE group has the greatest median PDI, followed by the Asian, Hispanic, and White groups. The Black SIRE group shows the greatest number of diseases with elevated risk relative to the White reference group, followed by the Hispanic and Asian groups. Perceived discrimination index was found to be positively and significantly associated with 489 out of 1755 (27.86%) diseases. \"Mental Disorders\" is the disease category with the greatest proportion of diseases positively and significantly associated with PDI: 59 out of 72 (81.94%) diseases. Mediation analysis showed that PDI mediates 69 out of 351 (19.66%) Black-White disease disparities.</p><p><strong>Discussion: </strong>Perceived discrimination is significantly associated with risk for numerous diseases and mediates Black-White disease disparities in the All of Us participant cohort.</p><p><strong>Conclusion: </strong>This work highlights the role of discrimination as an important social determinant of health and provides a means by which it can be quantified and modeled on the All of Us platform.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"823-834"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedBot vs RealDoc: efficacy of large language modeling in physician-patient communication for rare diseases.","authors":"Magdalena T Weber, Richard Noll, Alexandra Marchl, Carlo Facchinello, Achim Grünewaldt, Christian Hügel, Khader Musleh, Thomas O F Wagner, Holger Storf, Jannik Schaaf","doi":"10.1093/jamia/ocaf034","DOIUrl":"10.1093/jamia/ocaf034","url":null,"abstract":"<p><strong>Objectives: </strong>This study assesses the abilities of 2 large language models (LLMs), GPT-4 and BioMistral 7B, in responding to patient queries, particularly concerning rare diseases, and compares their performance with that of physicians.</p><p><strong>Materials and methods: </strong>A total of 103 patient queries and corresponding physician answers were extracted from EXABO, a question-answering forum dedicated to rare respiratory diseases. The responses provided by physicians and generated by LLMs were ranked on a Likert scale by a panel of 4 experts based on 4 key quality criteria for health communication: correctness, comprehensibility, relevance, and empathy.</p><p><strong>Results: </strong>The performance of generative pretrained transformer 4 (GPT-4) was significantly better than the performance of the physicians and BioMistral 7B. While the overall ranking considers GPT-4's responses to be mostly correct, comprehensive, relevant, and emphatic, the responses provided by BioMistral 7B were only partially correct and empathetic. The responses given by physicians rank in between. The experts concur that an LLM could lighten the load for physicians, rigorous validation is considered essential to guarantee dependability and efficacy.</p><p><strong>Discussion: </strong>Open-source models such as BioMistral 7B offer the advantage of privacy by running locally in health-care settings. GPT-4, on the other hand, demonstrates proficiency in communication and knowledge depth. However, challenges persist, including the management of response variability, the balancing of comprehensibility with medical accuracy, and the assurance of consistent performance across different languages.</p><p><strong>Conclusion: </strong>The performance of GPT-4 underscores the potential of LLMs in facilitating physician-patient communication. However, it is imperative that these systems are handled with care, as erroneous responses have the potential to cause harm without the requisite validation procedures.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"775-783"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of simulation testing for the evaluation of ambient digital scribes: a case report.","authors":"Joshua M Biro, Jessica L Handley, James Mickler, Sahithi Reddy, Varsha Kottamasu, Raj M Ratwani, Nathan K Cobb","doi":"10.1093/jamia/ocaf052","DOIUrl":"10.1093/jamia/ocaf052","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this work is to demonstrate the value of simulation testing for rapidly evaluating artificial intelligence (AI) products.</p><p><strong>Materials and methods: </strong>Researcher-physician teams simulated the use of 2 Ambient Digital Scribe (ADS) products by reading scripts of outpatient encounters while using both products, yielding a total of 44 draft notes. Time to edit, perceived amount of effort and editing, and errors in the AI-generated draft notes were analyzed.</p><p><strong>Results: </strong>Ambient Digital Scribe Product A draft notes took significantly longer to edit, had fewer omissions, and more additions and irrelevant or misplaced text errors than ADS Product B. Ambient Digital Scribe Product A was rated as performing better for most encounters.</p><p><strong>Discussion: </strong>Artificial intelligence-enabled products are being rapidly developed and implemented into practice, outpacing safety concerns. Simulation testing can efficiently identify safety issues.</p><p><strong>Conclusion: </strong>Simulation testing is a crucial first step to take when evaluating AI-enabled technologies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"928-931"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interoperability of health-related social needs data at US hospitals.","authors":"Sahil Sandhu, Michael Liu, Laura M Gottlieb, A Jay Holmgren, Lisa S Rotenstein, Matthew S Pantell","doi":"10.1093/jamia/ocaf049","DOIUrl":"10.1093/jamia/ocaf049","url":null,"abstract":"<p><strong>Objective: </strong>To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data.</p><p><strong>Materials and methods: </strong>This study combined national data from the 2022 American Hospital Association (AHA) Annual Survey, AHA IT Supplement, and the Centers for Medicare and Medicaid Services Impact File. Multivariable logistic regression was used to identify hospital characteristics associated with receiving HRSN data from external organizations.</p><p><strong>Results: </strong>Of 2502 hospitals, 61.4% reported electronically receiving HRSN data from external sources, most commonly from health information exchange organizations. Hospitals participating in accountable care organizations or patient-centered medical homes and hospitals using Epic or Cerner electronic health records (EHRs) were more likely to receive external HRSN data. In contrast, for-profit hospitals and public hospitals were less likely to participate in HRSN data exchange.</p><p><strong>Discussion: </strong>Hospital ownership, participation in value-based care models, and EHR vendor capabilities are important drivers in advancing HRSN data exchange.</p><p><strong>Conclusion: </strong>Additional policy and technological support may be needed to enhance HRSN data interoperability.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"914-919"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The health data utility and the resurgence of health information exchanges as a national resource.","authors":"Anjum Khurshid, Indra Neil Sarkar","doi":"10.1093/jamia/ocaf032","DOIUrl":"10.1093/jamia/ocaf032","url":null,"abstract":"<p><strong>Objectives: </strong>(1) Describe the evolution of Health Information Exchanges (HIEs) into Health Data Utilities (HDUs); (2) Provide motivation for HDUs as a public strategic investment target.</p><p><strong>Materials and methods: </strong>We examine trends in developing HIEs into HDUs and compare their criticality to that of the national highway system as an investment in the public good.</p><p><strong>Results: </strong>We propose that investment in HDUs is essential for our nation's healthcare data ecosystem. This investment will address the increased need for healthcare delivery and public health data.</p><p><strong>Discussion: </strong>HDUs can meet the current and future needs of healthcare delivery and public health surveillance. Their structure and capabilities will underpin their success to support data-driven decision-making.</p><p><strong>Conclusion: </strong>Transforming HIEs into HDUs is essential to realizing the vision of a distributed and connected healthcare data system. Public funding is critical for this model's success, similar to the continued investment in the national highway system.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"964-967"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equity implications of extended reality technologies for health and procedural anxiety: a systematic review and implementation-focused framework.","authors":"Tom Arthur, Sophie Robinson, Samuel Vine, Lauren Asare, G J Melendez-Torres","doi":"10.1093/jamia/ocaf047","DOIUrl":"10.1093/jamia/ocaf047","url":null,"abstract":"<p><strong>Objectives: </strong>Extended reality (XR) applications are gaining support as a method of reducing anxieties about medical treatments and conditions; however, their impacts on health service inequalities remain underresearched. We therefore undertook a synthesis of evidence relating to the equity implications of these types of interventions.</p><p><strong>Materials and methods: </strong>Searches of MEDLINE, Embase, APA PsycINFO, and Epistemonikos were conducted in May 2023 to identify reviews of patient-directed XR interventions for health and procedural anxiety. Equity-relevant data were extracted from records (n = 56) that met these criteria, and from individual trials (n = 63) evaluated within 5 priority reviews. Analyses deductively categorized data into salient situation- and technology-related mechanisms, which were then developed into a novel implementation-focused framework.</p><p><strong>Results: </strong>Analyses highlighted various mechanisms that impact on the availability, accessibility, and/or acceptability of services aiming to reduce patient health and procedural anxieties. On one hand, results showed that XR solutions offer unique opportunities for addressing health inequities, especially those concerning transport, cost, or mobility barriers. At the same time, however, these interventions can accelerate areas of inequity or even engender additional disparities.</p><p><strong>Discussion: </strong>Our \"double jeopardy, common impact\" framework outlines unique pathways through which XR could help address health disparities, but also accelerate or even generate inequity across different systems, communities, and individuals. This framework can be used to guide prospective interventions and assessments.</p><p><strong>Conclusion: </strong>Despite growing positive assertions about XR's capabilities for managing patient anxieties, we emphasize the need for taking a cautious, inclusive approach to implementation in future programs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"945-957"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care.","authors":"Serena Jinchen Xie, Carolin Spice, Patrick Wedgeworth, Raina Langevin, Kevin Lybarger, Angad Preet Singh, Brian R Wood, Jared W Klein, Gary Hsieh, Herbert C Duber, Andrea L Hartzler","doi":"10.1093/jamia/ocaf046","DOIUrl":"10.1093/jamia/ocaf046","url":null,"abstract":"<p><strong>Objective: </strong>Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.</p><p><strong>Materials and methods: </strong>We interviewed primary care patients experiencing social needs (n = 19) and clinicians (n = 14) about their acceptability of \"SDoH autosuggest,\" an AI-based approach for extracting SDoH from clinical notes. We presented storyboards depicting the approach and asked participants to rate their acceptability and discuss their rationale.</p><p><strong>Results: </strong>Participants rated SDoH autosuggest moderately acceptable (mean = 3.9/5 patients; mean = 3.6/5 clinicians). Patients' ratings varied across domains, with substance use rated most and employment rated least acceptable. Both groups raised concern about information integrity, actionability, impact on clinical interactions and relationships, and privacy. In addition, patients raised concern about transparency, autonomy, and potential harm, whereas clinicians raised concern about usability.</p><p><strong>Discussion: </strong>Despite reporting moderate acceptability of the envisioned approach, patients and clinicians expressed multiple concerns about AI systems that extract SDoH. Participants emphasized the need for high-quality data, non-intrusive presentation methods, and clear communication strategies regarding sensitive social needs. Findings underscore the importance of engaging patients and clinicians to mitigate unintended consequences when integrating AI approaches into care.</p><p><strong>Conclusion: </strong>Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"855-865"},"PeriodicalIF":4.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}