Simulation-Based Evaluation of a Large Language Model-Enabled Clinical Decision Support Platform in Oncology
Open accessAbstract
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: LLM-CDS significantly reduced the summary completion time compared with SimEPR (6:55 v 8:47 minutes; P < .001). Summary completeness was rated higher with LLM-CDS (mean score, 3.93 v 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.
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Retrieval-augmented generation, domain-specific fine-tuning, and deployment within structured clinical-decision-support platforms have been associated with improved guideline concordance in oncology settings.
“Retrieval-augmented generation grounded in current NCCN and ESGO guideline corpora, domain-specific fine-tuning on curated gynecologic oncology cases, and deployment within structured clinical-decision-support platforms have each been associated with improved guideline concordance in oncology settings [28,36]”
From Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study (2026) · cites this paper as [36]
The Introduction describes an oncology clinical-decision-support platform with a Guidelines tool, but the Methods and Results evaluate clinical-summary efficiency, completeness, correctness, and conciseness—not guideline concordance. Searches for guideline concordance, retrieval-augmented generation, fine-tuning, NCCN, and ESGO found no passage establishing the claimed association. (LLM, conf=0.76)
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References 26 · Cited by 2
- Authors
- Lajmi, N., Patel, M., Obery, G., Dorge, A., Sharma, A., Halligan, J., & Lo, E.
- Affiliations
- Roche Information Solutions, Roche Diagnostics, Indianapolis, IN
- Imperial College London (GB)
- Prova Health Ltd, London, United Kingdom
- Roche (United States) (US)
- Roche Information Solutions, Roche Diagnostics, Pune, Maharashtra, India
- Corresponding author
- Lajmi, Nesrine
- Venue
- JCO Clinical Cancer Informatics
- Published
- 2026-01
- Volume
- 10
- Issue
- 1
- Pages
- e2500244-e2500244
- ISSN
- 2473-4276 (ISSN-L)
- PMC
- PMC12863596
- OpenAlex
- W7125969587
- Cited by
- 2
- References
- 26
- Citations by year
- 2026: 2
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- JCO Clinical Cancer Informatics (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- large language models, clinical decision support, oncology, multidisciplinary tumor board, clinical summarization, electronic health records, workflow efficiency, cognitive load, usability, breast cancer, user acceptance
- MeSH terms
- Large Language Models, Computer Simulation, Female, Humans, Language, Medical Oncology, Neoplasms, Decision Support Systems, Clinical, Workflow, Electronic Health Records
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