Aug 2026· JAMA Network Open· Vol 9, pp. e2629579· 0 citations· 29 references
Medicine
TL;DR
The findings of this study suggest that an LLM-powered human-in-the-loop agentic workflow could accurately triage surgical patients for a surgical comanagement service.
Abstract
Key Points Question Can surgical patient triage be automated using a large language model (LLM) agentic workflow? Findings In this quality improvement study, the LLM tool recommended hospitalist consultation for nearly a quarter of the 6193 triaged cases. The tool achieved 94% sensitivity and 74% specificity, and post hoc medical record review suggested that most discrepancies reflected modifiable gaps in clinical criteria, institutional workflow, or physician practice variability, rather than LLM misclassification. Meaning The findings of this study suggest that an LLM-powered human-in-the-loop agentic workflow could accurately triage surgical patients for a surgical comanagement service.
The VLM demonstrated reliable clinical interpretation and an acceptable safety profile, however its integration into clinical workflows for early recognition of physiological deterioration and patient acuity assessment requires further rigorous evaluation and comparison to currently used track-and-trigger systems and patient monitoring methods.
I. Strechen, P. Krishnan, O. Kilickaya et al.· International Journal of Med...· 0 citations
Background Radiation oncology workflows generate large volumes of electronic health record (EHR) data requiring daily synthesis. Large language model (LLM)-based automation is promising, but workflow-embedded implementations at scale remain limited. We describe the design and early usability and adoption evaluation of The Daily Dose (TDD), an LLM-driven system for automated clinical summarization and trial identification in radiation oncology. Materials and methods TDD delivers physician-specific email summaries each morning across three Mayo Clinic campuses using RadOnc-GPT (GPT-4o) to generate EHR-derived patient summaries and identify potentially eligible clinical trials for new or consult visits. One month post-deployment, an anonymous cross-sectional survey adapted from the System Usability Scale and Technology Acceptance Model was administered to all recipients. Results Fifty-five of 110 users responded (50%); 94.5% were in radiation oncology and 69.1% were attending physicians. Overall, 83.6% used TDD at least several times per week. Mean domain scores (5-point Likert) were 3.89 ± 1.04 for usability and satisfaction, 3.43 ± 1.24 for perceived usefulness, and 3.80 ± 1.17 for impact and future use. Satisfaction was significantly associated with perceived time savings (p < 0.001); 27% estimated saving ≥10 min daily. Internal consistency was high (α = 0.97). Free-text responses highlighted improved preparedness and patient-context awareness but noted occasional inaccuracies and imperfect trial matching. Conclusion In this early usability and adoption evaluation, a workflow-integrated LLM summarization tool was widely adopted and generally favorably perceived. These findings reflect user perceptions; objective validation of summary accuracy, trial-matching performance, and workflow efficiency is needed to establish clinical impact.
J. Holmes, F. Mastroleo, M. Borras-Osorio et al.· Clinical and Translational R...· 0 citations
Current evidence suggests that LLMs have considerable potential to support future ICU handoff workflows but substantial evidence gaps remain and prospective evaluation in real-world ICU settings, with clinically meaningful safety metrics and structured communication frameworks, is needed.
S. Bains, Michael R Kolesnikov, S. Bedrick et al.· Considerations in Medicine· 0 citations
BACKGROUND
Although interoperability advances and policy initiatives have expanded EHR functionality and were intended to streamline clinical workflows, many administrative tasks remain burdensome.
OBJECTIVE
To describe the prevalence and co-occurrence of three administrative burdens in family medicine and to assess associations of health information technology (health IT) and organizational resources with these burdens.
DESIGN
Cross-sectional study PARTICIPANTS: In total, 8419 US family physicians completing American Board of Family Medicine certification requirements in 2024.
MEASUREMENTS
Self-reported effort spent tracking down external health information and completing prior authorizations and time spent documenting clinical care outside regular office hours. Key independent variables included perceived EHR support for obtaining external information, ability to complete prior authorizations within the EHR, and use of documentation support tools including scribes, other staff, transcription tools, and EHR templates.
RESULTS
Respondents were 46% female and 60% under age 50. More than three-quarters of physicians reported at least one substantial administrative burden, and 15% experienced substantial burden from all three activities. Satisfaction with EHR support for obtaining external information was associated with lower likelihood of substantial effort for that task (OR 0.47, P < 0.001), whereas ability to complete prior authorizations within the EHR was not associated with lower prior authorization burden. Use of staff support and EHR templates rated as helpful were associated with lower likelihood of substantial after-hours documentation (staff support OR 0.83, P < 0.001; templates OR 0.70, P < 0.001) and of experiencing the triple burden (templates OR 0.63, P < 0.001).
LIMITATIONS
Cross-sectional, self-reported data from a single physician specialty may limit generalizability.
CONCLUSION
Administrative burdens remain common in family medicine. Interoperability and documentation supports may mitigate some burdens, whereas prior authorization burden persists despite current electronic capabilities.
PRIMARY FUNDING SOURCE
US Department of Health and Human Services, Office of the National Coordinator for Health IT.
Meghan Gabriel, Chelsea Richwine, R. Phillips et al.· Journal of general internal...· 0 citations
Objective To systematically examine different clinical data modalities in large language models (LLMs) and multimodal large language models (MLLMs), and to quantify the contribution of data modalities in early inpatient risk prediction and decision support tasks. Materials and Methods We conducted a systematic analysis using MIMIC-IV, MIMIC-IV-Note, and MIMIC-CXR-JPG datasets to create a unified cohort of 22,254 hospital admissions containing structured electronic health records (EHRs), radiology reports (clinical notes), and chest X-ray images. We evaluated general-purpose and medical-adapted LLM/VLMs across uni-, bi-, and tri-modal configurations on two risk prediction tasks (in-hospital mortality and length-of-stay [LOS] prediction) and two clinical decision support (CDS) tasks (discharge diagnosis phenotyping and medication-use prediction). Results For risk prediction tasks, structured EHR data alone achieved the best or comparable performance (best mortality AUROC: 0.849; LOS AUROC: 0.868), with limited incremental benefit observed from adding radiology reports or medical images. For CDS tasks, multimodal integration yielded substantial improvements: the best tri-modal configuration achieved F1-scores of 0.589 (diagnosis) and 0.405 (medication), representing 21.4% and 18.4% improvement over the best unimodal approach. Radiology reports consistently outperformed raw single-view chest radiographs as a supplementary modality. MLLMs demonstrated better zero- and few-shot performance than unimodal LLMs. Multi-view imaging consistently improved performance over single-view across all tasks. Conclusion The benefits of multimodal data integration are task-dependent. Healthcare LLMs should examine clinical data modalities according to specific tasks for efficient integration. These findings provide practical guidance for designing efficient clinical decision support systems.
Cheng Peng, Mengxian Lyu, Ziyi Chen et al.· JAMIA Journal of the America...· 0 citations
Despite decades of health-care digitalisation efforts worldwide, health information systems remain highly fragmented, with multiple vendor-specific silos that communicate through incomplete solutions. This fragmentation prevents the creation of real-time, lifelong patient health records and becomes increasingly problematic as demand grows for person-centred care, data-driven clinical practice, and greater patient involvement in health-care decisions. To address these challenges and establish a foundation for a nationwide electronic health record (EHR), the Spanish Ministry of Health commissioned a steering committee to develop recommendations based on a comprehensive national consensus. The committee conducted a Delphi study comprising 45 items across four domains, which was distributed to 220 experts from June 23, 2023, to Sept 26, 2023. With a response rate of 69·1% (152/220), the study achieved consensus in a single round, with all items reaching the pre-established threshold of greater than or equal to 70% agreement (scores 7-9 on a 9-point Likert scale), and consensus ranging from 118 (77·6%) to 151 (99·3%) of 152 responses (44 items ≥80%). The resulting recommendations were externally validated by an international advisory board, which assessed their consistency and alignment with global best practices and standards. The final set included 20 recommendations across four domains: justification of need (2 items), functional characteristics (7 items), technical characteristics (6 items), and governance (5 items). These recommendations provide a roadmap for developing a robust, integrated national health information system centred on a standardised, longitudinal EHR. The proposed approach moves beyond generic calls for interoperability by embedding clinical knowledge into open, standardised EHR architectures through ontology-driven semantic integration, supported by federated governance and citizen-controlled data use. This roadmap equips Spain to implement a longitudinal, knowledge-driven national record while providing a scalable model for other countries transforming fragmented health information systems.
J. Piera-Jiménez, Isaac Cano, G. Carot-Sans et al.· The Lancet Digital Health· 0 citations
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