Unleashing the Potential of Healthcare Data: Combining Machine-Readable Files with Aggregated and Deidentified 837/835 Data

The healthcare data landscape is about to transform, opening the door to unprecedented insights and opportunities. With the release of machine-readable files (MRFs) under the CMS Transparency in Coverage Rule and the continued aggregation, normalization, and deidentification of 837 (claims) and 835 (payment remittance) data, organizations across the healthcare spectrum are poised to create new business models, solve longstanding challenges, and deliver more personalized experiences.

When these two datasets are combined, the result is a comprehensive, high-resolution view of healthcare economics and clinical outcomes—unlocking applications for employers, life sciences companies, pharma, payers, providers, and even direct-to-consumer innovators.

A New Era of Healthcare Insights

By merging the granular pricing and network data in MRFs with detailed claims and payment data from 837/835s, organizations can answer critical questions with precision:

• Machine-readable files provide a view of negotiated rates, in-network/out-of-network pricing, and coverage tiers.

• 837/835 data adds claims-level detail, including service utilization, diagnosis codes, procedural outcomes, and payment remittances.

The integration of these datasets offers a unique opportunity to:

• Normalize disparate data sources.

• Tokenize and deidentify data for privacy compliance.

• Create insights that span cost, quality, and outcomes across the healthcare continuum.

Use Cases and Business Models

1. Employer Benefits Optimization

Employers can use this dataset to:

• Benchmark provider pricing: Identify high-value care by comparing costs and outcomes across regions, networks, and providers.

• Optimize plan designs: Tailor benefits to steer employees toward cost-effective, high-quality providers.

• Proactive cost management: Predict high-cost conditions and implement wellness or care navigation programs to improve outcomes and reduce expenses.

Business Model: Data-as-a-service platforms offering real-time insights for benefits managers and human resource teams.

2. Life Sciences and Pharma