Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology.

Accuracy of Retrieval-Augmented Large Language Model-Generated Preconsult Summaries in Breast Surgical Oncology.

Generating Patient Documents from Electronic Health Records Using Generative Artificial Intelligence: A Feasibility Study in a Japanese Cancer Center

Abstract Objectives Clinical documentation consumes substantial clinician time, potentially detracting from patient care. Generative artificial intelligence (AI) may support drafting discharge summaries and patient referral documents, but feasibility in non-Western-language oncology settings using real-world electronic health record (EHR) data remains insufficiently evaluated. This study assessed feasibility in a Japanese cancer hospital using an enterprise AI system. Methods Medical records from 61 consenting adult patients at Chiba Cancer Center were analyzed. Although the plan aimed at comprehensive EHR data, actual input was limited to extractable text (physician notes, nursing records); structured laboratory data and imaging, endoscopy, and pathology reports were not directly used, and existing summaries and external referrals were excluded to avoid information leakage. Data were converted to JavaScript Object Notation; GaiXer generated 31 discharge summaries and 30 referral documents. Four evaluators scored them; ≥80/100 was an exploratory threshold for draft-level practical utility. Feedback drove one refinement cycle. Results Generated documents scored approximately 60 to 70. A score ≥80 was reached by 9 of 31 discharge summaries in each evaluation; for referrals, none reached the threshold initially, whereas 5 of 30 did after refinement. Discharge summary scores did not substantially improve; referral scores did. Raw percent agreement among three nonphysician evaluators was high, although chance-corrected agreement varied. Wilcoxon signed-rank tests showed no significant change for discharge summaries ( p  = 0.866) but significant improvement for referrals ( p  = 0.006). Conclusion This feasibility study suggests AI may support drafting these documents in a secure environment using real-world Japanese EHR data, although the generated documents did not consistently reach the predefined threshold for draft-level utility. Findings should not be interpreted as demonstrating workload reduction or maximum performance under ideal data conditions. Future studies should evaluate larger datasets, multiple institutions and models, blinded evaluations, actual editing time, clinician acceptance, and workflow impact.

The daily dose: Early usability of an LLM tool for patient summaries and trial matching in radiation oncology

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.

Radiologically Relevant Clinical History Summarization with Large Language Models: A Multireader Performance Study.

LLMs generated radiology-relevant indications from clinical notes that were more comprehensive and factual than clinician indications, and when generated by the proprietary LLM, were ranked most useful in protocoling and imaging interpretation.

A Modular Evaluation of AI-Assisted Clinical Documentation

The results support the use of this modular AI-assisted clinical documentation pipeline as a human-supervised draft-generation tool that still requires clinician review, local workflow evaluation, and prospective clinical validation before broader deployment.

Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges

The study concludes that evidence-based, auditable, locally adaptable, locally adaptable, and supervised by licensed clinician retrieval systems with generative AI can support safer, faster, and more relevant decision-making processes in clinical settings.