Analyzing data with AI agents

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The problem: price transparency in US healthcare

Healthcare pricing in the US is complex, inconsistent, and opaque, making it difficult for patients to estimate their medical costs. Key challenges include:

• Lack of standardized pricing across providers and insurers.
• Hidden costs and add-ons that make billing unpredictable.
• Inconsistent price disclosure, making cost estimation difficult for patients.

The solution: RevEnge - reverse engineering price

Castlight developed RevEnge, a system that predicts medical costs by analyzing provider data, insurance claims, and statistical pricing algorithms.

🔹 How it works:
1️⃣ Ingests data – collects insurance claims, provider details, specialties, and procedures.
2️⃣ Correlates data – matches historical claims with providers to identify real-world procedure costs.
3️⃣ Applies statistical pricing algorithms – uses machine learning models to estimate total costs, including pre-surgery, surgery, and post-surgery expenses.

🔹 Technology stack:
🛠 Python, BigQuery, MongoDB power the platform’s data processing and prediction models.

Impact

✅ Provides accurate cost estimates for patients.
✅ Helps navigate insurance complexities by factoring in deductibles, out-of-pocket limits, and coverage phases.
✅ Reduces reliance on manual price inquiries, making healthcare planning transparent and predictable.

Demo & key features

• A search tool allows users to find in-network doctors and see estimated costs.
• Takes into account different phases of insurance coverage to provide personalized cost estimates.

Takeaways

By leveraging data-driven insights and AI, Castlight is improving price transparency in US healthcare, helping patients make informed decisions about their medical expenses.