{"entity":{"id":"idea-data-llm-documentation-rct-oncology","kind":"idea","name":"A randomised trial of AI scribes in oncology clinics measuring errors and time","aka":[],"tldr":"AI tools that write clinic notes are spreading fast in cancer clinics. Test them properly: do they save time, do they make mistakes about drugs and doses, and do patients notice a difference?","summary":"Ambient documentation tools built on large language models are being adopted across clinics without randomised evidence, and oncology notes carry high-stakes details (regimens, doses, trial eligibility, goals of care). The proposal is a multi-centre randomised trial of AI scribes versus usual documentation in oncology clinics, with primary outcomes of clinically significant documentation errors (blinded audit), clinician time and burnout, and patient-reported communication quality, plus a secondary analysis of structured data completeness (mCODE elements captured).","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021)","url":"https://doi.org/10.1038/s41591-021-01312-x"}],"tags":[],"related":[],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation","b-workforce"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"AI scribes will reduce documentation time and burnout but will introduce a non-trivial rate of clinically significant errors in oncology-specific content unless paired with structured verification, and the trial will quantify both.","rationale":"Early observational reports show time savings and occasional hallucinated content; the trade-off in oncology, where a wrong dose or regimen in the note propagates, must be measured rather than assumed.","test":"Randomise 200 oncologists across ten centres for six months; audit 5,000 notes blinded for errors; measure time, burnout and patient experience.","maturity":"early-clinical","actor":"research","cost":"medium","horizonYears":2},"route":"/ideas/idea-data-llm-documentation-rct-oncology/","neighbours":{"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-workforce","kind":"bottleneck","name":"Not enough oncologists, nurses, pathologists, physicists","route":"/bottlenecks/b-workforce/"}],"paper":[{"id":"paper-wu-nat-med","kind":"paper","name":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","route":"/key-papers/paper-wu-nat-med/"}]}}