I’ve been exploring the NPPES dataset, the federal registry that assigns NPI numbers to every healthcare provider in the U.S. It currently has about 9 million records and grows by ~30k per month, but accessing it usually means downloading multi-gigabyte CSVs or using the CMS lookup that returns one provider at a time.
I built NPIScan to make the dataset browsable. You can search by name, NPI, specialty, or location and drill down from state → city → ZIP code. Each provider has a profile with credentials, practice locations, taxonomy codes, and digital health endpoints.
A few interesting patterns from the data:
- 2025 had ~631k new NPI registrations, the largest jump on record
- Behavior Technicians grew to ~526k providers and are now among the largest specialties
- California alone has ~1.1M providers (~12% of the country)
- Only ~0.5% of providers have registered digital health endpoints
Tech stack: Next.js, PostgreSQL, Meilisearch, Redis.
The main challenge was making 9M records feel fast to browse. I solved it with denormalized listing tables, Meilisearch full-text search, and Redis caching for aggregated queries. Most pages respond in <40ms after cache warmup.
Curious to hear feedback from anyone working with healthcare data.
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Show HN: NPIScan search 9M U.S. healthcare providers from the NPI registry · HackerTrans
I built NPIScan to make the dataset browsable. You can search by name, NPI, specialty, or location and drill down from state → city → ZIP code. Each provider has a profile with credentials, practice locations, taxonomy codes, and digital health endpoints.
A few interesting patterns from the data:
- 2025 had ~631k new NPI registrations, the largest jump on record
- Behavior Technicians grew to ~526k providers and are now among the largest specialties
- California alone has ~1.1M providers (~12% of the country)
- Only ~0.5% of providers have registered digital health endpoints
Tech stack: Next.js, PostgreSQL, Meilisearch, Redis. The main challenge was making 9M records feel fast to browse. I solved it with denormalized listing tables, Meilisearch full-text search, and Redis caching for aggregated queries. Most pages respond in <40ms after cache warmup.
Curious to hear feedback from anyone working with healthcare data.