The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
This paper describes The Daily Dose (TDD), an LLM-driven automated clinical summarization and trial identification system integrated into routine radiation oncology practice. A mixed-methods evaluation with 55 clinicians shows promising usability, satisfaction, and time savings.
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Key points
- TDD uses RadOnc-GPT to generate daily physician-specific email summaries including patient schedules, EHR-derived clinical status, and identification of relevant clinical trials.
- Among 55 respondents, 94.5% worked in radiation oncology, 69.1% were attending physicians, and 83.6% used TDD daily or several times per week.
- Mean usability and satisfaction score was 3.89 on a 5-point Likert scale, and overall satisfaction was positively associated with perceived time savings.
Why it matters
This matters because TDD uses RadOnc-GPT to generate daily physician-specific email summaries including patient schedules, EHR-derived clinical status, and identification of relevant clinical trials.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26346] The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
[Submitted on 25 May 2026]
Title:The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
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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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
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