Turning of the Tide – Transparency in Coverage is a Sea-Change for Healthcare

Updated on October 20, 2022
Healthcare costs and fees concept.Hand of smart doctor used a calculator for medical costs in modern hospital with VR icon diagram

What does opening the door to greater transparency in the healthcare sector look like? How can we disseminate healthcare pricing information so it’s accessible to all stakeholders?

As of July 1, 2022, the American healthcare industry is reckoning with these questions in real time. Per the Transparency in Coverage Rule, the Centers for Medicare and Medicaid Services (CMS) now requires all healthcare payer organizations to post the agreed-upon rate for every healthcare procedure or service offered in-network with their health plan. More than 900 payer organizations in the U.S. were required to publish their price transparency datasets.

Experts believe that this new transparency will inspire stiffer competition in the healthcare services market, and, ultimately, smaller year-over-year increases in total overall healthcare spending in the U.S.

An ocean of data

It sounds like a no holds barred win for consumers — but there’s a catch.

The files in question are colossal, containing every possible medical service that can be offered by any in-network provider, down to the smallest variations. Turquoise Health, a private company attempting to crunch and cleanse this data, anticipates that final downloads will equal two or three petabytes total. (For context, they’d downloaded approximately half a petabyte — or 700,000 unique files — by mid-July.)

A chorus of opposition from healthcare experts has ensued, many arguing that this ocean of data was simply too vast to provide any meaningful level of feedback to the market — or to the consumers it was meant to protect.

While CMS anticipated that a new market would form where technologists and experts offer access to insights mined from these datasets to healthcare stakeholders, there just hasn’t been enough time for them to respond to the volume of data that is contained in these datasets. 

As we wait, it’s important to remember that the datasets in play are some of the largest healthcare datasets ever made available to the public. It will require time and full access to cloud computing resources to fully parse and warehouse this data. The cloud-based resources required are not readily available to many analysts in the healthcare space — particularly analysts in academic environments, where IT resources aren’t always allocated to projects based on a business value justification. 

A sea of opportunity for payers

Some organizations do have access to the technology and personnel talent to work with these datasets, but they often lack subject matter expertise in healthcare provider contracting. They may be able to crunch the data, but they won’t understand what insights will prove most valuable to stakeholders. 

However, tech-enabled healthcare organizations of all sizes are hard at work constructing the code, processes and resources needed to ingest these datasets — and ultimately, to mine the insights within to provide value to patients, plan members and everyday healthcare consumers in new and exciting ways.

For instance, private payer organizations can now forensically build model representations of their competitors’ plans’ networks. This will help payers better understand their competitors’ overall contracting strategy and network sculpting priorities. It’ll also accelerate potential opportunities for payers to contract with providers in value-based payment models that provide additional incentives or revenue to providers that meet agreed-upon performance benchmarks.

Due to their size, large national payer networks often have more variation in the contracted rates for the same services. This means that a regional employer who offers a self-funded plan through a large national payer could potentially be paying a lot more than what a similar employer in a different geography is paying for the same healthcare services, all else being equal.

For this reason, some large employer groups and government organizations are forming purchasing organizations and combining leverage to form their own health plan.  By contracting directly with multiple independent hospital systems, employer groups can pit them against one another to contract highly competitive rates. 

The granular data in the Transparency in Coverage datasets could give these employer plan sponsors and purchasing organizations additional contracting leverage, potentially leading to lower costs or richer benefits for the 155 million Americans who get their health insurance from employer-sponsored health plans. 

Waves of value for all stakeholders

For providers and hospitals, these datasets possess a level of granularity that is so detailed, it can inform providers of opportunities where they have leverage over the payers with whom they are negotiating their in-network rates. Payers may have to provide more thorough justification for rate differences by linking their costs to the quality of care that a provider delivers. This justification is largely absent for most fee-for-service contracting arrangements today.

Heavyweight consulting firms also have a part to play in harnessing information from these datasets. The “Big Four” consultants especially can act as access points that will ultimately connect smaller payers and providers to valuable insights.

Researchers will also want to work with this data. For example, our understanding of how potential harm has played out from value-based payment models between payers and providers who primarily serve rural or socioeconomically marginalized communities is hazy at best. This data gives us the opportunity to better understand the impacts of these types of models — particularly in vulnerable communities where hospital closures due to rapid changes in patient mix are more common.

Historically, the quality of synthetically generated claims data has been very poor with respect to measurements of cost of care. Now, with this broad pool of data available, detailed and accurate synthetic datasets can be created to study the impacts of large systemic changes in healthcare finance policy.

Plus, as more payers are combining with telehealth providers to own the “first mile” of care delivery, understanding the relationship between cost and quality of services rendered over telehealth versus in-person office visits will help regulators protect consumers from potential backlash from payer overuse of telehealth services in lieu of services that need to be provided in an office or home visit.

Waiting for the tide of data

All these stakeholders have different interests and seek somewhat disparate insights from these datasets, but to be certain, these possibilities – and so many more that we have yet to discover – depend upon the public’s access to the granular level of detail within these vast datasets. 

The alternative —more compact, aggregated datasets — would be of limited use. They can’t help researchers and public policy officials understand and predict hospital closures or give patients a front-row seat to the shifts in provider networks that impact their care.

Transforming these datasets into insights ready for decisioning will take time. But in the long-term, full transparency can only benefit healthcare stakeholders — and, most vitally, ensure patients receive the care they need at a price they can afford. 

About the author

Seth Lester, Senior Industry Consultant, SAS, specializes in actuarial methods for quantifying health care finance and risk, primarily for US-based commercial payer organizations. He is an Associate of the Society of Actuaries. Seth is passionate about evangelizing the risks and opportunities of modern-day analytics in the US healthcare space, as well as the importance of democratizing financial risk insights across all healthcare stakeholders – most importantly, patients.