Case Study

Unlocking the Potential of Healthcare Price Transparency for Employers: How to Avoid Missing the Forest for the Trees

This article was written in collaboration among Ahmed Marmoush (CEO & co-founder, Handl Health), Ria Shah (CPO & co-founder, Handl Health) and Francois de Brantes (EVP, Network Performance, XO Health).

Over the last few years, the American healthcare industry has seen a push toward greater price transparency, driven by regulation like Transparency in Coverage (TiC). TiC aims to provide plan sponsors (employers) and plan beneficiaries (employees and dependents) access to detailed information about the prices of medical services, helping them make informed decisions about their healthcare benefits. It does so by requiring plan sponsors to publish Machine Readable Files (MRFs) of negotiated rates and offer plan beneficiaries a “shopping tool” to understand their cost-sharing liability for healthcare services.

In theory, TiC should assist employers in comparing payor networks to assess relative cost-effectiveness. It should also enable plan members to understand the cost of a single appointment or procedure with different doctors or at different locations. This transparency should offer valuable insights that help navigate the complex healthcare landscape, but it doesn’t, due to reported issues with data quality and completeness. Despite these limitations, can the MRFs be used to drive a better purchasing strategy for employers and their beneficiaries? In this article, we use the MRFs to demonstrate how the data can be used to evaluate networks and design cost effective and accessible health plans – without the need for traditional network discount analyses. 

The Challenge of Inaccurate Data

Several analyses highlighted in the Health Affairs' Forefront series on 'Provider Prices in the Commercial Sector' shed light on the challenges faced in ensuring that the data made available under the TiC rule is reliable and in compliance – collectively revealing significant inaccuracies and discrepancies in the pricing data provided by payors.


The accuracy and completeness of TiC data has significant consequences:

  • Statutory Penalties: Inaccurate or incomplete TiC data could expose employers to significant statutory penalties, highlighting the need for greater compliance.
  • Plan Sponsor Challenges: With inaccurate or incomplete TiC data, employers and their consultants will continue to struggle with comparing prices of networks available through insurance carriers or third-party administrators. They resort to a discount analysis, which lacks granularity, precision and impartiality. Accurate TiC data would enable more precise and granular network comparisons and claims repricing.
  • Patient Decision-Making: Plan beneficiaries will continue accessing care without context or knowledge on price variation and out-of-pocket liability. They would also benefit from accurate TiC data. It allows them to make informed choices about where to receive care, helping them accurately estimate the costs of healthcare services.

And so, the question remains: despite the current limitations of the MRFs, can they be used to drive a better purchasing strategy for employers and their members?

Before price transparency regulations, employers and members lacked the ability to meaningfully compare the cost of network choices or even doctors and hospitals. In theory, the availability of payor MRFs introduces more granular and reliable cost estimates when comparing networks and healthcare services.

Let's consider the analogy of leaves, branches, trees and forests. A leaf in this instance represents a provider delivering care. A branch represents all providers that deliver a procedure at a specific hospital (e.g. a total knee arthroplasty). A tree represents the entirety of providers and services at a hospital. The forest represents all hospitals in a given carrier network. Some trees carry defects and are missing leaves and branches (e.g. providers and procedure codes). Even with these defects, it is possible to compare trees and even forests to see patterns emerge, and these patterns can provide valuable insight when making purchasing decisions, designing health plans or seeking care. 

Case Study: Analyzing variation in contracted rates across plans and hospitals

To illustrate the practical implications of TiC data in its current state and how to derive the most value, we conducted an analysis comparing two carrier networks: UnitedHealthcare’s (UHC) Navigate network file and Blue Cross Blue Shield (BCBS) of Texas’ Blue Essentials network file, across three large acute care hospitals in Dallas.

Exhibit 1: Comparative details of hospitals included in the analysis

The analysis used 14 billing codes, including 11 Current Procedural Terminology (CPT) codes and 3 Diagnosis-Related Groups (DRG) codes, drawn from the two carrier’s MRFs from March and April 2024. Specifically, the study centered on institutional rates, providing insights into the costs associated with inpatient and outpatient services.

Plan Selection: Analyzing the Forests

The analysis revealed meaningful differences in contracted rates for the same billing codes between BCBS and UHC, ranging from -52% (favorable to UHC by 52%) to +24% (favorable to BCBS), and averaging a favorable 11% for UHC, meaning that, overall, BCBS prices were 11% higher than UHC’s.

Beyond this general average, the variation in prices seem to form certain observable patterns, from the setting of care (i.e. inpatient vs. outpatient), to lines of services (e.g. orthopedics) to acute care hospitals overall. These differences create insights that could lead a purchaser to making a different decision than when sticking with the overall average difference.

Exhibit 2 shows that UHC’s contracted rates were 10% higher for inpatient services, and 25% lower for outpatient services when compared to BCBS. Exhibit 3 shows that when the prices are compared by clinical service line, the differences can be reduced or inverted in certain instances (e.g. obstetrics). Exhibit 4 shows that the price differences are very favorable to UHC for one facility (Baylor University Medical Center) across all lines of services, and moderately disfavorable for the other facilities.

Exhibit 2: Comparative analysis of average contracted rates between UHC and BCBS across inpatient and outpatient services

Exhibit 3: Comparative analysis of billing code average contracted rates between UHC and BCBS by clinical service line

Exhibit 4: Comparative analysis of billing code average contracted rates between UHC and BCBS by hospital

Care Navigation: Analyzing the Trees

As shown in Exhibits 3 & 4, there are significant differences in contracted rates between facilities within a plan and across lines of service. This holds true for both carriers and across services. However, the net differences in rates for a specific service across providers and carriers can either be directionally the same as observed in the general average, or in the inverse direction. Exhibit 5 shows the differences for DRG 470 (replacements of the lower joints) between UHC and BCBS for each of the facilities. UHC prices are lower for this DRG across all three hospitals. Exhibit 6 shows the same differences for CPT 45380 (colonoscopy) and in that instance UHC has lower prices in two of the three hospitals.

Exhibit 5: Comparative analysis of DRG 470 contracted rates for BCBS and UHC at the three acute care hospitals in Dallas

Exhibit 6: Comparative analysis of CPT 45380 contracted rates for BCBS and UHC at three acute care hospitals in Dallas

What these two Exhibits imply is that, even once a plan has been selected, the choice of the site of care to optimize plan and member costs can vary. While in both plans members would benefit by getting a knee or hip replacement at Baylor, plan members whose employer is using the BCBS network would benefit significantly by having a colonoscopy at Texas Health Presbyterian.


In this analysis we compared two plans’ networks that are offered to self-insured employers. On the face of it, our analysis suggests that in this particular metropolitan area, employers would incur lower prices for certain inpatient and outpatient services when selecting the UHC plan. 

However, when comparing prices at the service line level and for an acute care hospital overall, the quest for price optimization requires a lot more nuance. For example, as shown in Exhibit 3, an employer in this geography with an employee population that has a young demographic profile or a predominantly female workforce with a potentially higher utilization of orthopedic and obstetrical services might be better off selecting UHC. Similarly, if an employer’s historical claims reveal their workforce favors Baylor University Medical Center (Exhibit 4), UHC may be the better choice. Conversely, if an employer has mostly employees with cardiac-related conditions and favors Texas Health Presbyterian, they may be better off with BCBS.

The availability of payor MRFs enables a degree of granularity and precision when evaluating a plan’s network that powers a much more refined decision-making process than the traditional general network discount analysis. This presents an opportunity to personalize network selection based on an employer’s geographic location, workforce demography, utilization patterns and clinical needs.

Our analysis, like others, highlights the differences in prices for the same procedures across acute care hospitals. Importantly, however, it shows a clear pricing pattern for procedures across different acute care hospitals. In other words, while for a given procedure a given acute care hospital can be higher or lower than a competitor, some acute care hospitals are more expensive overall and some are less expensive. And the differences in those prices have significant implications on total plan spend and on employee cost-sharing.

As a result, once an employer has completed the first purchasing decision and selected their plan and network, the payor MRFs can be valuable in plan design and guiding their employees on where to seek care. This is optimizing for the second purchasing decision, “purchasing care.” 

However, this two-step purchasing strategy, which almost all employers are forced into, is still suboptimal. Until employers become freer to leverage prices in order to configure their networks - what we call “Network Design 2.0” - network selection combined with in-network steerage remains a relatively blunt tool for price optimization.

Policy implications

As plan sponsors, employers bear legal liability for the accuracy and completeness of the TiC files posted under their tax IDs, and they have a clear fiduciary responsibility to use the available data to lower or moderate the rate of increase in plan spending through accurate network comparisons, effective plan design and equipping their employees with cost estimates to make informed choices about where to receive care. 

While not illustrated in this analysis, the work done to create the Exhibits revealed many gaps in the completeness and accuracy of the two MRFs used. Those gaps make it hard for employers to fully accomplish their fiduciary duty to provide accurate information to plan members and optimize plan spending. As such, like others, we strongly recommend the following actions are taken to improve MRF compliance with TiC regulations:

  1. Plan sponsors should be required to attest to the accuracy and completeness of the MRFs posted with their EIN, which should be contractually delegated to their plan’s network provider or TPA
  2. The CMS’ MRF validation tool, should be significantly enhanced to not only check that the MRFs are schematically correct, but also complete and accurate


Despite the current limitation of the payor MRFs, we find that they can still be used to drive a better purchasing strategy for plan sponsors and their beneficiaries! At a small scale, it's feasible to identify patterns in the MRFs that provide valuable insights when selecting networks, designing health plans or seeking care. The payor MRFs have introduced a degree of granularity and precision when evaluating plan costs that should open the door to the next generation of plan design and effective utilization. More work is required at policy and legislative levels to continue enhancing the value of these datasets so that we can collectively leverage their transformative potential.

Disclaimer: The analysis in this article represents data from a point in time, using data from March and April 2024.

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