HEALTHCARE
AI your clinicians can
Practical controls for hospitals and clinics using AI to summarize, document, and decide, so doctors can trust what they sign.
What we hear
“Doctors are using AI to summarize records, and we have no idea how accurate it is.”
“We can’t tell which AI tools touch patient information.”
“Our vendors added AI features to software we already use.”
“If an AI summary misses something, who is accountable?”
"Did the AI really read all 50 pages?"
A physician asks an AI assistant (an LLM model) to summarize a 50-page discharge report. The summary reads well, but there is no way to tell whether it skipped page 37, where the medication change was. A3R sets up the process so the AI has to show its work, the way a resident presents a case to an attending:
- A reading receipt: the AI lists every page and section it read. Missing or unreadable pages are flagged, not silently skipped.
- Every line points to its source: each statement in the summary links to the page it came from. One click checks it. A statement with no source gets flagged.
- A must-not-miss checklist: medication changes, allergies, abnormal labs, pending results, and follow-up appointments are pulled out separately and checked against the report, so they can’t drop out of a nicely worded summary.
- An automatic second check: doses, dates, and lab values in the summary are compared with the original, and any mismatch is highlighted.
- Green, amber, and red flags tell the doctor where to look, instead of asking them to trust the whole summary.
- The doctor signs off, and the clinic keeps a record of what was checked. The clinic owner gets a monthly spot-check report showing how often summaries needed corrections, and testing reruns whenever the AI vendor changes the LLM model.
What you get
AI inventory
Every AI tool and vendor AI feature in use, and which ones touch patient information.
Source-linked summaries
Summaries where every line can be checked against the record in one click.
Must-not-miss checklists
Built with your clinicians, by specialty and document type.
Monthly accuracy reporting
Spot checks that show the clinic owner how often summaries needed correction.
Access and retention controls
Aligned to HIPAA, so patient information goes only where it should.
Retesting on vendor changes
When a vendor updates its LLM model, the checks run again before staff rely on it.
Start with the assessment.
In about four weeks, you’ll know what AI you’re running, where you stand on the maturity curve, and what to fix first.