What 482 hospitals charge vs. what insurers pay, from their own files
US hospitals must publish a machine-readable file of their prices, including the rate they've negotiated with each insurer. The files are huge (one is 36GB), in four formats and often messy, so almost nobody reads them.I built a crawler that finds each hospital's official file and pulls out 29 common services. Across 482 hospitals: a moderate ER visit is listed at a median $1,285 while insurers pay $288; a metabolic panel is listed at $207 while insurers pay $11.46.To keep it honest: only per-service fee-schedule dollar rates count (no case rates or percentages, which cover a whole visit), each figure needs at least 3 insurers, and every value is checked against Medicare reference rates. Surgery is excluded because insurer surgery rates cover the whole operation while the list price is one line item. Each hospital page links its source file. Feedback on the methodology is very welcome.
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A tool that extracts and compares hospital list prices versus insurer payments from official files to reveal pricing disparities.
The pain
The gap
Build angle
Strengths
- Addresses a well-documented, significant transparency problem in healthcare pricing.
- Uses publicly mandated data sources, ensuring legitimacy and defensibility.
- Technical solution is feasible with existing tools and can be built by a small team or individual.
- Provides clear, comparable metrics that highlight large discrepancies between list prices and insurer payments.
Risks
- Unclear who the paying customers are and how to monetize the tool effectively.
- Data complexity and variability may limit scalability or accuracy over time.
- Excludes surgery and other complex billing, which may limit perceived completeness.
- Potential legal or compliance challenges in republishing or interpreting hospital data.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 90
- Solution gap
- 75
- Willingness to pay
- 65
- Buildability
- 80
The problem of opaque hospital pricing is clear and painful, the solution addresses a real gap, but willingness to pay is uncertain and the build is moderately complex but feasible.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a significant pain point with a clear value proposition, but monetization and customer acquisition strategies are not fully defined.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The concept leverages unique data aggregation to create transparency, but the market readiness and direct customer demand are moderate.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The project solves a real information asymmetry with a technically challenging but achievable approach, though the business model and scale remain unclear.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The startup tackles a big problem with a novel data-driven tool, but it needs clearer plans for growth and monetization to be YC-ready.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
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Postgres extension for BM25 relevance-ranked full-text search
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GlycemicGPT – Open-source AI-powered diabetes management
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AI
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