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VeriMed – open-source medical license verification

Hacker News

VeriMed – open-source medical license verification

Hi HN, I built VeriMed while working on an AI-powered telemedicine platform. I quickly realised that verifying whether a healthcare provider is legitimately licensed is surprisingly hard. Every country has a different registry, a different API format (REST, SOAP, FHIR), and different access requirements. I couldn't find a unified solution, so I built one. VeriMed: - Connects to 5 national medical registries: - USA (NPI) - France (RPPS) - UAE (DHA) - Netherlands (BIG) - Israel (MOH) - Falls back to AI document verification when registries aren't available - Uses fuzzy name matching to handle variations (e.g., "Greg" vs "Gregory") - Production-ready with Docker, Kubernetes manifests, and health checks - MIT licensed, free to self-host - Enterprise extension available: - SSO (SAML 2.0 & OIDC) - RBAC (Admin, Reviewer, Viewer) - Audit Dashboard & CSV Export - Bulk CSV Import Wrapper Tech stack: NestJS, TypeORM, OpenAI (optional), Fuse.js GitHub: https://github.com/daretechie/verimed I'd love feedback on the architecture or suggestions for additional country adapters to prioritise.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: dock, openai, open · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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