Authored By: Shyam Manoj Karunakaran, Sr. Vice President - Health Plans, CitiusTech, Shobhit Saran, Asst. Vice President - Health Plans, CitiusTech and Venkatesh Bagal - Senior Healthcare Consultant, CitiusTech
In 2020, the US Federal Government finalized the “Transparency in Coverage” Rule, requiring health insurers, group health plans, as well as self-funded clients, to provide cost-sharing data to consumers via machine-readable files (MRF). Beginning July 1, 2022, required machine-readable files have provided pricing data for covered items and services based on in-network negotiated payment rates and historical out-of-network allowed amounts.
Over the last few months, we have observed how payers and providers have adapted to this mandate, working to optimize their techniques for generating MRFs.
At the beginning of the mandate compliance, an East Coast-based national payer had MRFs as large as 650 GB (compressed) that, when downloaded and uncompressed, would mushroom to more than 1 TB of data. The situation is better today as payers, have identified and resolved issues and generated MRFs using the right solutions, like provider references.
Over 85% of health plans fully adopted and became compliant within a few months of the mandate’s launch. These positive signs indicate that CMS’ mandate should reach full transparency for healthcare services.
Research organizations have already begun engaging with the MRF data, enriching, and using them for novel use cases and business objectives.
Our MRF data findings, observations, and recommendations
Working with MRF, and developing MRF solutions, have taught us much about the true value of the data. Here are a few observations and lessons learned.
MRFs reveal extreme price variations
Figure 1 depicts the extreme variation in negotiated prices for a single service (repair and revision procedures on the femur and knee joint) in a specific region of Pennsylvania. By using benchmarks and detailed market analysis, we can identify the root cause of this variance from the data being published by payers. However, sifting through publicly available MRF data and curating it to derive strategic insights is a mammoth effort.
Fig 1: Variances in negotiated prices for a single service in a single location.
Outlier analysis identifies data issues to resolve
Detailed data quality analysis, using data quality (DQ) rules, can identify quality issues with MRF data. DQ rules should be designed to highlight erroneous records and not propagate them through analytics. However, resolving issues by making changes to the data should be avoided. Here, we outline five data issues that should be carefully reviewed when attempting solutions.
Provider references:
Although CMS’ Price Transparency Guide introduces provider references as a technique to improve MRF readability, payers have slightly misinterpreted this. In one instance, for a national payer, we identified four providers (using references) paired with 11 distinct negotiated prices for the same procedure. One cannot decipher this node in the MRF and correctly pair the provider to the correct negotiated price. Based on our understanding of CMS specs, this is not allowed and may be considered a violation.
Repetitive rates:
When we analyze data patterns, it appears that payers are complying with the mandate in the least helpful way possible. We uncovered over 27,000 services with the same negotiated rate of $79,429.6. Figure 2 lists those services that have negotiated rates over $1, making it difficult for organizations to use this data effectively.
Fig 2: Percentage of billing codes negotiated for more than $1 each
Invalid negotiation arrangements:
CMS’ Price Transparency Guide outlines the enumerated values acceptable as part of this attribute, including fee-for-service, bundled, and capitation. Yet, invalid values—like custom, local, and default—lead us to believe that not all payers are using CMS’ validator tool.
Invalid characters for billing codes (HCPCS):
Our analysis has identified a small subset of HCPCS codes that are entirely invalid or include invalid characters. Curated data validation rules will ensure that only clean, valid data enters the final data store.
Expired records:
- Contracts no longer active are also showing up in the MRFs, which adds to the confusion when negotiated rates are used for comparison. Since the goal is to report accurate and active contractual rates, reporting rates from expired contracts makes MRFs invalid.
- Although it is not mandatory for payers to maintain active codes, publishing rates for billing codes that are no longer active, which CMS and the American Medical Association can decide, undermines MRF data accuracy.
These data issues cloud a payer’s data accuracy and the validity of their reporting through their MRFs. Because the data originates from payers and their contract management and information systems, working with experienced MRF data and technologists is critical to identify quality issues and cleaning up the data.
Solution strategies to overcome MRF data challenges
The challenges we outlined earlier can not only degrade or invalidate your MRF data but also limit the reliable insights you might glean from the data. Resolving these issues enhances your compliance and improves the value of your MRF data for strategic decisions like contract negotiations, value-based arrangements, and market analysis.
Figure 3 indicates the challenges we’ve discovered alongside four solution strategies that we’ve seen improve data validity for compliance and strategic insights.
Fig 3: Overcoming MRF data challenges with four solutions strategies.
Carry out data validation
Although CMS has published a validator tool, our ingestion of MRFs and validation rules tells a different story. Many payers are not validating their MRFs through the validator process, which results in incorrect or invalid data. For example, negotiated rates for services in the same geography that contracted with the same provider for the same service have shown extreme price variation across payers.
We recommend the following three-step data quality process to prepare your data for analysis stringently. Data assessment: Identify the standard limits of the negotiated rates by using benchmarking.
Data transformation: Restrict records that fall within these limits; all other data can be considered outliers. Data enrichment: Add and connect MRF data with other related information to provide deeper insights.
Ensure data quality checks at multiple levels
It’s vital to apply business rules to your ingestion and curation process. This standardizes the data, removes apparent outliers, and paves the way for generating insights using analytics seamlessly.
Data quality is paramount for accurate price transparency. The solution you use can be fully transparent only when the data being published is valid and reliable. Our data quality checks flag erroneous data that appears on a dashboard. It’s important to note that concerning data are not eliminated but highlighted so that payers have an opportunity to fix the MRFs.
Data enrichment
MRF data, although robust, can only be utilized in a few ways. You can amplify the impact of your data by enriching it with internal and external datasets. Adding datasets like geography, utilization, and quality levels increases the value of MRF data, which can be leveraged for specific use cases across the care continuum. For example, core payer data from claims, utilization, and quality datasets can be paired with MRF data to unlock additional use cases and support strategic operational, financial, and business decisions.
Maximizing the value of your MRF data using CitiusTech’s MRF solution called RealSight
Using our RealSight solutions, we prepared Figure 4, illustrating how ingested data from health plans can reveal insights critical to maximizing the value of MRFs.
You can use MRF data to identify alternative care settings, as shown in Figure 4. If this information is paired with provider performance and quality metrics, invaluable insights can change the trajectory of business and financial negotiations and strategic investments. This sample highlights what’s possible.
Fig 4: An example of the insights MRF data can reveal.
How can RealSight maximize the business value of MRF data for your organization?
A senior Gartner analyst has called CitiusTech a pioneer in price transparency. We’ve spent the last two years working with machine-readable files, building and implementing our regulatory compliance solution called MRF Engine (Machine Readable File Engine) for more than 10 health plans.
Now, we’ve launched our Price Transparency Analytics solution called RealSight. Beginning with data from four national payers– Aetna, Cigna, Elevance Health (Anthem), and United Healthcare (UHC)—we’re bringing a cloud-based data platform that offers Enriched Data as a Service to help healthcare organizations maximize the value enhanced MRF data.
Figure 5 gives you a sneak peek at the valuable insights embedded in the dashboard alone. From data visualizations highlighting negotiated rates varying wildly to how various services are distributed across specialties, RealSight will take your compliance-driven price transparency data to a new level of value for your organization’s strategic strength.
Fig 5: A Sneak peek at RealSight's Dashboard
Request a RealSight Demo and maximize the value of MRF data
Our team of technologists and MRF data experts can illustrate the hidden value in your MRF data and demonstrate how RealSight’s enhanced datasets turn regulatory-driven data into your go-to source for financial and business decisions. Contact us.
