Come valutiamo il rischio di allucinazione dell'IA clinica
La storia delle prove di Notat non è una percentuale di accuratezza magica. È una metodologia trasparente: estrarre prima i fatti clinici, generare le note da quei fatti, mostrare il contesto grezzo al clinico e rivedere ogni output rispetto alle evidenze.
Metodo di valutazione
1. Build the reference facts
A reviewer reads the encounter material and creates a reference set of clinical facts: symptoms, negatives, medication changes, assessment, plan, safety-netting, and coding-relevant details.
2. Compare transcript-direct output
We generate a note from transcript-style input and mark unsupported statements, missing high-salience facts, incorrect attribution, and invented certainty.
3. Compare FactsContext output
We generate documentation from extracted facts and review whether each sentence is supported by a visible fact. The clinician-facing fact list is evaluated as part of the output, not hidden.
4. Record limits, not magic numbers
We do not publish fake accuracy percentages. Each evaluation note includes dataset scope, review date, known limitations, and examples of what the system still requires clinicians to verify.
Cosa contrassegnano i revisori
Unsupported clinical assertion
Wrong medication, dose, frequency, or route
Missing red-flag negative or safety-net advice
Wrong diagnosis certainty: possible vs established
Wrong speaker attribution
Unsupported ICD-10 suggestion
Clinician-visible evidence for each key statement
Facts reusable for notes, codes, referrals, and patient instructions
Nota interna datata
Nota di valutazione corrente, 2026-07-01: questa pagina documenta il metodo e gli esempi qualitativi utilizzati per valutare l'architettura FactsContext. Non rivendica una convalida esterna né un'accuratezza universale. La prossima pietra miliare delle prove dovrebbe essere una revisione in cieco, stratificata per specialità, con dimensione del dataset, accordo tra revisori e tasso di affermazioni non supportate riportati apertamente.
Estrazione di fatti vs generazione diretta da trascrizione
Medication change discussed twice
Rischio diretto da trascrizione
“Increase amlodipine to 10 mg and stop lisinopril.” The transcript contained a correction: the clinician first considered stopping lisinopril, then decided to continue it after reviewing renal function.
Output di FactsContext
Facts: amlodipine increased to 10 mg daily; lisinopril continued; renal function normal; review in 6 weeks. Note generated from those facts only.
Perché è importante
Transcript-direct generation can smooth over corrections. FactsContext preserves the final decision as a discrete fact before writing.
Negative finding matters
Rischio diretto da trascrizione
“No neurological symptoms.” The actual encounter only documented no saddle anesthesia and no bladder symptoms; leg radiation was present.
Output di FactsContext
Facts: left leg radiation to calf; SLR positive left; no saddle anesthesia; no bladder or bowel symptoms. Note keeps the negatives specific.
Perché è importante
Broad negative statements are risky. The fact list keeps the clinical context granular and reviewable.
Code suggestion requires evidence
Rischio diretto da trascrizione
Suggested J44.1 for COPD exacerbation without showing the symptom or treatment evidence.
Output di FactsContext
Facts: increased breathlessness, purulent sputum, prednisolone burst, antibiotics started. Suggested J44.1 with those facts as evidence.
Perché è importante
The code is easier to verify because the reason for the suggestion is visible, not buried in prose.
Limiti noti
Clinician review remains mandatory. Notat drafts; clinicians verify and sign.
The method reduces unsupported statements by architecture, but no clinical AI should claim zero hallucinations.
Small internal evaluations are useful for engineering direction, not a substitute for external clinical validation.
Specialty, language, audio quality, speaker overlap, and local coding rules can change performance.
Leggi le prove, poi ispeziona i tuoi fatti.
Il metodo di valutazione è semplice perché il prodotto è progettato per essere ispezionabile: prima i fatti, poi le note, sempre la revisione del clinico.
Prova Notat gratis