Simulation-Based Evaluation of a Large Language Model–Enabled Clinical Decision Support Platform in Oncology

PURPOSE: A core clinical task is to synthesize fragmented patient data into a coherent summary to support decision making. However, electronic health record (EHR) inefficiencies burden clinicians and contribute to their cognitive overload and burnout. This study evaluated the impact of a large language model (LLM)-enabled clinical decision support (LLM-CDS) platform compared with a simulated EHR (SimEPR) on workflow efficiency and user experience in generating accurate clinical summaries during tumor board preparation and explored its applicability to consultation preparation, referrals, treatment planning, and patient communication. METHODS: In a remote, within-participant simulation, 26 oncologists from the United Kingdom, United States, Spain, and Singapore reviewed synthetic breast cancer cases and created comprehensive summaries for tumor board discussions using both LLM-CDS and SimEPR. LLM-CDS provided editable LLM-generated summaries; SimEPR required manual composition. Time to task completion was recorded. An independent reviewer assessed summary quality based on completeness, correctness, and conciseness. Participants also completed surveys on usability, cognitive load, and feature acceptability. RESULTS: 3.13), whereas correctness and conciseness were similar. Overall, 87% of participants would recommend LLM-CDS and 96% would anticipate time savings. The system usability scale score for LLM-CDS was 65.7. Although perceived cognitive load was lower with LLM-CDS, the difference was not statistically significant. The LLM summary was the most valued, with 92% finding it useful for the tumor board and consultation preparation. CONCLUSION: The LLM-CDS platform improved the efficiency and completeness of clinical summarization. Strong user acceptance and anticipated time savings underscore the potential for streamlining a range of oncology workflows.