The Transparency in Coverage (TiC) Rule changed everything about accessing healthcare price transparency data. If you're a healthcare data analyst or consultant working with payer price transparency data, understanding this regulation isn't optional—it's fundamental to your work. This guide breaks down what the TiC Rule requires, why it matters for your analysis, and how to actually use machine readable files (MRF) effectively.
What the TiC Rule Actually Requires
The Transparency in Coverage Rule is a federal regulation from CMS that requires group health plans and insurers to publicly disclose pricing information. Implemented starting July 1, 2022, it mandates three types of disclosures that create the payer MRF data you're now working with.
Who It Applies To
The regulation applies to:
- Group health plans (both self-insured and fully insured)
- Health insurance issuers in the commercial market
- TPAs that handle compliance for self-insured employers
It doesn't cover Medicare, Medicaid, or TRICARE, but it does cover the vast majority of Americans with private health insurance.
The Three Key Requirements
Health plans must publish three types of machine readable files monthly on publicly accessible websites:
- In-network negotiated rates - What plans actually pay providers
- Out-of-network allowed amounts - Historical payments for out-of-network care
- Prescription drug pricing - Negotiated rates with pharmacies and PBMs
The files must use JSON format following CMS technical schemas.
The Scale of Data
The data volume is substantial. Large health plans publish files containing hundreds of gigabytes covering thousands of billing codes across extensive provider networks. This is health insurance pricing data at scale, which creates both opportunities and challenges for analysis.
Plans also must provide members with online price comparison tools showing estimated out-of-pocket costs based on their specific plan design. These tools initially covered 500 shoppable services starting January 1, 2023, and expanded to all covered services by January 1, 2024.
Why This Matters for Your Work
For Healthcare Data Analysts
The TiC Rule created access to healthcare cost data that was previously locked away in proprietary systems. You can now:
- Build competitive benchmarking dashboards comparing negotiated rates across payers
- Identify pricing outliers for contract negotiations
- Analyze geographic variation in actual payment rates rather than theoretical fee schedules
For Healthcare Consultants
This fundamentally changes your advisory capabilities. You can:
- Provide clients with market-specific rate benchmarks based on real payer price transparency data instead of claims, estimates, or surveys
- Conduct network adequacy analysis comparing client rates to market alternatives using actual health insurance pricing data from multiple payers
- Elevate from benefits administration to strategic cost advisory
The challenge is that accessing and using this medical pricing transparency data isn't straightforward. Raw machine readable files are difficult to work with, which is exactly why we built Deductible Data.
The Real Challenges with TiC Data
Let's be honest about what you're dealing with:
Technical Challenges
- Massive file sizes - Individual payer MRF files can exceed 100GB each
- Complex JSON structures - Deeply nested formats requiring specialized parsing
- Inconsistent formatting - Files vary significantly across plans even when following the same CMS schema
- Data quality issues - Missing data elements, encoding errors, and inconsistent coding practices
Cross-Plan Comparison Difficulties
Publishers interpret the schema differently. One plan might structure provider information one way while another takes a completely different approach to representing the same data. This fragmentation makes aggregating payer MRF data across multiple sources time-consuming and error-prone.
Healthcare Expertise Required
Negotiated rates alone don't tell you much—you need to understand:
- Billing code hierarchies
- Bundling practices
- Distinction between professional and facility fees
This technical and domain complexity is what Deductible Data handles for you. We process raw TiC files into organized, searchable datasets with standardized formatting, consistent provider identification, and quality validation. Instead of spending months building data pipelines to process machine readable files, you can focus on analysis and insights.
How to Actually Use This Data
Start Focused
If you're doing healthcare cost benchmarking, start with specific use cases rather than trying to analyze everything. Define clear questions:
- Are you comparing negotiated rates for high-volume procedures?
- Analyzing geographic price variations?
- Benchmarking your organization's rates against market averages?
Starting focused makes the data manageable.
Combine Data Sources
Combine payer price transparency data with other sources for richer insights:
- Pair negotiated rates with quality metrics to identify high-value providers
- Overlay utilization patterns to understand where the actual spending is
- Match healthcare cost data with outcomes information to analyze value, not just price
Validate Everything
Not all published health insurance pricing data is accurate or complete:
- Implement checks for outliers and anomalies
- Validate against known benchmarks
- Flag unreliable data points
At Deductible Data, we run comprehensive quality checks on all payer MRF data we process, flagging anomalies and identifying gaps so you know which data is reliable for your analysis.
Track Changes Over Time
As monthly machine readable files accumulate, you can analyze how rates evolve:
- Are certain providers increasing rates faster than others?
- Are health plans negotiating better rates in specific service lines?
This longitudinal view of medical pricing transparency provides insights that single snapshots miss. As you accumulate monthly healthcare cost data over time, you can build longitudinal datasets to analyze how rates evolve—without the complexity of managing years of raw files yourself.
Best Practices for Healthcare Cost Benchmarking
Focus on Actionable Insights
Translate findings into concrete recommendations. If you're advising an employer client, don't just show them that their rates are 15% above market—show them which specific providers or service lines are driving that difference and what they can do about it.
Stay Current with Compliance Requirements
CMS continues to refine technical specifications and enforcement priorities. The February 2026 schema version 2.0 updates will change how machine readable files are structured. Understanding these changes helps you maintain reliable analysis pipelines.
Consider Total Cost of Ownership
Direct download from plan websites is free but requires:
- Significant technical resources to find files
- Parsing JSON structures
- Data cleaning and validation
- Monthly update maintenance
- Data engineers who understand both healthcare and complex data processing
This is exactly the calculation that led organizations to work with Deductible Data. We've invested in the specialized infrastructure for processing payer price transparency data. Our customers tell us that using our processed healthcare cost data versus trying to handle raw files themselves saves months of engineering time. You focus on pricing analysis and insights instead of data plumbing.
What You Should Do
If You're Already Working with This Data
Assess your current approach:
- Are you spending more time wrangling data than analyzing it?
- Are you confident in the quality of your payer MRF data?
- Can you easily compare rates across multiple payers, or is that comparison process manual and time-consuming?
If You're Just Getting Started
Understand that the learning curve is steep but the analytical value is significant. Organizations that build capabilities around healthcare cost data now have advantages in:
- Network optimization
- Contract negotiations
- Strategic advisory work
Build vs. Buy Decision
The practical question is whether building data infrastructure yourself makes sense versus working with a provider like Deductible Data.
We provide:
- Processing of machine readable files into analysis-ready datasets
- Consistent formatting and quality validation
- Standardized provider identification
- Schema updates and monthly refreshes handled automatically
- Exactly the health insurance pricing data you need (specific payers, markets, procedure codes)
- Delivery in business days instead of weeks
The Bottom Line
The Transparency in Coverage Rule created unprecedented access to healthcare cost data, but accessing and using payer price transparency data effectively requires significant technical investment. Raw machine readable files are difficult to work with. Building processing infrastructure takes time and specialized expertise.
We built Deductible Data specifically to solve this problem for healthcare data analysts and consultants. We transform complex healthcare pricing data into organized, searchable datasets ready for medical pricing transparency analysis. Whether you need payer MRF data for competitive benchmarking, market research, or client advisory work, we deliver clean healthcare cost data optimized for your analytical workflows.
The value of healthcare price transparency data is clear. The challenge is accessing and using it effectively. That's where we come in.
Ready to work with clean healthcare cost data? We transform machine readable files into analysis-ready datasets for healthcare cost benchmarking and market analysis. Get the payer price transparency data you need, delivered in days instead of weeks. Learn more about Custom Data Pull.
About Deductible Data: We specialize in transforming raw healthcare pricing data into organized, searchable datasets for analysts and consultants. Our processed payer MRF data includes quality validation, consistent formatting, and standardized provider identification to support reliable medical pricing transparency analysis. Simple pricing, fast delivery, no long-term contracts.