Journal of the American Medical Informatics Association最新文献

{"title":"Small language models in clinical medicine: a systematic review of performance, safety, and deployment feasibility.","authors":"Alon Gorenshtein, Mahmud Omar, Yiftach Barash, Jonathan B Kruskal, Muneeb Ahmed, Olga R Brook, Ben Illigens, Girish N Nadkarni, Eyal Klang","doi":"10.1093/jamia/ocag164","DOIUrl":"https://doi.org/10.1093/jamia/ocag164","url":null,"abstract":"<p><strong>Objectives: </strong>To review the clinical evidence for small language models (SLMs), 4 billion parameters or fewer, for performance, safety, and deployment feasibility.</p><p><strong>Materials and methods: </strong>We searched 5 databases through February 10, 2026 for English-language reports of an SLM on a clinical task, following PRISMA 2020 under a PROSPERO-registered protocol. Two reviewers independently assessed eligibility (Cohen κ = 0.93) and rated methodological quality and transparency. We calculated a relative task score (RTS): the primary metric of each study's best-performing qualifying SLM, divided by a study-specific reference comparator selected by a uniform hierarchy, calculated by the review authors.</p><p><strong>Results: </strong>Eleven studies (7 peer-reviewed, 4 preprint or technical-report) were eligible. Across the 9 studies with a ratio-scale reference comparator, RTS ranged from 0.30 to 2.36; 1 was a raw difference on a non-ratio scale and 1 had no comparator. We did not pool estimates, given heterogeneity. Hallucination was evaluated in 6 of 11 studies, a scalar calibration-error metric or epistemic uncertainty in 0 of 11 (1 reported a calibration curve only), and on-hardware inference timing in 2 of 11. Our memory model estimated that a 4-billion-parameter model requires about 9.3 GB at a 2048-token context, indicating single-GPU memory feasibility rather than demonstrated deployment.</p><p><strong>Discussion: </strong>Domain-adapted SLMs are memory-feasible, but the evidence base is too small and heterogeneous for inferential comparison with larger models, and safety properties (including adversarial robustness) go unmeasured.</p><p><strong>Conclusion: </strong>Routine reporting of calibration and uncertainty and measurement of end-to-end clinical performance are prerequisites for responsible SLM deployment.</p><p><strong>Prospero registration: </strong>CRD420261331444.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comments on extensions to the fundamental theorem of Biomedical Informatics for the Artificial Intelligence era.","authors":"Vimla L Patel, Edward H Shortliffe","doi":"10.1093/jamia/ocag170","DOIUrl":"https://doi.org/10.1093/jamia/ocag170","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orchestrator multi-agent clinical decision support system for secondary headache diagnosis in primary care.","authors":"Xizhi Wu, Hang Zhang, Nelly Estefanie Garduno-Rapp, Justin F Rousseau, Mounika Thakkallapally, Yuelyu Ji, Shyam Visweswaran, Yifan Peng, Yanshan Wang","doi":"10.1093/jamia/ocag111","DOIUrl":"10.1093/jamia/ocag111","url":null,"abstract":"<p><strong>Objective: </strong>Secondary headaches require urgent recognition due to potentially devastating consequences if untreated. Despite established clinical \"red flag\" criteria, identifying patients needing immediate evaluation remains challenging in primary care. This study developed and evaluated a large language model (LLM)-based multi-agent clinical decision support system for interpretable secondary headache diagnosis.</p><p><strong>Materials and methods: </strong>We first established 7 clinically relevant secondary headache red flag domains through manual review and synthesis of clinical guidelines. Based on these domains, we designed an LLM-based system using an orchestrator-specialist multi-agent architecture that decomposes diagnostic reasoning into 7 guideline-aligned agents corresponding to key red flag features. Each agent generates structured, evidence-grounded reasoning, coordinated by a central orchestrator. The system was evaluated on 90 expert-validated secondary headache cases and compared with a single-LLM baseline under 2 prompting strategies: question-based prompting (QPrompt) and guideline-based prompting (GPrompt). Five open-source LLMs (Qwen-8b, Qwen-14b, Qwen-30b, GPT-OSS-20b, and Llama-3.1-8b) were tested.</p><p><strong>Results: </strong>The orchestrated multi-agent system with GPrompt achieved the highest red flag classification performance across models, measured by F1 score. Performance gains were consistent and more pronounced in smaller LLMs, suggesting that structured reasoning improves efficiency and accuracy beyond prompt engineering alone. The framework also produced transparent and guideline-aligned intermediate reasoning.</p><p><strong>Discussion: </strong>Decomposing clinical reasoning into specialized agents enhances interpretability and diagnostic reliability compared with monolithic LLM approaches. Multi-agent orchestration provides a clinically aligned framework for explainable decision support.</p><p><strong>Conclusion: </strong>An orchestrator-specialist multi-agent LLM framework improves secondary headache diagnosis accuracy and transparency, supporting the development of explainable AI systems for time-constrained clinical decision-making in primary care.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1910-1920"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13419605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340822","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 carer perceptions and experiences of technology-facilitated appointment reminders in healthcare: a scoping review.","authors":"Anastasia Rousaki, Debra Jones, Dawn Dowding, Norina Gasteiger","doi":"10.1093/jamia/ocag112","DOIUrl":"10.1093/jamia/ocag112","url":null,"abstract":"<p><strong>Background and aim: </strong>Missed healthcare appointments, or \"Did Not Attends\" (DNAs), pose challenges for healthcare systems. Technology-facilitated reminder systems, such as SMS and phone calls are often implemented to improve attendance. This scoping review aimed to map the evidence on patient and carer perspectives and experiences of technology-enabled appointment reminders in healthcare and, secondly, examine how equity-relevant characteristics were reported.</p><p><strong>Methods: </strong>A scoping review which searched EMBASE, MEDLINE, PsycINFO, Web of Science, Scopus, and EBSCO from September to October 2025 for studies published between 2010 and 2025. Studies examining patient or informal/unpaid carer perceptions and experiences of technology-based reminder systems were included. Equity-relevant dimensions were examined using the PROGRESS-Plus framework. Data were extracted and summarised using a structured form; with findings synthesised thematically.</p><p><strong>Results: </strong>A total of 45 studies were included. Most focused on patient perspectives, with comparatively fewer centered specifically on carers. The majority were cross-sectional surveys, with fewer longitudinal and theory-informed designs. SMS reminders were the most studied modality, followed by phone calls and app-based notifications. Patients and carers reported high satisfaction. Recurring barriers included digital exclusion, language challenges, confidentiality concerns, and socio-economic inequalities. Equity reporting across studies was inconsistent, with many studies describing sociodemographic characteristics without examining how they influenced engagement with reminder systems.</p><p><strong>Conclusions: </strong>Technology-enabled appointment reminders are widely perceived as useful. Digital exclusion, privacy, language, and trust-related barriers limit engagement and access. Greater user involvement is needed to ensure reminder systems support, rather than reproduce, healthcare inequalities. Future research and implementation efforts should prioritise equity-informed design and evaluation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2029-2039"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148320413","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":"Human factors methods for designing safe health information technology: what do the experts think?","authors":"Selvana Awad, Rachel Begg, Thomas Loveday, Andrew Baillie, Melissa T Baysari","doi":"10.1093/jamia/ocag106","DOIUrl":"10.1093/jamia/ocag106","url":null,"abstract":"<p><strong>Objective: </strong>Human Factors (HF) methods have potential to ensure the design of health information technology (HIT) is safe and usable, but limited research has examined what HF methods have been applied or could be applied to HIT. This study aimed to identify key HF and safety analysis methods used and/or recommended by HF experts working in healthcare or non-healthcare industries that may be suitable for supporting the design, redesign and configuration of HIT.</p><p><strong>Materials and methods: </strong>We ran semi-structured interviews with 21 HF experts working across health and non-health industries to identify key HF and safety analysis methods used and/or recommended for supporting the design, redesign and configuration of HITs.</p><p><strong>Results: </strong>Forty-seven HF methods across 11 method categories were reported by HF experts. Non-health participants reported a wider range of Human Error Identification and Risk Assessment Methods and were the only cohort to report workload assessment methods. Both health and non-health participants described their approaches for selecting methods and recommended that methods be applied flexibly.</p><p><strong>Discussion: </strong>This study highlights the need to uplift HF capability by teaching methods to system designers, particularly where access to HF experts is limited and undertaking efforts to learn from HF practice in safety-critical industries outside of healthcare.</p><p><strong>Conclusion: </strong>Further work is required to integrate HF methods and approaches, especially those focused on safety, into the work of HIT designers.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1888-1897"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347206","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":"Real-time EHR secure messaging to coordinate emergency department disposition for 30-day revisit patients.","authors":"Priyanka Solanki, William Small, Jaya Sondhi, Simon Jones, Nicholas Genes, Ajay Mansukhani, Dinesha Prabhu, Reed Turley, Edwin Pineda, David Johnson, Benjamin Moeller, Brian Bosworth, Jonathan Austrian","doi":"10.1093/jamia/ocag128","DOIUrl":"10.1093/jamia/ocag128","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate whether real-time electronic health record (EHR)-based secure messaging between emergency department (ED) clinicians and prior discharge teams influences ED disposition decisions for patients re-presenting within 30 days of hospital discharge.</p><p><strong>Materials and methods: </strong>This 18-month pre-post study included 27 592 ED revisit encounters across 3 campuses within one academic healthcare system. Robotic process automation generated a real-time EHR secure message connecting the ED attending with the index hospitalization discharge team during the ED disposition window. The primary outcome was the proportion of encounters resulting in ED disposition changes. Secondary outcomes included length of stay and messaging engagement by service, hospital campus, and time of day.</p><p><strong>Results: </strong>Systemwide, inpatient readmissions rates were unchanged (49.4% vs 48.6%, P = .149), and observation status increased (7.0% vs 8.0%, P < .001). At one campus where messaging was paired with proactive care coordination, inpatient admissions decreased (56.9% vs 54.1%, P = .029) and treat-and-release increased (36.8% vs 39.6%, P = .024). Overall, 61.8% of messages received a response, but engagement did not correlate with disposition changes systemwide.</p><p><strong>Discussion: </strong>Disposition changes occurred only where messaging was integrated with care coordination workflows with the operational capacity to act, indicating that real-time communication alone is insufficient without supporting infrastructure.</p><p><strong>Conclusion: </strong>Real-time EHR messaging may be most effective when paired with structured care coordination models rather than deployed as a standalone alerting tool.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1957-1966"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632401","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 consensus-based CINEX guideline for reporting clinical information extraction studies.","authors":"Daniel Reichenpfader, Jamil Zaghir, Elsa Cécilia-Joseph, Boris Delange, Anastassia Shaitarova, Emmanuelle Sylvestre, Fabio Dennstädt, Boya Zhang, Douglas Teodoro, Stéphane Meystre, Henning Müller, Kerstin Denecke","doi":"10.1093/jamia/ocag136","DOIUrl":"10.1093/jamia/ocag136","url":null,"abstract":"<p><strong>Objective: </strong>Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies.</p><p><strong>Material and methods: </strong>We developed the Clinical Information Extraction Reporting Guideline (CINEX) through a multi-phase process. The initiative was prospectively registered on the EQUATOR Network as a reporting guideline under development, and a detailed Delphi study protocol was published in advance. A scoping review informed an initial set of items, which was refined through a 3-round electronic Delphi study with 20 international experts, followed by a final consensus meeting. Items were iteratively refined based on predefined inclusion criteria and expert feedback.</p><p><strong>Results: </strong>The final CINEX guideline comprises 29 checklist items grouped into 5 domains: information model, architecture, data, annotation, and outcomes. The 3 eDelphi rounds included 20, 15, and 12 experts, respectively. Two items were added after round one. Consensus for inclusion was reached for a 21 and additional 7 items after rounds 2 and 3, respectively.</p><p><strong>Discussion: </strong>CINEX provides a structured framework to improve transparency, reproducibility, and interpretability in clinical IE research. By standardizing reporting of key methodological components, such as data provenance, annotation processes, and evaluation strategies, it facilitates meaningful comparison across studies and supports safer clinical implementation.</p><p><strong>Conclusion: </strong>CINEX complements existing AI reporting standards by addressing domain-specific challenges in clinical IE.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2040-2050"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698204","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":"Correction to: A comparison of Fast Healthcare Interoperability Resources and Observational Medical Outcomes Partnership electronic health record data within the All of Us Research Program.","authors":"","doi":"10.1093/jamia/ocag134","DOIUrl":"10.1093/jamia/ocag134","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2076"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766389","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":"Association of virtual nursing implementation with documentation completeness and interruptions in emergency departments.","authors":"Saif Khairat, Jennifer Morelli, Shiyi Yang, Randy Fakhreddin, Barbara Edson, Mauri Williams, Jamie Crandell","doi":"10.1093/jamia/ocag145","DOIUrl":"https://doi.org/10.1093/jamia/ocag145","url":null,"abstract":"<p><strong>Background: </strong>Virtual nursing (VN) is an increasingly popular nursing care model being implemented nationally to help alleviate nursing burden and shortages which are particularly prevalent in emergency care.</p><p><strong>Aims: </strong>This study aimed to evaluate the impact on documentation-related and workflow outcomes among adult patients admitted through the emergency department (ED) who received either a traditional in-person admission assessment or a VN admission assessment. Outcomes included admission assessment duration, documentation completeness, and number of interruptions.</p><p><strong>Methods: </strong>This study was a non-randomized retrospective cohort study of adult patients over the age of 18 seen in the ED of 2 Southeastern hospitals and subsequently admitted for inpatient care. Retrospective data were extracted from the EHR and included patients seen in the ED and subsequently admitted from August 2022 through December 2023.</p><p><strong>Results: </strong>Patients who utilized VN for their admission had an overall longer admission assessment duration (on average 3.16 minutes longer). Virtual nursing admission assessments were associated with a statistically significant increase in the number of completed admission assessment tasks and a significant reduction in the number of admission interruptions compared to in-person encounters.</p><p><strong>Conclusions: </strong>Virtual nursing for admission assessments was associated with greater documentation completeness and fewer interruptions, with only a modest increase in assessment duration. These findings highlight the potential of VN to help mitigate the challenges associated with nursing shortages and interruptions in emergency care settings.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health technology burden and frustration among patients with multimorbidity.","authors":"Haoxin Chen, Jiancheng Ye","doi":"10.1093/jamia/ocag090","DOIUrl":"10.1093/jamia/ocag090","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to characterize patterns of digital health technology (DHT) use and examine the relationships between multimorbidity, DHT adoption, and user-reported frustration, with a focus on identifying socioeconomic and phenotypic determinants of digital health burden.</p><p><strong>Materials and methods: </strong>A cross-sectional analysis was conducted using nationally representative public data from Health Information National Trends Survey (HINTS) 7. Adults with at least one chronic condition were included (N = 3753). The outcomes were the number of DHTs used and frustration with digital tools. Multivariable logistic regression models were employed, adjusting for sociodemographic and clinical variables. Phenotypic subgroup analyses were conducted based on multimorbidity, DHT use, and frustration patterns.</p><p><strong>Results: </strong>Among 3753 participants, 46.9% had multimorbidity. Participants with multimorbidity reported using a greater number of DHTs on average (mean = 3.9 vs 3.6, P < .001) compared to those with a single condition, yet exhibited significantly higher rates of frustration with digital tasks (61.2% vs 54.1%, P < .001). Both higher income and educational groups were associated with lower odds of frustration. Greater DHT use was also independently associated with reduced frustration (OR = 0.78, 95% CI: 0.71-0.87, P < .01). Phenotypic subgroup analysis further identified individuals with multimorbidity and low DHT versatility as the most vulnerable profile, characterized by older age, lower socioeconomic status, and the highest frustration prevalence (60.3%).</p><p><strong>Conclusion: </strong>While individuals with multimorbidity use more DHTs, they experience greater frustration, particularly those with lower socioeconomic status. However, higher engagement with DHTs (as measured by number of technology types adopted) is associated with lower frustration, suggesting that technology proficiency may mitigate burden. Targeted, equity-conscious interventions, including competency-based digital literacy programs, simplified user-centered design, and structural supports addressing device access and connectivity, are needed to reduce digital health burden and improve outcomes for patients living with multiple chronic conditions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1852-1865"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303869","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}