Price Transparency with Decentralized Equity, Distributed Computing

How healthfi needs to be the lever to revolutioniz health

Over the past few months, I have chatted with several key players in the Health Finance (healthFI) domain and developed a vision for the future. I see a future where there will be a massive economic opportunity driven by the financialization of health. At Constellations.Health, our goal is to accelerate this future. In the past, I have managed and worked with teams to build companies from the early days of health digitization, FHIR revolution, oncology data products, multi-omics data, and machine learning to decentralized clinical trials. Throughout my 15+ years career, the common theme for lasting change and success has always been financial.  For patients, it is a financial burden. For providers, it is about improving outcomes while reducing financial burdens. For payers, it is to reduce collective financial burden. Although, for Health Tech, it has always been about improving outcomes but has traditionally been disconnected from the financial ROI. The key to unlocking the future of health is simple – reducing the financial burden of health for anyone, anywhere. 

HealthFi: Stripping away the noise 

By its nature, health data is extremely noisy, volatile and has a terribly low signal to noise ratio. By purely focusing on facilitating finance, Constellations.Health is able to eliminate a majority of that noise and bring our focus purely to financial burden. Additionally, recent market forces have provided ripe ground for having a stake in the ground. Two key movements are now enforced by the CMS which require hospitals and payers (which my co-founder, Akshay Sharma covered in detail here) to publicly publish transaction data. This collection of transaction data is a good backbone to begin determining financial burden.

However,  there are still challenges involved, primarily, the data sizes for these files are ginormous. By Constellations.Health estimates, the size of the data is upwards of 100-500Tb across all the providers and payers. Processing this data and making it accessible for a variety of applications is not trivial and requires more than moderate investments. During our investigations with specific payers looking to process this data, yearly costs may be as large as $2 million dollars just to put it into a DB. Although Price Transparency data may be public, it is also insanely inaccessible to Americans. Currently, there are several efforts being made to process this data by startups and companies in the space. 

Unfortunately, even with a complete dataset of price transparency, it is nearly not enough to demystify costs for individual visits to hospital. There are several varieties of data stores that must be tapped from outcome, location, explanation of benefits and more. Having worked in the health data industry, Constellations.Health knows that collecting, processing and making an aggregate of this data useful today is fundamentally broken. There are very few trusted parties that we would feel comfortable holding this data. This is where I strongly believe there’s a need for a paradigm shift. 

Change is neither simple nor linear … perhaps not even a curve

At this point as health data scales exponentially a single entity should not be collecting and organizing all of this data. The efforts to make price transparency a reality needs to be a joined effort between several members of the community. Community members such as providers, payers, patient groups, health tech companies and data providers all hold a piece of the puzzle.

For example, answering this specific question: ‘How much will knee surgery for a 45 year old male in Wisconsin will cost?’ is not answerable with just your medical data, cost data, EOB, etc. Data aggregated and joined by other willing participants is more likely to be able to answer at a level of contextual accuracy (based on their location, insurance, comorbidities, etc) that a patient can make a decision of if they should stay in state or go elsewhere. The question then follows is are the incentives aligned?

The lack of incentive alignment is the fundamental catch 22 of HealthFi. Currently, price transparency is focused on providing “apparent” but inaccessible value to consumers. However, fundamentally, there are no incentives for sharing and collecting data for this purpose. More importantly it isn’t TRANSPARENT who/how gets paid for the effort to do this work. For there to be real price transparency and financialization, these questions must be answered and made obvious upfront. 

Decentralized Equity, Distributed Compute

A bold new way forward

At Constellations.Health, we believe to solve this catch 22 we need to make a bold move. We need to decentralize information power brokers and centralize communities. If pricing information is open, then we can track accountability across the system when something doesn’t add up. By forming communities of patients and other specific stakeholders, we can create ownership and agency for everyone in an equitable way. 

At Constellations.Health, we will address this gotcha by focusing on decentralization of equity with Decentralized Autonomous Organization (DAO) mechanisms. 

From Investopedia:

A decentralized autonomous organization (DAO) is an emerging form of legal structure. With no central governing body, every member within a DAO typically shares a common goal and attempts to act in the best interest of the entity. Popularized through cryptocurrency enthusiasts and blockchain technology, DAOs are used to make decisions in a bottoms-up management approach.

Kahini Shah from Obvious Ventures talks about the finance community involved in an amazing post

The purpose of our DAO would be to provide transparency in how the equity generated by the data products created by these communities. Essentially, it is a vehicle to align incentives for community members to share data and fund projects from proceeds generated from data products/APIs. 

Constellations.Health will also be working on providing technology for the community projects to  process and prepare these data products with privacy baked in. A lot of this technology has already been figured out using distributed computing and privacy preserving technologies some of which we pioneered. Using distributed computing we can ensure that data processing can be done in disparate data silos and at a fraction of the cost of current cloud computing systems. Recently working with a large payer, our estimates show that can process the CMS price transparency data from payers at ⅕ of the cost. We accomplish this by leveraging techniques like stream processing and differential privacy. In fact, Akshay and I built 2 companies and world class teams doing just this. 

These are not easy challenges but never before has there been a community of like-minded people driven by a common goal of demanding transparency and avoiding financial ruin.

If you are reading this, we feel you are a part of that community!