Question: How can we have a massive explosion in health data and digital health technology and have no improvement in lifespan or reduction in costs?
Answer: Gross inefficiency and data hoarding.
Over 350,000 digital health developers are ultimately trying to improve human health and wasting billions of dollars 💸 and billions of hours ⏳ building the same features. We have a market incentive structure that punishes open-source cooperation, and data sharing and 💰 rewards closed, proprietary, and wasteful duplication of effort.
Solution: An open platform for clinical research that incentivizes cooperation and data sharing.
CureDAO utilizes a new meritocratic economic system of Collaborationism that will transcend the incentivization failures and inefficiencies of previous economic models such as Communism and Capitalism. The CureDAO incentive structure overcomes the traditional collaboration and data sharing barriers by encoding contributions through non-fungible tokens (NFTs). Using smart contracts, the platform will compensate all contributors of work, data, and IP with ongoing royalties.
Our hypothesis (and dream) is that this new system can accelerate the rate of clinical discovery 350,000 times 🚀️ and create a world where suffering is optional. 😃
But we can't realize this dream without you!
Hmm. You're still here, so I guess you're not convinced. 😕 Then venture on, dear reader!
Our first project is a community-owned, open-source, no-code platform for health data aggregation and analysis.
It will provide a basic foundational technology layer to remove barriers for physicians, researchers, clinicians, and developers of digital health applications.
It consists of two primary components:
Our novel incentive structure overcomes the traditional collaboration and data sharing barriers by encoding contributions through non-fungible tokens (NFTs).
Using smart contracts, the platform will compensate all contributors with royalties.
The solution is to use the oceans of real-world evidence to accelerate the discovery of new cures and reveal hidden causes of disease.
The human body can be viewed as a black box with inputs (like diet, treatments, etc.) and outputs (like symptom severity). We're creating a mathematical model of human biology to determine the input factors and values that produce optimal health outcomes.
The high costs lead to:
1. No Data on Unpatentable Molecules
We still know next to nothing about the long-term effects of 99.9% of the 4 pounds of over 7,000 different synthetic or natural compounds. This is because there's only sufficient incentive to research patentable molecules.
2. Lack of Incentive to Discover Every Application of Off-Patent Treatments
Most of the known diseases (approximately 95%) are classified as rare diseases. Currently, a pharmaceutical company must predict particular conditions to treat before running a clinical trial. Suppose a drug is effective for other diseases after the patent expires. In that case, there isn't a financial incentive to get it approved for the different conditions.
3. No Long-Term Outcome Data
It's not financially feasible to collect a participant's data for years or decades. Thus, we don't know if the long-term effects of a drug are worse than the initial benefits.
4. Negative Results Aren't Published
Pharmaceutical companies tend to only report "positive" results. That leads to other companies wasting money repeating research on the same dead ends.
5. Trials Exclude a Vast Majority of The Population
One investigation found that only 14.5% of patients with major depressive disorder fulfilled eligibility requirements for enrollment in an antidepressant trial. Furthermore, most patient sample sizes are very small and sometimes include only 20 people.
6. We Only Know 0.000000002% of What is Left to be Researched
The more research studies we read, the more we realize we don't know. Nearly every study ends with the phrase "more research is needed".
If you multiply the 166 billion molecules with drug-like properties by the 10,000 known diseases, that's 1,162,000,000,000,000 combinations. So far, we've studied 21,000 compounds. That means we only know 0.000000002% of the effects left to be discovered.
Despite this massive growth in health data and innovation, we've seen increased costs and disease burden and decreased life expectancy.
The reason is awful incentives. There are more than 350,000 health apps.
Each costs an average of $425,000 to develop.
Most have significant overlap in functionality, representing a cost of $157,500,000,000 on duplication of effort.
Isolated streams of health data can only tell us about the past. For example, dashboards filled with descriptive statistics such as our daily steps or sleep.
If this data and innovation efforts were combined, this could increase the rate of progress by 350,000 times.
The obstacle has been the free-rider problem. Software developers that open source their code give their closed-source competitors an unfair advantage, increasing their likelihood of bankruptcy.
How to Overcome the Free-Rider Problem
A global open-source platform and plugin framework will enable the transformation of data into clinical discoveries.
The functional scope of the platform includes:
of health data from different sources.
Create a basic foundational technology layer suitable for any digital health application that provides better interoperability, portability, availability, analysis, and data security.
The platform consists of two primary components:
Data Ingestion and Access API
The Unified Health application programming interface (API) includes an OpenAPI specification for receiving and sharing data with the core database. Software development kits (SDKs) will enable developers to implement easy automatic data access and sharing options in their applications.
Data Mapping and Validation
Data from files or API requests can be mapped from many different proprietary formats into a standard schema.
Data should be owned by the individual who generated it. It should remain under their control throughout the entire data life-cycle from generation to deletion.
Value stream management allows the exchange of data for tokens.
3rd party plugins can interact with the core and provide additional functionality. They may be free or monetized by their creator. These include:
Data Analysis Plugins
Data Analysis Plugins will apply statistical and machine learning methods to the ocean of high-frequency longitudinal individual and population-level data. The resulting value will include:
Example Data Presentation Plugins
We use the DAO structure and NFT IP royalties to reward data sharing and open-source collaboration.
This illustrates the flow of value between different stakeholders. Unlike traditional zero-sum games, CureDAO provides everyone with more value from participation than they have to put into it.
Incentives for Patients to share their de-identified data will include:
Businesses housing data silos include health insurers, pharmacies, grocery delivery services, digital health apps, hospitals, etc. These will be incentivized to allow individuals to easily share their data via a well-documented OAuth2 API by:
Disease advocacy nonprofits will benefit from promoting studies to their members by:
CureDAO is a laboratory consisting of many experiments.
It's a global laboratory where the 7 billion human "natural experiments" are conducted, revealing the effects of various factors on human health and happiness.
It's an experiment to determine if a new model for clinical research using real-world data can more effectively reduce the global burden of chronic illness.
It's an experiment to see if a new economic model called Collaborationism can reward the creation of open-source "public goods" and overcome the failures of Capitalism and Communism.
It's an experiment to determine if a direct democracy can produce better results than traditional hierarchical command and control organizations.
Given the unprecedented nature of such a project, each working group will constantly be experimenting with new ways to execute this mission. We recognize the importance of using real-world evidence to improve human health. Execution within the working groups should take the same data-driven approach to manage their area of the overall mission.
Accordingly, the organization is composed of three primary components.
CureDAO generates revenue streams to compensate IP and data contributors and sustain and grow the project. The biotech and pharmaceutical industries are two of the fastest-growing sectors of the U.S. economy.
These factors all point to a massive potential for the revenue necessary to sustain this project.
The primary sources of income include:
Digital health companies can save months of development time and tens of thousands of dollars by using our platform instead of reinventing the wheel. A usage-based subscription platform for health application developers would start at $0.50/end-user per month.
Electronic health records (EHRs) can be used to support randomized controlled trials (RCTs). A meta-analysis found that the per-patient cost in EHR-supported trials varied from $44 to $2000.
Using NFTs, we can link donated data to the patient to receive ongoing royalty payments for the use of their data once new interventions reach the market.
The project core framework will be open-source for any non-commercial purpose. However, we will utilize a Fair-code or Fair Source licensing model to generate revenue when used by for-profit entities.
Licensing fees will be negotiated such that a fraction of the profits generated by the licensee's project use.
Congratulations! You've won the chocolate factory! You did it! You did it! I knew you would! I just knew you would!
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