Two landmark federal rules have fundamentally changed the availability of healthcare pricing information in the United States. The Hospital Price Transparency Rule, effective January 2021, requires hospitals to post machine-readable files disclosing their negotiated rates with all commercial payers for all items and services, alongside standard charge information. The Transparency in Coverage Rule, effective for plan years beginning in 2022, requires group health plans and insurers to publish similar files disclosing their negotiated rates with in-network providers and out-of-network allowed amounts. Together, these rules have made publicly available a volume of pricing information that was previously accessible only to sophisticated market actors with proprietary claims data — creating a new foundation for competitive analysis, benchmarking, and market research across the healthcare industry.
At their core, the machine-readable files required by these rules contain payer-specific negotiated rates: the amounts that a hospital or other provider has agreed to accept from a given insurance plan for a specified service, identified by billing code (DRG for inpatient services, CPT or HCPCS for outpatient). Files also contain standard charges — the hospital’s chargemaster list price and minimum and maximum negotiated rates across all payers — which provide context for understanding where any given negotiated rate sits relative to the hospital’s internal pricing structure. In practice, a large academic medical center may have negotiated rates with dozens of distinct payer-plan combinations, producing an MRF that contains millions of individual rate entries. Understanding the structure of these files — including the difference between fee schedule rates, case rate rates, and percent-of-billed-charges arrangements — is prerequisite to interpreting any analysis derived from them.
For analysts beginning to work with MRF data, the most important practical steps are: first, obtaining a reliable, pre-processed version of the data rather than attempting to work directly with raw files (which vary enormously in format and quality); second, understanding which billing codes are relevant to the questions being asked and how those codes map to clinical service categories; and third, applying appropriate filters to exclude rates that are likely to be ghost rates or format artifacts before constructing any distributional analysis. Simple Healthcare’s platform handles all three of these steps, providing a clean, queryable dataset with intuitive search and filtering interfaces designed for analysts who need to produce reliable market intelligence without deep data engineering expertise.