How health insurance DAOs can break up RWD silos

Background

The holy grail for human health is accessibility of all health data related to each person into a single data lake, structured into a relational database in the same spec for each patient, for the entire world’s doctors and scientists to mine for insights that contribute to our understanding of disease and improving the standard of care. An App Store of biosensors, if you will, vying for the average patient/consumer to wear and share. Single payer systems (political and personal opinions aside), do benefit from centralization of clinical data, arguably the most valuable of all human health data at this point. Answering questions like, “does taking a booster dose of the COVID vaccine reduce deaths caused by infection with the Omicron variant given prior infection with the Delta variant for individuals aged 20–29?” should be readily answerable with any curious hypochondriac looking to make decisions that impact the health of them and their loved ones. Instead, we are left to consume the political or capitalist motivated talking points fed to us, unable to be audited or questioned, by the entities supposedly created to protect our health. If you think that the average person would never have the know-how to perform the necessary biostatistic considerations to ask this question properly, consider that brands and reputation can form around the generation of these epidemiological analyses to provide analysis legitimacy the same way brands provide legitimacy to any other product or service. What no entity has been able to figure out yet is, how to create the right mechanism design and incentive structure to integrate all human health data into a single platform such that it can be queried to answer questions like the one mentioned above.

Biotech Web2

Real-world data (RWD) companies who aggregate data from hospitals and with point-of-care diagnostics such as Flatiron Health, Foundation Medicine, Tempus, and ConcertAI, have viable businesses predicated on selling health data to pharma and (sometimes) smaller biotech companies. There are two primary reasons that drug developers make the vast majority of the health data market: 1) they have deep pockets and 2) only they can realize the value of the insights that are found in these health data aggregators. Even if you are the best computational biologist in the world, the only chance you’re going to get access to the biggest health databases in the world at one of these RWD companies is if you work for one of them, or a pharma company that happens to purchase a small subset of their database. This results in an underutilization of the data both from a capital perspective to these RWD companies, but also from a clinical perspective. There is vastly more data sitting in databases than is being studied for insights because RWD companies place multimillion dollar moats between you as a scientist and the keys to the data castle.

  1. Analytic validity: Lab data is easy to generate, but requires significant time to prepare and publish for peer-review, where ultimately, the data and code are not necessarily published, and thus, auditable. What would it take to force all lab data out into the open for anyone willing to audit it?
  2. Clinical validity: Clinical data has been historically generated with trials data, but is now becoming more amenable to using RWD to provide the evidence for clinical validity. What would it take to expose this clinical data for all to verify the authenticity?
  3. Clinical utility: This has always, and likely will always be demonstrated with clinical trials, though the population is usually not representative of the population it intends to be used for, and often is composed of the patients with the highest likelihood of making the diagnostic appear performant. What would it take to encode clinical trial protocols and randomization to ensure accurate disease representation and prevent cherry picking patients for your trial?
  4. Cost-effectiveness: This is typically demonstrated with rough approximations based on epidemiology and publicly available cost data. The conclusions of these analyses are dependent on highly sensitive assumptions that are difficult to accurately estimate. What would it take to expose claims data to get exact cost measurements?

Biotech Web3

Cryptocurrency markets have seen multiple bull runs over the past half decade, where the 2017 run was fueled by an ICO boom, 2021 was fueled by a decentralized finance (DeFi) boom, and the next bull run shaping up appears to be fueled by a decentralized autonomous organization (DAO) boom. DAOs are networks of individuals uniting under some common goal. That goal can be speculative, like getting a return on investment for DeFi projects (BitDAO), or something more fun like unsuccessfully bidding on the constitution (ConstitutionDAO). ConstitutionDAO raised $49M USD, but still pales in comparison to the size of the BitDAO treasury ($2.5B USD). If you’re wondering whether this sounds like a fanciful GoFundMe, there’s three major discrepancies that make DAOs far more powerful. First, platforms like GoFundMe are mechanisms for crowdfunding, but not governance. You can use GoFundMe to raise money for your cat’s surgery, but not to govern which surgical equipment the vet can use. Second, because DAOs exist entirely within blockchains, all fund transfers are auditable. This means that we as funders of your cat surgery can see exactly how much of the funds were used to pay for the vet, the anesthetic, kitty crutches, and ensure you didn’t just pocket the funds that were supposed to heal Mittens. The third, and most pertinent difference is censorship-resistance. Recently, GoFundMe successfully blocked the Canadian Trucker protest from raising over $9M. Whether or not you agree with the underlying message the truckers are trying to send, GoFundMe attempted to siphon the funds to charities it thought were considered to be of higher moral standard. This incident perfectly illustrated how Web2 platforms can be used to exercise control for political purposes and validates the market need for DAOs, especially for crowdfunding and governance.

Health Insurance DAO

Imagine a Health Insurance DAO governed by patients, for patients, with two objectives: increase quality associated life years (QALYs), and reduce healthcare costs. Ultimately, that should be what a health insurance company cares about. Unfortunately, in capitalist incentive structures, motivations of insurance companies are often profit-motivated, and it is left for others to care about health outcomes. This misalignment in incentives between patients and health insurers is a textbook example of the principal-agent problem and has left us with a suboptimal and grossly expensive healthcare system. Here in the US, we have the 18th best healthcare system in the world, but it costs us 20% of our GDP! Evidently, there is room for improvement.

The flywheel of health

The prospect of a group of individuals, motivated by studying real-world health data and capitalizing on the resulting medical discoveries to improve the health of humanity is invigorating. What is most exciting about a platform where health data is collected at a massive scale and being mined by the whole world’s computational biologists and other medical scientists, is that it will exponentially benefit the care of those who share more data. The more that’s shared, the deeper insights people will be able to receive about their own health. Maybe not everyone will care enough about their health and just share data for financial incentives, but for those with privacy concerns or sufficient wealth, optimizing health may be enough of an incentive to share their de-identified data with the world. Once data has been collected at a large scale and mined extensively, to have the best chances at a healthy life will likely require sharing health information. The more data you share, the deeper insights you will receive about your health, enabling you to biohack one day at a time and get direct feedback on how your actions affect your body. You only have one — might as well make the most of it.

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