More mothers die in Nigeria than anywhere else.
of all maternal deaths worldwide in 2023 were in Nigeria: about 75,000 women.
GuideLock answers health workers' questions from the official guidelines, with the section and page it came from.
Catch the danger sign before the patient goes home.
of all maternal deaths worldwide in 2023 were in Nigeria: about 75,000 women.
children born in Nigeria die before age five, and 39 in 1,000 in their first month (2024).
of the world's malaria deaths in 2024 were in Nigeria, including 38.6% of deaths in children under five.
people: Nigeria's density of medical doctors. First contact is a CHEW, a nurse or a midwife, with a guideline booklet.
Short points in the guideline's own words. Tap the citation and the page opens, with the sentence highlighted.
Of danger-sign test cases caught. The referral is quoted word for word, for the right patient: child, newborn or pregnancy.
Dosing rows a person has checked against the printed page. Missing age or weight? It asks instead of guessing.
Supervisors see danger signs that are rising and questions the guidelines don't cover.
This is the real engine, running now. Every step streams in as it happens: what was searched, which passages it found, and each of the ten automated source checks every answer must pass. These checks confirm quotes and sources; they are not a clinical review.
Runs on the official guidelines loaded into GuideLock (WHO and Federal Ministry of Health, Nigeria). It is a demonstration, not a substitute for clinical judgement or the printed guideline.
Reviewed word lists in five languages and the guideline's own sign lists catch the danger sign before any AI runs.
Every point names the guideline, version, year, section and page, and opens that page with the sentence highlighted.
A real lookup in the verified table. Outside every band, or missing an input, it refuses instead of guessing.
Pidgin, Hausa, Yoruba, Igbo and English. Medicine names and numbers are locked before translation and checked after.
A child with any general danger sign needs urgent referral to hospital…
A printable handover with the guideline's referral instruction and any pre-referral dose, taken from the database, never retyped.
Questions the guidelines can't answer are grouped and flagged, so programmes know what to add, or what to train on.
Synthetic questions at three fictional facilities, answered by the real engine and counted by code. The same numbers appear on the supervisor dashboard.
Each official PDF is split by section with its pages kept, so every passage knows where it lives. Dosing tables become rows that a clinician must approve against the printed page before they are ever used.
Before any AI runs, code checks the question against reviewed danger-sign words, the guideline's own sign lists and numeric thresholds. A match always shows the referral. The AI can add a warning; it can never remove one.
Search runs by keyword and by meaning together, then re-ranks, so "diarrhea" in American spelling and "diarrhoea" in the guideline find the same section. Nothing outside the loaded guidelines is ever used.
The AI writes short points, each tied to one passage with an exact quote. Code then runs ten source checks, including whether the passage is for the same patient group and condition. One failure and the health worker sees the guideline excerpt instead.
Questions are logged with phone numbers, emails, ID numbers and recognised names removed. Supervisors get the danger signs that are rising, the gaps, and a weekly report. Every number is counted by code and clicks through to its questions.
223 gold questions, 51 of them in local languages, and 30 attempts to trick it. If any safety gate fails, the build fails. These numbers load from the latest test report.
Measured on GuideLock's synthetic test guideline. These results show the pipeline and its safety gates work. Clinical accuracy on real guidelines must be measured with a clinician-written question set before use.
GuideLock is open source and ships with no guideline text. A state or programme runs it with its own documents.
Trained health workers at primary health care centres: CHEWs, nurses and midwives, and their supervisors. It is not for patients, it does not diagnose, and it does not replace referral protocols.
It can try, but it can't get it to the screen. Every point must quote the guideline exactly, every number must be in the cited passage, and any dose for a medicine with a dosing table is blocked. Doses come only from rows a clinician approved. If a check fails, the health worker sees the guideline excerpt instead.
Code finds it from reviewed word lists and the guideline's own sign lists. A red "Refer urgently" block shows the guideline's referral instruction word for word, for the right patient group, and can become a printable referral note.
GuideLock says so plainly and points to the referral protocol. The question shows up on the supervisor dashboard as a guideline gap.
Danger signs and doses scored 100% on the local-language test questions. The local-language word lists still need review by native-speaking clinicians before field use, and some AI translations were unreliable in testing, which is why danger detection doesn't depend on them.
Yes. On WhatsApp the referral comes first and the whole answer fits in 600 characters, with short citations. The workspace uses no heavy downloads and can be installed on a phone.
Not yet. The official guidelines are loaded, but before clinical use clinicians must verify the dosing rows, write a test set from the real documents, and review the local-language word lists. Until dosing rows are verified, GuideLock gives no medicine doses.