Understanding Price Transparency Requirements
The Centers for Medicare & Medicaid Services (CMS) implemented groundbreaking price transparency regulations to fundamentally transform how healthcare pricing information becomes accessible to consumers and researchers. These transparency mandates represent one of the most significant developments in American healthcare policy, creating unprecedented public access to negotiated healthcare service rates.
The transparency framework operates through two complementary regulations. The Hospital Price Transparency rule, implemented in 2021, requires hospitals to publish machine-readable files containing prices for shoppable services. The Transparency in Coverage (TIC) rule, effective in 2022, mandates commercial insurers to publicly disclose comprehensive negotiated pricing information for in-network services and historical payment amounts for out-of-network care through monthly-updated machine-readable files.
These TIC files must include negotiated rates for all healthcare providers across insurer networks, creating an expansive dataset that encompasses physicians, hospitals, and ancillary service providers. The scope and scale of this publicly available pricing information creates powerful opportunities for market analysis, policy research, and consumer decision-making.
Study Objectives and Methodology
This groundbreaking analysis represents the first comprehensive evaluation of TIC data completeness, addressing two fundamental questions that determine the practical utility of transparency requirements. First, researchers assessed what proportion of providers in each payer’s nationwide network actually appear in their TIC files. Second, the study examined what percentage of commonly utilized billing codes contain pricing information within the transparency data.
Data Collection Approach
Researchers obtained TIC files from the broad national preferred provider organization networks of Aetna, Cigna, and UnitedHealthcare during the second quarter of 2025. The study excluded Blue Cross Blue Shield plans due to their fragmented structure comprising dozens of individual plans with varying reporting methodologies that prevent collective analysis.
Network Completeness Assessment
The TIC data presents provider information at the National Provider Identifier (NPI) level. Researchers matched listed NPIs against multiple databases to calculate specialty distributions and generate national provider estimates. Denominators came from insurer websites reporting their broad network sizes across categories including physicians, non-physicians, hospitals, and facilities.
Billing Rate Completeness Methodology
Evaluating billing rate completeness required a sophisticated analytical framework. Researchers identified the 100 most frequently billed Healthcare Common Procedure Coding System codes for three common specialties—cardiology, family practice, and orthopedic surgery—plus the 100 most common codes for hospital outpatient settings, using Medicare claims data and All-Payer Claims Databases from Colorado and New Hampshire. For hospital inpatient services, the analysis included all 771 diagnosis-related group codes.
The study limited physician specialty analysis to groups with at least 10 physicians to ensure meaningful practice patterns. Researchers excluded rates falling outside reasonable ranges (50% to 800% of Medicare rates) or containing missing or inaccurate provider data.
Provider Network Completeness Findings
The analysis revealed substantial variation in how completely insurers report their provider networks within TIC files. Aetna demonstrated relatively consistent alignment between TIC data and marketing materials, with similar provider counts across both sources. Cigna’s TIC files contained significantly more providers than their marketing materials indicated, suggesting either conservative marketing estimates or comprehensive data reporting. Conversely, UnitedHealthcare’s TIC files included substantially fewer providers than their website advertised, raising concerns about data completeness or accuracy in marketing claims.
These discrepancies may stem from measurement challenges affecting both numerators and denominators. Provider counts derived from organization NPIs may differ from actual physician numbers. Insurer-reported network sizes may not reflect real-time network composition. Additionally, definitional variations regarding hospitals, facilities, and specialist categories could contribute to observed differences, though such variations cannot fully explain the magnitude of discrepancies identified.
Billing Code Data Completeness Results
Physician Specialty Data Quality
Physician specialty data demonstrated substantially better completeness compared to hospital settings across all three insurers. UnitedHealthcare achieved the highest physician data completeness, with cardiology, family practice, and orthopedic surgery groups showing at least 98% completeness at the 25th percentile—meaning three-quarters of physician groups had negotiated rates for nearly all common billing codes.
Cigna demonstrated strong physician specialty completeness with at least 89% at the 25th percentile and exceeding 99% at the median for all three specialties evaluated. Aetna showed moderate physician completeness, with family practice groups at the 25th percentile having rates for at least 54% of common codes and median groups achieving 77% completeness.
Hospital Service Data Variations
Hospital data completeness showed dramatic variation by insurer and service setting. Aetna’s inpatient data exhibited bimodal distribution—hospitals either had very complete data or very sparse information, with the median hospital reporting rates for all common inpatient codes while the 25th percentile hospital had only 11% completeness.
Cigna achieved high completeness for hospital inpatient data with at least 89% at the 25th percentile, but hospital outpatient data remained severely incomplete, with median facilities reporting only 4% of common codes. UnitedHealthcare presented the inverse pattern, with strong hospital outpatient completeness but extremely sparse inpatient data—median hospitals reporting negotiated rates for merely 2% of inpatient codes.
Insurer-Specific Performance Analysis
Each national payer demonstrated distinct strengths and weaknesses in their TIC data quality and completeness patterns. Aetna provided balanced moderate-to-good completeness across physician specialties, hospital outpatient services, and hospital inpatient care, though significant room for improvement remains across all categories.
Cigna excelled in physician specialty data and hospital inpatient information but showed critical gaps in hospital outpatient pricing data. UnitedHealthcare achieved near-complete physician group data and strong hospital outpatient information but failed to provide adequate hospital inpatient pricing information necessary for meaningful analysis.
Implications for Healthcare Policy
The significant variation in data completeness across insurers undermines the fundamental objectives of price transparency regulations. Incomplete data prevents accurate estimation of average prices, market sizes, and competitive dynamics—all critical parameters for effective healthcare policy development and consumer decision-making.
The absence of complete hospital inpatient data particularly hampers efforts to understand pricing for high-cost, high-impact healthcare services. Since inpatient care represents substantial healthcare expenditures and consumer financial exposure, gaps in inpatient pricing transparency limit the practical utility of transparency requirements.
Recommendations for Improvement
Enhanced Auditing Requirements
CMS should implement standardized auditing protocols to systematically evaluate TIC data completeness. Regular periodic audits should assess both coverage and accuracy, potentially involving provider verification of published negotiated rates. If internal CMS capacity proves insufficient, outsourcing to specialized vendors through contracts or compliance-sharing arrangements could ensure effective oversight.
Enforcement Actions
To date, no insurers have faced public penalties for transparency compliance failures despite evident data gaps. CMS must develop and execute meaningful enforcement mechanisms, including financial penalties proportionate to the scope and duration of non-compliance, to incentivize complete and accurate reporting.
Technical Infrastructure Improvements
Updated schema designs utilizing relational database structures could substantially improve data completeness and quality while simplifying both data creation for insurers and evaluation by regulators and researchers. Standardized data architecture would reduce technical barriers to compliance and facilitate more efficient quality assessment.
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