A randomised trial of AI scribes in oncology clinics measuring errors and time
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?
Ambient documentation tools built on large language models are being adopted widely 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).
- AI that is built but not validated or deployed · Thousands of cancer AI models are published; a handful are in clinical use, and fewer have shown they help patients.
- Not enough oncologists, nurses, pathologists, physicists · The number of people with cancer is rising faster than the workforce trained to treat them.
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