This is a submission for the GitHub Finish-Up-A-Thon Challenge
PuskesmasAI is an offline-first Progressive Web App (PWA) that brings AI-powered medical triage to community health workers (kader) in rural Indonesia — no internet connection required after the first setup.
Indonesia has 1 doctor per 5,000 people, far below the WHO's recommended 1:600 ratio. In remote 3T regions (Tertinggal, Terdepan, Terluar — Underdeveloped, Frontier, and Outermost), over 45% of community health posts lack adequate medical staff. The kader — non-medical volunteers who are often the only frontline health resource for millions — must make triage decisions without doctors nearby, without structured guidance, and frequently without internet.
PuskesmasAI solves this. A kader inputs patient symptoms via a simple form in Bahasa Indonesia, and the app returns a structured AI triage result: GREEN / YELLOW / ORANGE / RED — with recommended actions, possible conditions, red flags, and an auto-generated referral note. Patient records are stored locally in IndexedDB and sync to the Puskesmas dashboard when connectivity returns.
Built with Next.js 14 (PWA) + Tailwind CSS on the frontend, Python Flask on the backend, and Gemma 4 E4B (GGUF quantized via Ollama) as the on-device AI model (~2.5GB, zero cloud dependency). Zero patient data ever leaves the community — privacy by design.
This project means a lot to me personally. I'm based in Makassar, South Sulawesi — and the health disparity between urban and rural Indonesia is something I witness firsthand. PuskesmasAI is my attempt to put AI where it's needed most.
🔗 Repository: https://github.com/jefribulomakassar/gemma4_good_hackathon
⚠️
Local-first by design.PuskesmasAI runs entirely on-device — the Next.js frontend (port 3000) and Python Flask backend (port 5000) are started separately on a local machine, with Gemma 4 E4B served via Ollama. There is no hosted demo because the whole point is offline operation: no cloud, no data leaving the device.
To run locally:
cd backend && pip install -r requirements.txt
ollama run gemma4:e4b
python app.py # runs on :5000
cd frontend && npm install
npm run dev # runs on :3000
Demo scenario:
Phone in airplane mode → open PuskesmasAI → fill triage: 32-year-old female, symptoms: "fever for 3 days, red spots on skin, nausea" → tap Analyze → result: 🟠 ORANGE with action list and referral note → turn WiFi on → patient record auto-syncs to dashboard.
This project originally started as my submission for the Kaggle × Google DeepMind Gemma 4 Good Hackathon (Health & Sciences track, deadline: May 18, 2026). I had the full concept, the README, the architecture diagram, and the backend security layers ready — but I ran out of time before finishing the core data files and frontend components that would make the app actually work.
Before (what existed at the original deadline):
TriageResult.tsx
, OfflineBanner.tsx
, VoiceInput.tsx
componentsmedical_kb.json
— the AI had no medical knowledge basesymptom_map.json
— no symptom-to-condition mappingdrug_reference.json
— no drug dosage reference for kadersSymptomForm.tsx
— the main input form was missingdb.ts
/ sync.ts
— offline storage and sync not implementedAfter (what was added during the Finish-Up-A-Thon):
backend/data/medical_kb.json
— offline medical knowledge base covering the 10 most prevalent diseases in rural Indonesia (Dengue/DBD, Typhoid, Malaria, ARI/ISPA, Diarrhea, Hypertension, Tuberculosis, Malnutrition, Cholera, Pre-eclampsia) with symptoms, red flags, triage levels, and actions — all in Bahasa Indonesiabackend/data/symptom_map.json
— 32 symptom groups with Indonesian colloquial keywords, probability weights, and automatic triage escalation rulesbackend/data/drug_reference.json
— 10 essential Puskesmas drugs with pediatric dosing per kg body weight, contraindications, and kader-safe drug classification based on Indonesia's National Formulary (frontend/src/components/SymptomForm.tsx
— mobile-first patient intake form with symptom shortcut buttons, automatic pregnancy detection, temperature indicator, and form validationfrontend/src/lib/db.ts
— IndexedDB wrapper using Dexie.js for offline patient record storagefrontend/src/lib/sync.ts
— auto-sync module that uploads pending records to Turso cloud when connectivity returnsThe transformation: from a well-documented skeleton to a genuinely functional offline AI triage tool.
GitHub Copilot was central to finishing this project — especially for the data-heavy and boilerplate-heavy files that would have taken hours to write manually.
** symptom_map.json and drug_reference.json** were generated entirely using GitHub Copilot in the github.dev editor — no local setup needed. I simply pressed
.
on the repository page to open github.dev, then used Ctrl+I
to invoke inline prompts. I provided the full file path, the data structure requirements, and domain-specific context (Indonesian rural health context, Formularium Nasional constraints). The results were accurate, well-structured, and included details I hadn't explicitly specified — like colloquial Bahasa Indonesia symptom terms ("step"
for kejang/seizure, "ngos-ngosan"
for rapid breathing) and the appropriate kader-safe drug classification tiers.** db.ts and sync.ts** were scaffolded by Copilot with idiomatic TypeScript and Dexie.js patterns that matched the existing codebase — including the
synced
boolean field for tracking upload status and the navigator.onLine
check in the sync logic. What would have been an hour of boilerplate became a focused 10-minute review and refinement session.What I learned: Copilot works best when you give it full context — the exact file path in the repo, the purpose of the file, the related files it should be aware of, and the domain-specific constraints. A well-crafted prompt saved me hours on each file.
"The best AI is not the most complex one. It's the one that works for the people who need it most — even when the internet doesn't."