AI-supported first consultation preparation in oncology: a real-world clinical usability study

Abstract Large Language Models (LLMs) may support clinicians by facilitating information management, but their usability in real-world oncology practice remains understudied. This study evaluated the workflow performance of Artificial Intelligence-based First Consultation Preparation (AI-FCP), generating structured summaries from unstructured clinical documentation, co-created with end users. In a prospective single‑group pre‑post design, nine clinicians completed a person-level baseline, followed by 33 consultations with AI-FCP. Case numbers varied by clinic scheduling and enrollment timing, potentially introducing survivorship bias. Usability was evaluated using validated questionnaires and study-specific instruments. Post AI-FCP, overall perceived workload (NASA-TLX) was 28.9% lower than baseline (Hodges-Lehmann estimate difference −12.3; 95% CI –21.7 to –3.3), with self-reported average time savings of 3.26 min (SD = 2.65) per case. Preparation succeeded in 84.9% of consultations; 63.6% of summaries were complete. These exploratory findings suggest clinicians perceived lower workload and reported improved workflow efficiency with AI-FCP. Further research is needed to determine generalizability.