The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
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Computer Science > Computation and Language
arXiv:2605.26346 (cs)
[Submitted on 25 May 2026]
Title:The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
Authors:Jason Holmes, Federico Mastroleo, Mariana Borras-Osorio, Srinivas Seetamsetty, Satomi Shiraishi, Mirek Fatyga, Judy C. Boughey, Cornelius A. Thiels, William G.Breen, Daniel J. Ma, Daniel K. Ebner, David M. Routman, Brady S. Laughlin, Carlos E. Vargas, Samir H. Patel, Sujay A. Vora, Nadia N. Laack, Andrew Y.K. Foong, Wei Liu, Mark R. Waddle
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Abstract:Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-trial identification system integrated into routine radiation oncology practice. Design: Mixed-methods evaluation using a cross-sectional, anonymous clinician survey administered after 1 month of system deployment. Exposure: Daily automated delivery of physician-specific email summaries generated using RadOnc-GPT, including patient schedules, concise EHR-derived clinical-status summaries, and automated identification of potentially relevant clinical trials for new or consult visits. Main Outcomes and Measures: Primary outcomes included self-reported usability, satisfaction, perceived usefulness, perceived impact on workflow, time savings, and intention for continued use. Internal consistency reliability was assessed using Cronbach's $\alpha$. Results: Among 55 respondents, 52 (94.5\%) worked in radiation oncology, and 38 (69.1\%) were attending physicians. Most participants (83.6\%) reported using TDD daily or several times per week. Mean (SD) scores 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 (5-point Likert scale). Overall satisfaction was positively associated with perceived time savings ($p < .001$). Participants reported variable time savings, with 27\% estimating $\geq 10$ minutes saved per day. The questionnaire demonstrated excellent internal consistency (overall Cronbach's $\alpha$ = 0.97).
| Comments: | 28 pages, 4 figures, 1 table |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.26346 [cs.CL] |
| (or arXiv:2605.26346v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26346
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology, by Jason Holmes and 19 other authors
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