This week: an AI safety net that reads 25,000 primary care notes a week for missed referrals, a dental surgeon ships a deterministic validation harness, a GP rebuilds his own surgery's front door, and hundreds of practices lose their websites when a supplier folds.
Top signal
A safety net for the referrals nobody chased Wayne Han Lee and colleagues, writing in npj Digital Medicine on 30 October, built MR-AI, which reads primary care consultation notes and flags referral intent that never turned into an order. In a quasi-experimental study across seven clinics in Singapore it scanned about 25,000 notes a week and raised weekly missed referral detection by 4.7 percentage points (p < 0.001), an estimated sevenfold increase. Of 428 cases flagged, 225 were confirmed by a clinician, alongside 31 that patients reported, for 256 detections at 99.9 per cent specificity and 48.8 per cent sensitivity. In a blinded one-week comparison against seven physician reviewers, positive predictive value was 61.8 per cent against a human confirmation rate of 51.8 per cent across 83 flagged cases, with about 41 fewer projected staff-hours. Read the sensitivity first: the net catches roughly half of what is there, so it is a second pair of eyes, not a replacement for the first. The authors say lasting improvement needs preventive workflow changes and a cost-effectiveness study, not just a better model. npj Digital Medicine
Shipped this week
- Clinical AI Deterministic Harness: Guilherme Trentini, DDS, a dental and facial surgeon in Brazil with 18 years in practice who now works as a clinical product lead in health informatics, published an MIT-licensed Python library on 3 November that checks the identifiers a language model is most likely to invent: SNOMED CT concept IDs against the Verhoeff check digit and partition rules, US NPI and DEA numbers, FDA UDI strings and Brazilian CNS health card numbers, and a HIPAA Safe Harbor scrubber for free text leaving the building. The README is candid: not a Python programmer, he directed Google Antigravity and Gemini agents through it and verified test cases until the checks were deterministic. Zero dependencies is the right call for a guard layer. Caveats: three days old, no stars, no deployment, and a pattern-matching scrubber is a starting point, not de-identification you can rely on without your own test corpus. GitHub
- MARA at Burnett Edgar Medical Centre: Dr Muhammad Farhan Amin, MBBS, MRCGP, a GP at Burnett Edgar Medical Centre on Walney Island in Barrow-in-Furness, has rebuilt his own practice's front door around an agentic intake engine. He announced the practice's new website on 1 November, and MARA, the information-gathering engine behind it, is due to go live on 1 December, powering the AI receptionist, the online chat and the front desk; the site already shows it as coming soon. "Most AI in healthcare gets bolted onto the side of the system," he wrote. "I wanted to deliver primary care that's proactive and intelligent, built in from the start." He has been building towards this for five years with mentors and backers, so read it as the builder's own account: no published evaluation, the evidence so far is testing with patients whose feedback he calls excellent, and one practice going live is a pilot whatever the architecture promises. LinkedIn
- CatchUpRad on Android: Sven van Egmond, MD, PhD, a dermatology researcher at Erasmus MC in Rotterdam, brought CatchUpRad to Google Play about a month after he first shared the iOS and web versions. The app builds a daily updated reading list of new radiology papers, with summaries and links to the originals, filtered by the topics, study designs and journals you choose. He announced the Android build on r/Radiology: "CatchUpRad is now available on Android. Thank you to everyone who helped test it!" The Play listing sits under his own developer account and shows in-app purchases, so read his "free, has no ads and doesn't require an account" as a description of the core app rather than of everything; the store also shows 10 or more installs, which is the honest size of a one-person literature app in its first weeks on Android. It is a reading aid, not clinical decision software. Google Play
Build safely
- A supplier folds and 500 practices lose their front door: Silicon Practice, which ran websites, online booking and consultations for around 500 practices in England, just under a tenth of the total, with services in Wales affected too, went into administration and its services went offline, the BBC reported; the BMJ dates the outage to 28 October. Patients were pushed back to the telephone; the NHS App kept working. Two numbers make the point for anyone who builds for a practice: more than 40 per cent of appointments in England are now booked online, and the supplier advertised its FootFall tool as cutting patient phone calls to practices by 30 per cent, "proven in 1,000+ GP practices across the UK". What failed was load-bearing, not decorative. The BMJ's report adds the governance question, with GP leaders asking why there was no warning and no national contingency plan. Your version of this: a documented exit path, a tested export, and a way to reach patients when your domain stops resolving. BBC News
- Deprioritise a scan only when two models agree: Seungjun Lee and colleagues, writing in npj Digital Medicine on 5 November, tested whether a worklist can safely push non-contrast brain CTs down the queue. In a retrospective multicentre validation of 2,001 emergency and trauma scans, standalone commercial detectors left 31 of 324 exams needing attention deprioritised, 16 false negatives for the target disease and 15 outside their indications. Their fix is architectural: pair a generalist foundation model, BrainCT-CLIP, with the specialist detectors and deprioritise only when no model returns a positive. That recovered every remarkable scan at a prespecified threshold, 100 per cent sensitivity and negative predictive value, while deprioritising 18 per cent (368 of 2,001). Caveats: retrospective, by a team from Korean hospitals, scans stayed in the reporting workflow under radiologist oversight, and the two vendors gave the team access to their detectors. npj Digital Medicine
Tools & guides
- An LLM judge is not an expert panel: Pakull and colleagues, writing in npj Digital Medicine on 4 November, audited six open-weight models used as judges of clinical text against expert reviewers, on four German datasets covering report and nursing summaries, discharge summaries and patient letters, across three evaluation paradigms and four prompt configurations. The cost is what matters: substituting the model judge for the expert panel shifted 57.2 per cent of pointwise categories, swapped 41.7 per cent of pairwise winners, missed 10.2 per cent of clinical errors, and inverted clinical priorities by overrating the criteria succinct and accurate. Every judge showed position and identity bias, and no configuration was reliable across paradigms. If your eval harness leans on a model judge, this is the paper to cite when the numbers move, and the argument for keeping a sampled human review in the loop. npj Digital Medicine
Events
- 12 to 13 Nov (Basel): the ESC Digital & AI Summit runs in Basel on 12 and 13 November, and its first hackathon has already picked its teams: 25 participants in five multidisciplinary teams, who meet for a pre-summit kick-off on 11 November and present to a jury at the summit on the 13th, with an ESC Hackathon Award for the winner. Applications closed in September, so this is one to follow rather than enter. The published topics are worth reading anyway as a statement of what cardiology thinks is worth automating: real-time prediction of cardiac surgery duration, and a Virtual Heart Team that assembles referrals, flags missing diagnostics and prepares the meeting. ESC
New in the directory
- The directory now counts 99 clinician-builders with verified, evidence-graded ships, the same as last week: nobody joined or left. Browse it, forward this to a colleague who ships, and reply if you know someone who belongs in it. cliniciansthatcode.com
From us
- The BAA table, four rows updated: our guide to which platforms sign a BAA changed in four of its 20 rows on 4 November, after the drift watch flagged the vendors' pages. Base44's docs now carry a HIPAA section stating that Base44 is not HIPAA compliant and cannot sign a BAA. Our note had said no Base44 page mentioned HIPAA, which was true of the copy archived on 10 October but is no longer, so the note now quotes Base44 directly; the verdict was already no and stays no. Supabase's HIPAA page dropped the compute condition on point-in-time recovery, while its Backups page still says PITR needs at least a Small compute add-on, so we now watch both; Anthropic's pages now name the new Ultra plan among the consumer plans a BAA never covers. All 108 dated entries in the changelog now carry a one-line summary, and the full notes have their own page, so you can see what changed and when, platform by platform. Clinicians That Code