Solving the MRF Data Challenge in Transparency in Coverage: Key Insights

“In every other industry, you get to see prices up front. Healthcare should be no different. It’s time for transparency.”

Marty Makary, M.D., Surgeon, a Public Policy Researcher at Johns Hopkins University, and Author of “The Price We Pay” and “Unaccountable” (He writes for The Washington Post and The Wall Street Journal)

For a long time, Americans have grappled with the challenge of discovering their healthcare costs only after receiving the services followed by an unexpected bill. There was always a need for group health plans and health insurance issuers/carriers to disclose the price and member responsibility information to participants, beneficiaries, and enrollees.
Recognizing this longstanding issue and need, the Department of Health and Human Services (HHS), the Department of Labor, and the Department of the Treasury in the United States, working closely with the Centers for Medicare & Medicaid Services (CMS) implemented the Transparency in Coverage (TIC) rule, also known as Contract Price Transparency, improving price and quality transparency in American healthcare. This initiative aims to ‘Put Patients First’ by giving them real-time, personalized access to member responsibility information through an internet-based self-service tool.
In this article, we will delve deeper into the TIC rule and how it ensures all information about a health coverage plan—the premiums, deductibles, co-payments, out-of-pocket expenses, and details about the network of healthcare providers that are covered—is to be clearly and comprehensively communicated to the consumers. We will also explore the associated challenge of interpreting the TIC data which is shared as large format Machine Readable Files (MRFs) and how technology can help leverage the TIC data more efficiently for insights to meet the TIC objectives and transform healthcare in the long run.

The CMS Mandate for Transparent Cost Estimation and Disclosures

“Transparency can play a significant role in reducing healthcare disparities by giving patients more information to make decisions that align with their individual needs.” 

– American Medical Association

The Transparency in coverage rule primarily covers two kinds of mandatory disclosures – Disclosures to the Public and Disclosures to the Enrollees or Plan Participants –that need to be implemented in phases over three years, beginning with the public posting of pricing data and progressing to a more personalized transparency experience for plan members.
Disclosures to the Public through Machine Readable Files (MRFs)
Since January 1, 2022, health plans and issuers (insurance carriers) must disclose the following information –
  • In-network provider-negotiated rates based on the contract for all covered items and services.
  • Historical data that shows both billed and allowed amounts for all covered items and services set by out-of-network providers, including prescription drugs.
  • Negotiated rates and historical net prices for prescription drugs set by in-network providers. Currently, HHS has delayed enforcing this piece of requirement indefinitely, and it is pending additional rulemaking.
All these health plan details are to be posted by payers on the web as Machine Readable Files every month. An MRF contains all the mandatory information in a digital format that can be imported or read by a computer system for further processing without human intervention.

“The MRFs are wonderful because, for the first time, the industry has a normalized source of common and comprehensive data that software tools such as decision support apps can consume to powerfully help consumers make choices.” 

– Jay Sultan (Healthcare Innovation Executive, Operating Partner-United Generations Capital)

Disclosures to the Enrollees or Plan Participants through a Self-service Cost Estimator Tool

Since January 1, 2023, health plans have been required to provide a cost estimator online tool to their plan participants, beneficiaries, enrollees, or their authorized representatives. The objective is to disclose the cost-share estimates for 500 shoppable services covering areas such as imaging services (e.g., CPT Code – 78306), laboratory tests (e.g., CPT Code – 87205), elective surgical procedures (e.g., MS-DRG Code – 470), and preventive screenings such as mammograms and colonoscopies (e.g., HCPCS Code – G0120)

For plan years beginning on or after Jan. 1, 2024, the online tool must provide cost-share estimates for all covered services to help consumers understand healthcare costs and their estimated cost-sharing liability based on their benefits and deductible and/or out-of-pocket accumulations, as well as compare costs across providers before obtaining care. This self-service online tool should be able to provide below listed information/features as applicable: 

  • Both in and out-of-network estimated costs (out-of-network costs may be dollars or percent).
  • Allow members or personal representatives to search based on the billing code or description of the billing code.
  • Advise members of their status towards the deductible, out-of-pocket maximums, and their accumulations to date.
  • Provide a cost estimate in paper format at the member’s request.

The Challenge of Large Volumes of MRF Data: One of the Biggest Roadblocks in the TIC Endeavor

The MRF data that payers share publicly every month is packed with valuable business intelligence. However, the sheer volume and complexity of this MRF data makes managing and analyzing it to extract valuable insights a major challenge. There is a lot of data involved – hundreds of terabytes of data exposing prices of all included products and services within the health plan provider’s network, along with approved rates and invoiced costs associated with out-of-network providers. Unless we know how to extract relevant data from the mass of information, its effectiveness in creating “transparency” becomes limited.
CMS mandates that the information be made available in a non-proprietary open format such as JSON, YAML, or XML. This requirement underscores the importance of addressing the challenge of handling the complexity and variety of the data structures in machine-readable files. Normalizing the data from different formats and bringing it to a comparable format is essential to unlock the full potential of the Transparency in Coverage data.
To put this into perspective, when we analyzed the monthly Transparency in Coverage data from one national and two state payers, a whopping 12 terabytes of compressed JSON files had to be extracted from the payers’ websites.
The high volume of data, the variety of sources, the complexity of pricing structures, the need for real-time updates, data privacy concerns, and other challenges collectively turn Transparency in Coverage into a significantly big data challenge. Integrating big data techniques into healthcare transparency efforts can yield valuable insights for improving patient care, optimizing healthcare processes, and driving informed decision-making.

A Step-wise Approach to Addressing the Big Data Challenge in MRF Analysis

Healthcare transparency is here to stay and focusing on MRF analysis for useful insights is critical. Solving the big data challenge presented by MRF data requires a holistic approach that combines advanced technological solutions, effective data management strategies, and careful attention to regulatory considerations. The following structured approach provides a roadmap for effectively leveraging extensive MRF data while ensuring data accuracy and compliance with healthcare regulations –
A Structured Approach to Solving the Big Data Challenge in MRFs and Bringing Transparency in Coverage
Data Integration and Centralization
  • A centralized repository that integrates data from diverse sources.
  • Data standardization to ensure consistent coding systems, terminologies, and formats across different data sources.
Real-time Data Updates
  • Automated mechanisms to update information in real-time to capture the changes that occur due to negotiations, guidelines, or other factors.
  • Data validation processes to ensure data accuracy and prevent discrepancies.
Data Privacy and Security
  • Robust data encryption, access controls, and audit trails to protect sensitive patient and financial information.
Data Quality Management
  • Data quality tools and processes to identify and rectify errors, inconsistencies, and inaccuracies in pricing data.
  • Data profiling and cleansing techniques to improve the reliability of the presented pricing information.
Data Visualization and Interpretation
  • User-friendly data visualizations, such as charts, graphs, and interactive dashboards.
  • Clear explanations and annotations to enhance the interpretation of complex structures.
AI-driven Recommendations and Forecasting
  • Recommendations on plans, pricing, and providers using the extensive open data ingested into the data lake.
  • An AI-based Q&A platform allowing users to ask questions about plans, providers, services, negotiation rates, etc.
  • Forecasting models, predicting the availability of a specific drug for a specific season.
Leveraging AI for Healthcare Data Analytics
An AI-powered analytics platform can be instrumental in reading large MRFs and gaining actionable insights from the TIC data. This can enable users to suggest the best plan negotiation rates, recommend providers for specific services, provide cost transparency, and offer more such services that promote health plan transparency and accountability.
A Case in Point
A healthcare tech organization faced challenges in enabling its clients to comprehend the Transparency in Coverage data generated by various health plans in the form of large Machine-readable Files (MRFs).
We helped the HealthTech company build an AI-powered analytics solution that automated their data-related processes and enabled them to gain deeper insights from MRFs far more efficiently.

Upholding the Promise of Healthcare Pricing Transparency for a Better Future

“Pricing transparency is expected to lower costs by helping consumers make informed choices. To succeed, we must engage consumers and make it worth their effort. We can get their attention by focusing efforts on areas where out-of-pocket expenses are an everyday pain point.” 
– Pua Cooper MSN, RN, FHIMSS, Chief Clinical Information Officer, Boca Raton Regional Hospital; South Florida Chapter of HIMSS President
In the quest for healthcare transparency, the Transparency in Coverage rule emerges as a pivotal milestone. Healthcare pioneers emphasize that pricing transparency benefits consumers and lowers costs. As we progress toward realizing the TIC objectives, harnessing intelligent technologies to manage the complex TIC data in the vast Machine-Readable Files becomes imperative. The predictive and other extraordinary capabilities of technologies like AI amplify the value of TIC data, streamlining data management and fostering actionable insights in healthcare. Above all, this pricing transparency becomes more than a mere regulatory requirement but a means to create a better future for all stakeholders of healthcare.

Rakesh Talreja
Rakesh Talreja is spearheading the Healthcare Business Unit at KANINI. He is a veteran Healthcare IT market leader, bringing 25 years of experience working with some of the best IT organizations across India and the US. Rakesh has a successful track record of delivering high customer value and creating an environment of growth.
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