Lor-1/clinical reasoning.
A clinical foundation model calibrated to reason inside South African clinical conventions — SA drug names, tiered formulary, national treatment guidelines, and local referral pathways — served behind an OpenAI-compatible API.
Stronger where SA
clinical practice lives.
- Drug dosingSubstantial
- Differential managementStrong
- Clinical scenariosSolid
- Referral & triageModerate
Qualitative — see Benchmarks tab for the approach
- Surface
- Lorraine app + Platform API
- Region
- South Africa
- Inference
- vLLM
- Decoding
- Speculative
- API
- OpenAI-compatible
- Hosting
- Self-hosted, GPU
- Status
- Production
Overview
A model for one healthcare system.
General-purpose language models have become remarkably capable medical reasoners. They can work through differential diagnoses, explain pharmacology, and synthesise complex clinical scenarios. They share one failure mode though: geographic bias. Trained on a corpus dominated by US and European medical literature, they develop statistical priors that skew heavily toward US and European practice patterns. Ask one for first-line therapy for an uncomplicated UTI and it will most likely suggest trimethoprim-sulfamethoxazole or ciprofloxacin — at odds with SA primary-care guidance, which directs clinicians to nitrofurantoin. Ask about a cardiac arrest and it will talk about epinephrine rather than adrenaline.
These aren’t surface errors of tone. In clinical practice they are errors.
Five shapes of drift
/what-we-observed- 01
Formulary drift
Reaches for drugs that aren’t on the SA Essential Medicines List, or that aren’t stocked at the level of care the question actually concerns.
- 02
Protocol mismatch
Falls back to international clinical pathways — NICE, UpToDate, CDC — instead of the corresponding local protocol: IMCI, VTP, BANC+, the national TB algorithm.
- 03
Naming conventions
Surfaces US drug names and abbreviations — epinephrine, acetaminophen — in place of their SA equivalents, adrenaline and paracetamol.
- 04
Referral-pathway ignorance
No working concept of PHC → district → regional → tertiary, or of the specific criteria that govern movement between levels of the SA public system.
- 05
False confidence
Produces authoritative-sounding answers exactly where SA guidance is absent or ambiguous, instead of flagging the gap.
These failures compound. A clinician who receives a fluent, confident, internationally-biased answer has no easy way to detect the drift without already knowing the correct local answer — which defeats the purpose of the tool in the first place.
Lor-1 is calibrated at the weights, not the prompt. System prompts and retrieval wrappers can paper over some of this drift, but the model underneath still reaches for the wrong defaults the moment context is thin. Training against a South African clinical corpus shifts the underlying priors so that SA drug names, the tiered Essential Medicines List, the SA Standard Treatment Guidelines, and the PHC → district → regional → tertiary referral ladder are the model’s native reference points — not a layer applied after the fact.
Lor-1 powers Lorraine, our clinical assistant for South African clinicians, registrars, and nurses, launched in March 2026. The model is also available directly through the Lorraine Platform API for teams building SA-aware clinical software. In both surfaces, Lor-1 is a reference tool with a qualified user in the loop — not an autonomous decision system.
What Lor-1 learned
- 01SA drug names and dosing conventions — paracetamol (not acetaminophen), adrenaline (not epinephrine), amoxycillin, and SA-standard formulations.
- 02Tiered formulary awareness — which drugs sit at PHC, district, regional, and tertiary levels, with the correct escalation criteria.
- 03SA protocol adherence — VTP, IMCI, BANC+, and the national TB algorithms, with the right thresholds and referral triggers.
- 04Tool-grounded answering — calling structured clinical lookups to cite SA guideline values rather than guessing from parametric memory.
- 05Calibrated uncertainty — explicit abstention when SA guidance is insufficient, instead of drifting into international defaults.
Where frontier models drift
- Drug naming
- epinephrine → adrenaline
- Formulary
- no EML → tiered EML
- Protocols
- US / EU defaults → SA STG / IMCI / VTP
- Referrals
- “refer to specialist” → PHC → district → regional → tertiary
- Uncertainty
- confident fabrication → explicit abstention
Native reference frame
Lor-1 is not a medical device.
It augments clinician reasoning; it does not replace it. Every integration is responsible for clinician oversight and safety controls appropriate to its clinical context.