Introduction
The transformation of Transparency in Coverage (TiC) machine-readable files (MRFs) from their raw, multi-terabyte states into structured, analytic datasets represents one of the most significant data-engineering challenges in modern health policy. While these files were intended to “democratize access” to commercial pricing, the practical reality is a “transparency paradox”: the data is technically public but requires specialized “sophisticated data experts” to navigate technical and logistical hurdles. The following methodology—grounded in SAS-based big data processing—addresses these challenges by focusing on relational mapping, script hygiene, and the elimination of both “zombie code” (computational bloat) and “zombie rates” (clinically implausible data).
Background
The Transparency in Coverage (TiC) final rule aimed to foster competition by requiring insurers to disclose in-network negotiated rates. However, researchers have faced significant challenges including decentralized repositories, massive file sizes, and inconsistent data formatting. A single insurer’s monthly update can exceed 2 TB, making traditional “load-everything” approaches impossible.
To make these data actionable, researchers must adopt a methodology that prioritizes data integrity and usability while maintaining script efficiency. This requires a two-pronged attack: one on “zombie rates” (implausible provider-rate combinations like podiatrists for heart surgery) and another on “zombie code” (legacy script logic that consumes overhead without providing value).
Phase I: Relational Mapping and the Unique Key Solution
Standardization is a primary concern in literature, with researchers noting that variations in file structure make it difficult to link TiC data to other clinical datasets. The raw JSON files typically nest provider arrays within rate objects, a hierarchy that must be “flattened” into a tabular format.
Our methodology addresses this by creating a concatenated relational key, IDPLUSGRP, which links a specific FILE_INDEX to a _GROUP_ID. This ensures that rates are matched to the specific provider groups defined within the same sub-file. This prevents “ghost matching” – a known issue where rates are erroneously attributed to providers in unrelated plans or geographic regions.
Phase II: The “Anti-Zombie” Protocol for Data Ingestion
A common pitfall in healthcare big data is the accumulation of “dirty data,” including redundant records and irrelevant observations. To avoid “zombie code” performance bottlenecks, we implement Early-Stage Curation via SAS formats. Rather than ingesting the entire universe of data, we use PROC FORMAT to filter for active National Provider Identifiers (NPIs) immediately.
This Lookup-at-Ingestion technique reduces the data volume by over 90% before the computationally expensive “Merge” phase. This directly addresses the literature’s recommendation to reduce the size of TiC files by excluding non-essential data elements early in the pipeline.
Phase III: Managing “Rate Explosion” and Statistical Validity
One of the most thorny issues in TiC data is the “Rate Explosion” phenomenon. Because MRFs lack “volume information” (the actual number of times a service was billed), a single negotiated rate can be attributed to thousands of NPIs within a large health system.
To maintain statistical integrity, our methodology utilizes a density threshold filter. By counting the number of NPIs associated with each group (IDgroup_cnt), we exclude massive conglomerates that might skew median prices for local market analysis.
This filter ensures the dataset remains manageable and represents individual or small-group practice patterns. Research from the Peterson Center notes that such filtering is essential for meaningful price-variation analysis, as it helps separate contracted “theoretical rates” from demonstrably active providers.
Phase IV: Deduplication and Normalization
Data cleaning is a mandatory step that must account for “inconsistent naming conventions”. We employ PROC SORT with the NODUP and NODUPKEY options to prune identical rate entries that differ only by minor service code modifiers.
This step is critical because TiC files often contain multiple identical rates for the same CPT code due to overlapping plan-product definitions. By normalizing these units, we ensure the final analytic file provides a unique price for each billing code.
Phase V: Creating Policy-Ready Analytic Files
The ultimate goal of this methodology is to produce data that can inform purchasing and policy. By merging the cleaned TiC rates with a clinical “Master” file (such as the HHS-defined 70 shoppable services), we transition from raw data to actionable insight.
As I and other experts have noted, focusing on shoppable services allows for a more accurate estimation of cost savings and price variation in the commercial market.
Conclusion: Toward a More Usable Transparency
While the TiC mandate is a significant step forward, its potential depends on the ability to overcome major technical and logistical hurdles. The methodology described here—prioritizing unique-key relational mapping, early-stage filtering, and density-based data reduction—provides a roadmap for researchers to build high-quality, high-integrity analytic files. By treating script hygiene (zombie code) with the same rigor as data quality (zombie rates), we can move toward a future where price transparency is a powerful tool for improving healthcare affordability.