The solution to the lack of progress and increasing healthcare expense is to use the oceans of real-world evidence to discover new cures.

The Personalized, Preventive, Precision Medicine of the Future

Out of an existing pool of big health data, an insilico model of human biology can be developed to discover new interventions and their personalized dosages and combinations.

This will enable the discovery of the full personalized range of positive and negative relationships for all factors without a profit incentive for traditional trials.

The Potential of Real-World Evidence-Based Studies

• Diagnostics - Data mining and analysis to identify causes of illness

• Preventative medicine - Predictive analytics and data analysis of genetic, lifestyle, and social circumstances to prevent disease

• Precision medicine - Leveraging aggregate data to drive hyper-personalized care

• Medical research - Data-driven medical and pharmacological research to cure disease and discover new treatments and medicines

• Reduction of adverse medication events - Harnessing of big data to spot medication errors and flag potential adverse reactions

• Cost reduction - Identification of value that drives better patient outcomes for long-term savings

• Population health - Monitor big data to identify disease trends and health strategies based on demographics, geography, and socioeconomic

Cost Savings in Drug Development

Failed drug applications are expensive. A global database of treatments and outcomes could provide information that could avoid massive waste on failed trials.

Cost Savings Through Decentralization

Problems with Historical Observational Research

When people think of observational research, they typically think of correlational association studies.

Why It Seems Like Diet Advice Flip-Flops All the Time

In 1977, the USDA and Time Magazine warned Americans against the perils of dietary cholesterol. Yet, in 1999, TIME released a very different cover, suggesting that dietary cholesterol is fine.

Correlational is Not The Same as Causation

There are two primary ways of undertaking studies to find out what affects our health:

1. observational studies - the easier of the two options. They only require handing out questionnaires to people about their diet and lifestyle habits, and then again a few years later to find out which patterns are associated with different health outcomes.

2. randomized trials - the far more expensive option. Two groups of randomly selected people are each assigned a different intervention.

The most significant benefit of randomized trials is the "control group". The control group consists of the people who don't receive the intervention or medication in a randomly-controlled trial. It helps to overcome the confounding variable problem that plagues observational studies.

A common source of confounding variables in correlational association studies is the "healthy person bias". For instance, say an observational study finds "People Who Brush Teeth Less Frequently Are at Higher Risk for Heart Disease". It may just be a coincidence caused by a third confounding variable. People that brush their teeth more are more likely to be generally concerned about their health. So, the third confounding factor could be that people without heart disease could also exercise more or eat better.

However, the massive amount of automatically collected, high-frequency longitudinal data we have today makes it possible to overcome the flaws with traditional observational research.

Overcoming the "No Control Group" Problem

The primary flaw with observational research is that they lack the control group. However, a single person can act as their own control group with high-frequency longitudinal data. This is done by using an A/B experiment design.

For instance, if one is suffering from arthritis and they want to know if a Turmeric Curcumin supplement helps, the experimental sequence would look like this:

1. Month 1: Baseline (Control Group) - No Curcumin

2. Month 2: Treatment (Experimental Group) - 2000mg Curcumin/day

3. Month 3: Baseline (Control Group) - No Curcumin

4. Month 4: Treatment (Experimental Group) - 2000mg Curcumin/day

The more this is done, the stronger the statistical significance of the observed change from the baseline. However, there are also effects from other variables. These can be addressed using a diffusion-regression state-space model that predicts the counter-factual response. This involves the creation of a synthetic control group. This artificial control illustrates what would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to:

1. infer the temporal evolution of attributable impact

2. incorporate empirical priors on the parameters in a fully Bayesian treatment

3. flexibly accommodate multiple sources of variation, including:

   1. local trends

   2. seasonality

   3. the time-varying influence of contemporaneous covariates

• Strength Coefficient: A relationship is more likely to be causal if the correlation coefficient is large and statistically significant. This is determined through the use of a two-tailed t-test for significance.

• Consistency Coefficient: A relationship is more likely to be causal if it can be replicated. This value is related to the variation of the average change from baseline for other participants with the same treatment outcome variables in conjunction with the variation in average change from multiple experiments in the same individual.

• Specificity Coefficient: A relationship is more likely to be causal if there is no other plausible explanation. Relationships are calculated based on different potential predictor variables available for the individual over the same period. The value of the Specificity Coefficient starting at one is decreased by the strength of the most robust relationship of all other factors.

• Temporality Coefficient: A relationship is more likely to be causal if the effect always occurs after the cause.

• Gradient Coefficient: The relationship is more likely to be causal if more significant exposure to the suspected cause leads to a greater effect. This is represented by the k-means squared difference between the normalized pharmacokinetic time-lagged treatment outcome curves.

• Plausibility Coefficient: A relationship is more likely to be causal if a plausible mechanism exists between the cause and the effect. This is derived from the sum of the crowd-sourced plausibility votes on the study.

• Coherence: A relationship is more likely to be causal if compatible with related facts and theories. This is also derived from the sum of the crowd-sourced plausibility votes on the study.

• Experiment Coefficient: A relationship is more likely to be causal if it can be verified experimentally. This coefficient is proportional to the number of times an A/B experiment is run.

• Analogy: A relationship is more likely to be causal if there are proven relationships between similar causes and effects. This coefficient is proportional to the consistency of the result for a particular individual with the number of other individuals who also observed a similar effect.

Meta-Analyses Support of Real-World Evidence

Observational real-world evidence-based studies have several advantages over randomized, controlled trials, including lower cost, increased speed of research, and a broader range of patients. However, concern about inherent bias in these studies has limited their use in comparing treatments. Observational studies have been primarily used when randomized, controlled trials would be impossible or unethical.

when applying modern statistical methodologies to observational studies, the results are generally not quantitatively or qualitatively different from those obtained in randomized, controlled trials.

Mortality Observational Studies

Observational Studies for Various Outcomes

Historical Evidence in Support of Real-World Evidence

There is compelling historical evidence suggesting that large scale efficacy-trials based on real-world evidence have ultimately led to better health outcomes than current pharmaceutical industry-driven randomized controlled trials.

For over 99% of recorded human history, the average human life expectancy has been around 30 years.

1893 - The Advent of Safety and Efficacy Trials

In the late nineteenth and early twentieth century, clinical objectivity grew. The independent peer-reviewed Journal of the American Medical Association (JAMA) was founded in 1893. It would gather case reports from the 144,000 physicians members of the AMA on the safety and effectiveness of drugs. The leading experts in the area of a specific medicine would review all of the data and compile them into a study listing side effects and the conditions for which a drug was or was not effective. If a medicine were found to be safe, JAMA would give its seal of approval for the conditions where it was found to be effective.

The adoption of this system of crowd-sourced, observational, objective, and peer-reviewed clinical research was followed by a sudden shift in the growth of human life expectancy. After over 10,000 years of almost no improvement, we suddenly saw a strangely linear 4-year increase in life expectancy every single decade.

1938 - The FDA Requires Phase 1 Safety Trials

"adequate tests by all methods reasonably applicable to show whether or not such drug is safe for use under the conditions prescribed, recommended, or suggested in the proposed labeling thereof."

This consistent four-year/decade increase in life expectancy remained unchanged before and after the new safety regulations.

This suggests that the regulations did not have a large-scale positive or negative impact on the development of life-saving interventions.

1950's - Thalidomide Causes Thousands of Birth Defects Outside US

Fortunately, the existing FDA safety regulations prevented any birth defects in the US. Despite the effectiveness of the existing US regulatory framework in protecting Americans, newspaper stories such as the one below created a strong public outcry for increased regulation.

1962 - New Efficacy Regulations Reduce the Amount and Quality of Efficacy Data Collected

As effective safety regulations were already in place, the government instead responded to the Thalidomide disaster by regulating efficacy testing via the 1962 Kefauver Harris Amendment. Before the 1962 regulations, it cost a drug manufacturer an average of $74 million (2020 inflation-adjusted) to develop and test a new drug for safety before bringing it to market. Once the FDA had approved it as safe, efficacy testing was performed by the third-party American Medical Association. Following the regulation, trials were instead to be conducted in small, highly-controlled trials by the pharmaceutical industry.

Reduction in Efficacy Data

The 1962 regulations made these large real-world efficacy trials illegal. Ironically, even though the new regulations were primarily focused on ensuring that drugs were effective through controlled FDA efficacy trials, they massively reduced the quantity and quality of the efficacy data that was collected for several reasons:

• New Trials Were Much Smaller

• Participants Were Less Representative of Actual Patients

• They Were Run by Drug Companies with Conflicts of Interest Instead of the 3rd Party AMA

Reduction in New Treatments

The new regulatory clampdown on approvals immediately reduced the production of new treatments by 70%.

Explosion in Costs

High Cost of Development Favors Monopoly and Punishes Innovation

There's another problem with the increasing costs of treatment development. In the past, a genius scientist could come up with a treatment, raise a few million dollars, and do safety testing. Now that it costs a billion dollars to get a drug to market, the scientist has to persuade one of a few giant drug companies that can afford it to buy his patent.

Then the drug company has two options:

Option 1: Risk $1 billion on clinical trials

Possibility A: Drug turns out to be one of the 90% the FDA rejects. GIVE BANKER A BILLION DOLLARS. DO NOT PASS GO.

Possibility B: Drug turns out to be one of the 10%, the FDA approves. Now it's time to try to recover that billion dollars. However, very few drug companies have enough money to survive this game. So, this company almost certainly already has an existing inferior drug on the market to treat the same condition. Hence, any profit they make from this drug will likely be subtracted from revenue from other drugs they've already spent a billion dollars on.

Option 2: Put the patent on the shelf

Do not take a 90% chance of wasting a billion dollars on failed trials. Do not risk making your already approved cash-cow drugs obsolete.

What's the benefit of bringing better treatment to market if you're just going to lose a billion dollars? Either way, the profit incentive is entirely in favor of just buying better treatments and shelving them.

Cures Are Far Less Profitable Than Lifetime Treatments

If the new treatment is a permanent cure for the disease, replacing a lifetime of refills with a one-time purchase would be economically disastrous for the drug developer. With a lifetime prescription, a company can recover its costs over time. Depending on the number of people with the disease, one-time cures would require a massive upfront payment to recover development costs.

How is there any financial incentive for medical progress at all?

Fortunately, there isn't a complete monopoly on treatment development. However, the more expensive it is to get a drug to market, the fewer companies can afford the upfront R&D investment. So the drug industry inevitably becomes more monopolistic. Thus there are more situations where the cost of trials for a superior treatment exceeds the profits from existing treatments.

People With Rare Disease are Severely Punished

In the case of rare diseases, increasing the cost of treatment development to over a billion makes it impossible to recover your investment from a small number of patients. So rare disease patients suffer the most severe harm from the added regulatory burden on development.

How high should the cost of drug development be on our list of human problems? Well, when something costs more, you get less of it. For people dying of cancer, the fact that we couldn't afford enough research to cure them is definitely at the top of their list of human problems.

Delayed Life-Saving Treatments

One unanticipated consequence of the amendments was that the new burden of proof made the process of drug development both more expensive and much longer, leading to increasing drug prices and a “drug lag”. After that point, whenever they released some new cancer or heart medication that would save 50 thousand lives a year, it meant that over the previous ten years of trials, 500 people died because they didn't have access to the drug earlier.

Deaths Due to US Regulatory "Drug Lag"

• interleukin-2

• Taxotere

• vasoseal

• ancrod

• Glucophage

• navelbine

• Lamictal

• ethyol

• photofrin

• rilutek

• citicoline

• panorex

• Femara

• ProStar

• omnicath

Before US FDA approval, most of these drugs and devices had already been available in other countries for a year or longer.

Following the 1962 increase in US regulations, one can see a divergence from Switzerland's growth in life expectancy, which did not introduce the same delays to availability.

Perhaps it's a coincidence, but you can see an increase in drug approvals in the '80s. At the same time, the gap between Switzerland and the US gets smaller. Then US approvals go back down in the '90s, and the gap expands again.

Increase in Patent Monopoly

Industry agitation surrounding the “drug lag” finally led to the modification of the drug patenting system in the Drug Price Competition and Patent Term Restoration Act of 1984. This further extended the life of drug patents. Thus Kefauver's amendments ultimately made drugs more expensive by granting longer monopolies.

Decreased Ability to Determine Comparative Efficacy

The placebo-controlled, randomized controlled trial helped researchers gauge the efficacy of an individual drug. However, it makes the determination of comparative effectiveness much more difficult.

Slowed Growth in Life Expectancy

From 1890 to 1960, every decade saw a linear 4-year increase in human lifespan. This amazingly linear growth rate followed millennia with a flat human lifespan of around 28 years. Following this new 70% reduction in the pace of medical progress, the growth in human lifespan was immediately cut in half to an increase of 2 years per decade.

Diminishing Returns?

One might say, “It seems more likely — or as likely — to me that drug development provides diminishing returns to life expectancy.” However, diminishing returns produce a slope of exponential decay. It may be partially responsible, but it’s not going to produce a sudden change in the linear slope of a curve a linear as life expectancy was before and after the 1962 regulations.

Correlation is Not Causation

You might say "I don't know how much the efficacy regulations contribute to or hampers public health. I do know that correlation does not necessarily imply causation." However, a correlation plus a logical mechanism of action is the least bad method we have for inferring the most likely significant causal factor for an outcome (i.e. life expectancy). Assuming most likely causality based on temporal correlation is the entire basis of a clinical research study and the scientific method generally.

Impact of Innovative Medicines on Life Expectancy

FDA Mandate is Not to Maximize Lives Saved

Increasing lifespan is not the congressional mandate of the FDA. Its mandate is to ensure the "safety and efficacy of drugs and medical devices". It has been very successful at fulfilling its mandate.

But lots of people with AIDS and cancer will die while waiting for treatment.

Cognitive Bias Against Acts of Commission

Humans have a cognitive bias towards weighting harmful acts of commission to be worse than acts of omission even if the act of omission causes greater harm. It's seen in the trolley problem where people generally aren't willing to push a fat man in front of a train to save a family even though more lives would be saved.

Medical researcher Dr. Henry I. Miller, MS, MD described his experience working at the FDA, “In the early 1980s,” Miller wrote, “when I headed the team at the FDA that was reviewing the NDA [application] for recombinant human insulin…my supervisor refused to sign off on the approval,” despite ample evidence of the drug’s ability to safely and effectively treat patients. His supervisor rationally concluded that, if there was a death or complication due to the medication, heads would roll at the FDA—including his own. So the personal risk of approving a drug is magnitudes larger than the risk of rejecting it.

It's Impossible to Report on Deaths That Occurred Due to Unavailable Treatments

Here's a news story from the Non-Existent Times by No One Ever without a picture of all the people that die from lack of access to life-saving treatments that might have been.

This means that it's only logical for regulators to reject drug applications by default. The personal risks of approving a drug with any newsworthy side effect far outweigh the personal risk preventing access to life-saving treatment.

Current Regulation Expects Drug Developers to Have Psychic Powers

When running an efficacy trial, the FDA expects that the drug developer has the psychic ability to predict which conditions a treatment will be most effective for in advance of collecting the human trial data. If it was possible to magically determine this without any trials, it would render efficacy trials completely pointless.

In 2007, manufacturer Dendreon submitted powerful evidence attesting to the safety and efficacy of its immunotherapy drug Provenge, which targets prostate cancer. They were able to show that the drug resulted in a significant decline in deaths among its study population, which even persuaded the FDA advisory committee to weigh in on the application. But ultimately, the FDA rejected its application.

The FDA was unmoved by the evidence, simply because Dendreon didn’t properly specify beforehand what its study was trying to measure. Efficacy regulations state that finding a decline in deaths is not enough. The mountains of paperwork must be filled out just so and in the correct order. It took three more years and yet another large trial before the FDA finally approved the life-saving medication.

In addition to the direct costs to companies, the extreme costs and financial risks imposed by efficacy trials have a huge chilling effect on investment in new drugs. If you're an investment adviser, trying to avoid losing your client's retirement savings, you're much better off investing in a more stable company like a bomb manufacturer building products to intentionally kill people than a drug developer trying to save lives. So it's impossible to know all of the treatments that never even got to an efficacy trial stage due to the effects of decreased investment due to the regulatory risks.

What We Don't Know

We’re only 2 lifetimes from the use of the modern scientific method in medicine. Thus it's only been applied for 0.0001% of human history. The more clinical research studies we read, the more we realize we don’t know. Nearly every study ends with the phrase "more research is needed". We know basically nothing at this point compared to what will eventually be known about the human body.

That means we only know 0.000000002% of what is left to be known.

The currently highly restrictive overly cautious method of clinical research prevents us from knowing more faster.

We’re at the very beginning of thousands or millions of years of systematic discovery. So it’s unlikely that this decline in lifespan growth is the result of diminishing returns due to our running out of things to discover.

However, to validate the theory that large-scale real-world evidence can produce better health outcomes requires further validation of this method of experimentation. That's the purpose of CureDAO.

Proof of Concept

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DATA SECURITY POLICY IN BRIEF

CureDAO focuses on security from the ground up. Our Data Center (managed by Amazon Web Services) is SAS 70 Type II certified, SSAE16 (SOC 2) Compliant, and features proximity security badge access and digital security video surveillance. Our server network can only be accessed via SSL VPN with public key authentication or via Two-factor Authentication over SSL. We run monthly Qualys Vulnerability Assessments on our production environment. Additionally, our network can only be accessed via multi-factor authentication, and all access to our web portal is secured over HTTPS using SSL 256-bit encryption. Additionally, all staff members with access to Client Data receive certification as a HIPAA Privacy Associate.

DEFINITION OF TERMS & SYSTEM USERS:

• Client — A customer of CureDAO.

• User — An individual with access to a CureDAO Application.

• Admin — A Client User with the capability of viewing and managing certain aspects of the Client's CureDAO Account.

• Member — A Client User whose account is provisioned through Client’s Web Portal. A Member cannot log in or otherwise access any CureDAO Application directly. All Member Data stored in our system is de-identified in compliance with the HIPAA “Safe Harbor” de-identification standard.

• Developer — A User that can create vendor applications in CureDAO for the purpose of integrating mobile health apps and/or devices.

• CureDAO Admin — A CureDAO employee with access to managing a Client’s account.

DATA CENTER AND HARDWARE

All CureDAO application and database servers are physically managed by Amazon Web Services in secure data centers within the United States. Our security procedures utilize industry best practices from sources including The Center for Internet Security (CIS), Microsoft, Red Hat, and more. All data center facilities are certified SSAE 16 (SOC 2) Compliant and have 24/7 physical security of data centers and Network Operations Center monitoring.

Physical Security

All servers are located in Data Centers managed by Amazon Web Services within the United States. Physical access is controlled both at the perimeter and at building ingress points by professional security staff utilizing video surveillance, intrusion detection systems, and other electronic means. CureDAO employees do not have access to physical server hardware.

Data Access and Server Management Security

CureDAO has IPSec VPN connections to our hosting environment. Only select CureDAO employees are able to access the server network.

Environmental Safeguards

All Amazon Web Services data centers are equipped with automatic fire detection and suppression (either wet-pipe, double-interlocked pre-action, or gaseous sprinkler systems), climate and temperature controls ,fully redundant uninterruptible Power Supplies (UPS), and generators to provide back-up power for each physical site.

DATA STORAGE AND BACKUPS

All Member Data stored in our system is de-identified in compliance with the HIPAA “Safe Harbor” de-identification standard, and all data is encrypted at rest using 256-bit AES. CureDAO production database servers are replicated across multiple availability zones. Database backups use a fully disk-based solution (disk-to-disk) and full system backups are performed daily, weekly, and monthly. Daily backups are retained for a minimum of 7 days, weekly backups are retained for a minimum of 4 weeks, monthly backups are retained for 3 years. Backups are stored in multiple geographic availability zones within Amazon Web Services.

Client Data Policies

Client Data includes data stored by Clients in CureDAO applications, information about a Client’s usage of the application, data instances in the CRM system that we have access to, or data that the Client has supplied to use for support or implementation. Here are the special considerations we take into account when managing Client Data:

1. Client Data is not to be disclosed outside of CureDAO, except to the Client who owns the data or to a Partner who has been contracted by the Client to manage or support their account. Client Data should only be shared using a secure sending method. Approved sending and sharing methods include Dropbox, Google Drive, emailing of encrypted files or use of a Client-provided secure transfer method.

2. Client Data should only be stored temporarily outside of the CureDAO Application if at all. If there is a need to archive Client Data (for example, data provided by a Client during implementation or training), the data should be stored on a central file server and deleted from any personal computers. This includes report exports, contact lists, and presentations that contain Client information, and Client agreements.

3. Client Data should only be accessed on a need-to-know basis. Specifically, a Client’s account should only be accessed to provide support, troubleshoot a problem with that account, or for supporting the system as a whole.

4. Client Data should never be changed except with the explicit permission of the Client, with the exception of repairing data quality issues.

Destruction of Server Data

In order to maintain system integrity, Client Data that has outlived its use is retained up to 60 days before it is destroyed. The data may remain in our backup files for up to 14 months, as it is our policy to maintain weekly backups for a minimum of 52 weeks before those backups are destroyed. De-identified activity data from Members may be stored in perpetuity for future analysis.

Disposal of Computers and Other Data

Old computers and servers used to store or access client information receive a 7-pass erase that meets the U.S. Department of Defense 5220-22 M standard for erasing magnetic media; the devices are then recycled or resold to manufacturers. Paper information in the office is discarded using a document shredder or a commercial secure document shredding service.

Incident Response

CureDAO security administrators will be immediately and automatically notified via email if implemented security protocols detect an incident. All other suspected intrusions, suspicious activity, or system unexplained erratic behavior discovered by administrators, users, or computer security personnel must be reported to a security administrator within 1 hour.

Once an incidence is reported, security administrators will immediately begin verifying that an incident occurred and the nature of the incident with the following goals:

1. Maintain or restore business continuity

2. Reduce the incident impact

3. Determine how the attack was performed or the incident happened

4. Develop a plan to improve security and prevent future attacks or incidents

5. Keep management informed of the situation and prosecute any illegal activity

Determining the Extent of an Incident

Security administrators will use forensic techniques including reviewing system logs, looking for gaps in logs, reviewing intrusion detection logs, interviewing witnesses and the incident victim to determine how the incident was caused. Only authorized personnel will perform interviews or examine the evidence, and the authorized personnel may vary by situation.

Notifying Clients of an Incident

Clients will be notified via email within one hour upon detection of any incident that compromises access to the service, comprises data, or otherwise affects users. Clients will receive a status update every 4 hours and upon incident resolution.

APPLICATION SECURITY

All data transfer and access to CureDAO applications will occur only on Port 443 over an HTTPS encrypted connection with 256-bit SSL encryption.

System Updates and Security Patches

As a hosted solution, we regularly improve our system and update security patches. No client resources are needed to perform these updates. Non-critical system updates will be installed at predetermined times (typically 2:00 a.m. Eastern on Thursdays). Critical application updates are performed ad hoc using rolling deployment to maximize system performance and minimize disruption. All updates and patches will be evaluated in a virtual production environment before implementation.

Vulnerability and Security Testing

CureDAO performs Qualys Vulnerability Assessments and creates external security reports of our production environment once a month. Additional internal security testing is performed on the testing environment before code is checked into a master repository.

User Login and Session Security

All Member logins and sessions are authenticated via a secure OAuth 2.0 access token.

Application Password Management

Admin passwords must have at least 8 characters with at least one number and one letter.

CureDAO Admin passwords must have at least 8 characters with at least one number and one letter and at minimum either one capital letter and/or one special character.

DISASTER RECOVERY

CureDAO maintains real-time data stores mirrored across multiple geographic availability zones in Amazon Web Services within the United States. In a disaster situation, the full CureDAO platform will be recreated and available in a different availability zone within 1hr of the disaster declarations.

HIPAA & PHI COMPLIANCE

In addition to the above HIPAA compliant policies for data storage and handling, the following procedures are in place to ensure HIPAA compliance:

1. All CureDAO employees receive annual HIPAA Business Associate training and certification

2. CureDAO web-based applications receive annual internal HIPAA audits

PHI Handling Policy

All CureDAO staff members are made aware of relevant external regulations as part of their induction process, and all staff who may come into contact with PHI are trained in our PHI handling processes.

CureDAO anonymizes PHI upon receipt and destroys the original except in exceptional circumstances. Where anonymization is not possible (for example for technical reasons or where a product problem can only be recreated using PHI or if the Client specifies the data cannot be anonymized (e.g. if we are investigating a problem on a Client’s workstation), access to the data is restricted and the data is destroyed or returned to the Client as soon as it is no longer needed. Under no circumstances should identified data be added to the company dataset library.

CureDAO expects the professional integrity of our collaborators, clients, and partners providing PHI to us and will assume that they have obtained the data subject’s consent to use their data in this way.

Where a Business Associate Agreement or similar contract relating to PHI is in place, CureDAO staff members work under the terms of that agreement. Where no such agreement exists, the CureDAO PHI handling policy and process are followed.

CureDAO conducts periodic internal audits on compliance with this policy.

tl;dr

Question: How can we have a massive explosion in health data and digital health technology and have no lifespan improvement or cost reduction?

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!

Making Easier to Derive Insights from Health Data

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:

1. The Core Open-Source Platform

• Storage

• Security

• Access Control

• De-identified Data Sharing

• API with Advanced Querying Capabilities

2. The 3rd Party Plugin Framework

• Data import from any source

• Data Format Transformation

• Data visualizations

• Machine learning algorithms

• Data analysis

• Personalized Health Dashboards

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.

Join Us Right Now!

Hey, you! 👀

Our community is open to anyone interested in accelerating precision health and new discoveries. Our success is dependent on the participation of people like you. 🚀

Surely, you've been persuaded by now after seeing all those exciting diagrams?

What?!? You're still reading? Didn't you see that pointy hand icon? That means click it!

Fine. Have it your way.

Then you can read our Lite Paper. But I'm warning you. It's just a string of alphanumeric characters arranged in arbitrary patterns according to rules some British guy randomly came up with a long time ago.

💡 Litepaper

💡 Solution: A Collaborative Framework for Personalized, Precision Health

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 Potential of the Oceans of Real-World Evidence

• Discovering Hidden Causes of Illness - Data mining and analysis to identify hidden factors in our daily life that are making us sicker

• Preventative medicine - Predictive analytics and data analysis of genetic, lifestyle, and social circumstances to prevent disease

• Precision medicine - Leveraging aggregate data to determine the precise treatments and dosages for your unique biology

• Accelerated Treatment Discovery - Data-driven medical and pharmacological research to discover new treatments and medicines

• Reduction of adverse medication events - Harnessing big data to spot medication errors and flag potential adverse reactions

• Cost reduction - Driving better patient outcomes for long-term savings through prevention and avoidance of expensive and ineffective treatments

• Population health - Identify health strategies based on demographic, geographic, and socioeconomic trends

Why Clinical Research Has Failed

The high costs lead to:

1. No Data on Unpatentable 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

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".

Why Digital Health Innovation Has Failed

Duplication of Effort

Despite this massive growth in health data and innovation, we've seen increased costs and disease burden and decreased life expectancy.

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

2. Open-Source Royalty Compensation - By encoding contributions to the project with NFTs, we can provide ongoing royalty payments to open-source contributors.

4. Managed Software-as-a-Service - Digital health companies can save months of development time and tens of thousands of dollars 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.

5. For-Profit Plugins - WordPress, the most widely used web framework globally, is open-source. Businesses earn revenue by creating for-profit plugins. They contribute to improvements of the open-source core WordPress platform because these improvements benefit their business directly.

🏭 Platform

A global open-source platform and plugin framework will enable the transformation of data into clinical discoveries.

Functional Scope

The functional scope of the platform includes:

• aggregation

• managing

• processing

• storage

of health data from different sources.

Primary goal

Create a basic foundational technology layer suitable for any digital health application that provides better interoperability, portability, availability, analysis, and data security.

Use Cases

• EHR Systems for healthcare providers

• User-centered dashboards for personal health management

• Data sharing with doctors, health coaches, or family members

• Decentralized clinical trial platforms

• Patient recruitment services for clinical trials

• Citizen science platforms

• Health data marketplaces

• Open health databases for research

• Algorithm and scores development (e.g., in-silico trials)

• Niche health applications with specific requirements or custom integrations

The platform consists of two primary components:

• Core Open-Source Platform - The core platform is open-source and includes only universally necessary features. This primarily consists of user authentication, data owner access controls, data storage, data validation, and an API for storage and retrieval.

• Plugin Framework - Plugins will provide additional functionality like data import from specific sources, data mapping to various formats, data analysis, data visualization, notifications. These may be free or monetized by their creator.

Core 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 Ownership

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.

Data Compensation

Value stream management allows the exchange of data for tokens.

Plugin Framework

3rd party plugins can interact with the core and provide additional functionality. They may be free or monetized by their creator. These include:

• Data Import Plugins

• Data Visualization Plugins

• Machine Learning Plugins

• Electronic Health Record System Plugins

• Clinical Trial Management Plugins

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:

• Personalized Effectiveness Quantification - Determination of the precise effectiveness of treatments for specific individuals

• Root Cause Analyses - Revelation of hidden factors and root causes of diseases

• Precision Medicine - Determination of the personalized optimal values or dosages based on biomarkers, phenotype, and demographics

• Combinatorial Medicine - Discover relationships between variables or combinations of interventions

• Optimal Daily Values - Determination of the personalized optimal dosages of nutrients or medications

• Cost-Benefit Analysis of interventions by weighing clinical benefit against costs in terms of side effects and financial impact

Example Data Presentation Plugins

❤ Incentivization

We use the DAO structure and NFT IP royalties to reward data sharing and open-source collaboration.

Incentive Alignment

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.

1. Patients

Incentives for Patients to share their de-identified data will include:

1. Actionable ways to prevent and mitigate chronic illnesses.

2. The ability to license and earn a share of income for the use of their data for research and development by pharmaceutical companies and other businesses. NFTs will be linked to the user's cryptographic wallet address. Using a smart contract, the user will receive an ongoing royalty share of the profits for any product developed using their data for research and development.

2. Businesses Housing Data Silos

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:

1. A share of income for using their data for research and development.

2. An on-site instance of the OAuth2 server to retrieve required data from their on-premise databases.

3. Reduction in their team member healthcare costs (one of their most significant expenses)

4. Incentives for Digital Health Businesses

1. Reduced costs of software development.

2. Massive free marketing exposure through company-branded plugins in the Plugin Marketplace.

3. Revenue is derived from their plugins in the Plugin Marketplace.

5. Incentives for Disease Advocacy Non-Profit Organizations

Disease advocacy nonprofits will benefit from promoting studies to their members by:

1. Furtherance of their mission to reduce the incidence of chronic illnesses.

2. Member engagement is more productive than the traditional charity walk.

6. Incentives for Government Health Agencies

1. A reduction in healthcare costs is due to discovering new ways to prevent and mitigate chronic illnesses.

2. Furtherance of their stated reason for existence is to protect and promote the general welfare.

3. Their duty is to protect the rights of individuals' data. To fulfill this, they must require businesses in possession of it to give them the ability to access and share their data via a well-documented OAuth2 API

4. Cost-savings from international cost-sharing by using global open-source software.

5. Epidemiological discoveries on the effectiveness of different public health regulations between nations.

7. Incentives for Contributors to the Development of the Platform

1. Gitcoin Bounties for specific tasks

2. Encoding git commits with NFTs entitles the developer to ongoing royalties in proportion to their contributions.

🏛 Organization

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 experiment 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 execute their area of the overall mission.

Accordingly, the organization is composed of three primary components.

1. Citizen Scientists - CureDAO is an open and permissionless organization. Anyone has the right to earn their Citizenship by contributing labor or resources. In exchange, the Citizen Scientist will receive CureDAO tokens granting full governance rights over the actions of DAO Lab Staff.

2. DAO Laboratories - Internal working groups that carry out the Citizen Scientists' will.

3. External Service Providers - Individuals or entities outside the DAO deemed necessary to carry out the Citizen Scientists' will.

😷 Privacy

To protect privacy, CureDAO will use deidentification and obfuscated but equivalent data synthetically derived from actual patient data.

De-identification Methods

Data de-identification is the process of eliminating Personally Identifiable Data (PII) from any document or other media, including an individual's Protected Health Information (PHI). The HIPAA Safe Harbor Method is a precise standard for de-identifying personal health information when disclosed for secondary purposes.

Data De-identification Tools

1. ARX Data Anonymization Tool

2. deid software package

3. Synthetic Patient Generation

Read The Full Whitepaper

6. Legal Framework

1. The Problem: You and Everyone You Love Will Suffer and Die

Will throwing more money at the existing healthcare system save us?

Despite this additional spending, life expectancy in the US has actually been declining over that time.

Will digital health innovation save us?

There has been an explosion of recent technological advances in digital health, including:

• genetic sequencing

• gut microbiome sequencing

This data exists in the form of:

• Electronic Medical Records

• Genetic Sequencing

• Data from Fitness and Sleep trackers

• Data from diet and treatment tracking apps

• Health insurance claims

• Grocery, pharmacy, and nutritional supplement receipts and purchases

• Clinical trial results

The digital health revolution started over a decade ago. It was promised to improve human health and reduce costs. Yet, all we've seen is increasing costs, increasing disease burden, and decreasing life expectancy.

Why haven't we seen a reduction in disease burden?

So, this explosion in technology, data, and spending has produced no measurable improvement in human health. The reason, in a single word, is incentives. The current economic system punishes every stakeholder in the ecosystem for doing the things that would lead to progress.

1.2 Problems in Clinical Research

1.2.1 The Cost of Clinical Research

• The process takes over 10 years.

This high cost leads to the following problems:

No Data on Unpatentable Molecules

Lack of Incentive to Discover the Full Range of Applications for Off-Patent Treatments

No Long-Term Outcome Data

Even if there is a financial incentive to research a new drug, there is no data on the long-term outcomes of the drug. The data collection period for participants can be as short as several months. Under the current system, it's not financially feasible to collect data on a participant for years or decades. So we have no idea if the long-term effects of a drug are worse than the initial benefits.

1.2.2 Conflicts of Interest

The economic survival of the pharmaceutical company is dependent on the positive outcome of the trial. While there's not a lot of evidence to support that there's any illegal manipulation of results, it leads to two problems:

Negative Results are Never Published

This leads to a massive waste of money by other companies repeating the same research and going down the same dead-end streets that could have been avoided.

1.2.3 Trials Often Aren't Representative of Real Patients

External validity is the extent to which the results can be generalized to a population of interest. The population of interest is usually defined as the people the intervention is intended to help.

As a result, the results of these trials are not necessarily generalizable to patients matching any of these criteria:

• Suffer from multiple mental health conditions (e.g. post-traumatic stress disorder, generalized anxiety disorder, bipolar disorder, etc.)

• Engage in drug or alcohol abuse

• Suffer from mild depression (Hamilton Rating Scale for Depression (HAM-D) score below the specified minimum)

• Use other psychotropic medications

These facts call into question the external validity of standard efficacy trials.

Furthermore, patient sample sizes are very small. The number of subjects per trial on average:

In the example in graphic above a drug is prescribed to millions of patients based on a study with only 36 subjects, where a representation of the general public is questionable.

Solution: Collect Data on Actual Patients

In the real world, no patient can be excluded. Even people with a history of drug or alcohol abuse, people on multiple medications, and people with multiple conditions must be treated. Only through the crowdsourcing of this research, would physicians have access to the true effectiveness rates and risks for their real-world patients.

The results of crowd-sourced studies would exhibit complete and utter external validity since the test subjects are identical to the population of interest.

Furthermore, self-trackers represent a massive pool of potential subjects dwarfing any traditional trial cohort. Diet tracking is the most arduous form of self-tracking. Yet, just one of the many available diet tracking apps, MyFitnessPal, has 30 million users.

1.3 Problems in Digital Health Innovation

1.2.1 $157 Billion Wasted on Duplication of Effort

If this code was freely shared, everyone could build on what everyone else had done. Theoretically, 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. This increases their likelihood of bankruptcy even higher than the 90% failure rate they already faced.

How DAOs Overcomes the Free-Rider Problem

2. By encoding contributions to the project with NFTs, we can guarantee ongoing compensation in the form of royalties.

1.2.2 Isolated Data Silos

The best that isolated data on individual aspects of human health can do is tell us about the past. For example, dashboards telling us how many steps we got or how much sleep we got are known as “descriptive statistics”. However, by integrating all available data from individuals, similar populations, as well as existing clinical research findings and applying machine learning we may achieve “prescriptive” real-time decision support.

To facilitate data sharing, the CureDAO will provide data providers with an onsite easily provisionable OAuth2 API server that will allow individuals to anonymously share their data with the global biobank.

Incentive Alignment

Fully realizing the potential of the personalized preventative medicine of the future will require incentivizing cooperation between the following stakeholders:

This illustrates the flow of value between different stakeholders. As opposed to traditional zero-sum games, CureDAO provides a way for each self-interested party to derive more value from participation than they have to put into it.

1. Patients

Incentives for Patients to share their de-identified data will include:

1. Actionable ways to prevent and mitigate chronic illnesses.

2. The ability to license and earn a share of income for use of their data for research and development by pharmaceutical companies and other businesses. This will be achieved by encoding the user data using non-fungible tokens (NFTs) and issuing them to the user. The NFTs will be linked to the user's cryptographic wallet address. Using a smart contract, the user will receive an ongoing royalty share of the profits for any product that was developed using their data for research and development.

2. Businesses Housing Data Silos

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:

1. A share of income for use of their data for research and development.

2. An on-site instance of the OAuth2 server to retrieve required data from their on-premise databases.

3. An eventual reduction in their employee healthcare costs (one of their largest expenses) by resulting from the discovery of new ways to prevent and mitigate chronic illnesses.

3. Digital Health Businesses

On top of the incentives for businesses listed above, the following incentives will be provided to digital health businesses which enable data sharing:

1. A license to use a white-labeled version of the framework. This will dramatically reduce the costs of software development. These reduced costs will allow them to focus on innovating their unique value proposition, making them more competitive in the market.

2. Massive free marketing exposure through company branded plugins in the Plugin Marketplace.

3. Revenue derived through subscription or licensing agreements for usage of their plugins in the Plugin Marketplace.

4. Disease Advocacy Non-Profit Organizations

Disease advocacy non-profits will be incentivized to promote observational studies through the anonymous donation data by their members by:

1. Accelerated furtherance of their mission to reduce the incidence of chronic illnesses.

2. A new method of member engagement more motivating and productive than the traditional charity walk.

5. Government Health Agencies

Governments will be incentivized by:

1. A reduction in government healthcare costs due to the discovery of new ways to prevent and mitigate chronic illnesses.

2. Furtherance of their stated reason for existence to protect and promote the general welfare. General welfare is defined as the overall health and happiness of the population.

3. Their duty to protect the rights of individuals' data. To fulfill this, they must require businesses in possession of it to give them the ability to access and share their data via a well-documented OAuth2 API

4. Cost-savings from using open-source software. All publicly funded digital-health software projects should be free, secure, and open-source. Currently, the majority of government contracts go to closed-source and proprietary software. This leads to massive waste as governments around the world are paying to reinvent the wheel instead of sharing the costs. Shockingly, there is even a great deal of wasted money on duplicated software contracts between different agencies within the same governments.

5. Require international cooperation for all public health efforts to reduce wasted duplication of effort and take advantage of natural experiments resulting from differing public health regulations between nations.

6. Epidemiological discoveries from allowing citizens to anonymously share their data in a global database. This will enable us to take advantage of natural experiments resulting from differing public health regulations between nations. For instance, 27 countries have banned the use of the pesticide glyphosate due to concerns about the health effects. If no overall change in the health of the populations is observed, it will suggest that the health concerns may be unfounded.

6. Contributors to the Development of the Platform

Citizens of the DAO will be incentivized to contribute to the development of the platform by:

1. Gitcoin Bounties for specific tasks

2. Encoding git commits with NFTs entitling the developer to ongoing royalties in proportion to their contributions.

7. Compensation

CureDAO is a laboratory consisting of many experiments.

It’s a global laboratory where the 7 billion human “natural experiments” revealing the effects of various factors on human health and happiness are conducted.

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 be constantly experimenting with new ways to execute this mission. We recognize the importance of using real-world evidence in the mission of improving human health. Execution within the working groups should take the same data-driven approach to execute their area of the overall mission.

Accordingly, the organization is composed of three primary components

1. Citizen Scientists - DAO data donors or token holders with voting rights

2. DAO Laboratories - Working groups consisting of a Lab Manager who helps Lab Technicians carry out the duties of their Laboratory in accordance with the will of the Citizen Scientists.

3. External Service Providers - Individuals or entities outside the DAO deemed necessary to carry out the will of the Citizen Scientists.

Citizen Scientists

As an open and permissionless organization, anyone has the right to earn their Citizenship through the contribution of labor or resources. In exchange, the Citizen Scientist will receive CureDAO tokens granting full governance rights over the actions of DAO Lab Staff.

Citizens may participate in:

1. Governance Debate on Discourse

2. Token-Based Voting by staking their Governance Tokens on smart contracts

Lab Managers and Technicians

Lab Staff comprises the Laboratory working groups who carry out the will of the DAO. Citizens can apply to join Laboratories based on their experience or expertise. Laboratories may elect Lab Managers, who are responsible for coordinating between Laboratory Technicians. Laboratories may decide to create incentives for their Citizen Scientists in a variety of forms, including paying them for services or creating bounties.

In cases where Lab Staff are paid, Citizens may choose to compensate them with any of the following:

1. DAO Governance Tokens

2. Ethereum

3. Fiat Currency

4. Other Incentives

Initial Laboratories

The initial Laboratories will be created to carry out the following primary functions:

• Governance Lab - Changes governance and how proposals are created and deployed. Handles technical aspects of DAO token creation and distribution. Develops the DAO’s smart contracts.

• Legal Lab - Handles legal matters regarding business structure, health data, liability issues, and business contracts.

• Coordination Lab - Handles operational matters such as human resources, compensation, project management, onboarding. Provides resources for Lab Staff.

• Community Lab - Promotes community engagement with DAO and the broader world.

• UI/UX Lab - Creates a user-friendly interface for the platform front end.

• Collaborations - Facilitates financing of DAO projects. Coordinates partnerships between individuals and organizations.

• Dev Lab - Implements the platform back end and user interface

• Public Relations Lab - Promotes DAO’s presence in the public discourse.

• Data Lab - Integrates data from various sources and formats. Conducts research on data science and machine learning.

Service Providers

Service providers provide services to CureDAO, such as:

• development work

• IP sourcing and conversion to NFTs

• marketplace services

• public relations

• legal services

• data science

• customer support

• marketing

CureDAO will contract service providers and pay for their services with any of the following:

1. DAO Governance Tokens

2. Ethereum

3. Fiat Currency

4. Other Incentives

Next Tokenomics 👉

Privacy

The Health Insurance Portability and Accountability Act of 1996 (“HIPAA”) protects the privacy of patients and sets forth guidelines on how this private health information can be shared. Though the privacy of a patient must be protected, the legal right of a business to sell health information of patients has been upheld by the Supreme Court of the United States.

De-identification Methods

Data de-identification is the process of eliminating Personally Identifiable Data (PII) from any document or other media, including an individual’s Protected Health Information (PHI).

Safe Harbor Method of De-identification

The HIPAA Safe Harbor Method is a precise standard for the de-identification of personal health information when disclosed for secondary purposes. It requires the removal of 18 identifiers from a dataset:

1. Names

2. All geographical subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census:

   1. The geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people and

   2. The initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000.

3. All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older

4. Phone numbers

5. Fax numbers

6. Electronic mail addresses

7. Social Security numbers

8. Medical record numbers

9. Health plan beneficiary numbers

10. Account numbers

11. Certificate/license numbers

12. Vehicle identifiers and serial numbers, including license plate numbers

13. Device identifiers and serial numbers

14. Web Universal Resource Locators (URLs)

15. Internet Protocol (IP) address numbers

16. Biometric identifiers, including finger and voice-prints

17. Full face photographic images and any comparable images and

18. Any other unique identifying number, characteristic, or code (note this does not mean the unique code assigned by the investigator to code the data).

Data De-identification Tools

1. ARX Data Anonymization Tool

2. deid software package

3. Synthetic Patient Generation

References

3. The Platform

Functional Scope

The functional scope of the platform includes:

• aggregation

• managing

• processing

• storage

of health data from different sources.

Primary goal

Create a basic foundational technology layer suitable for any digital health application providing better interoperability, portability, availability, analysis, security of the data.

Use Cases

• EHR Systems for healthcare providers

• User-centered dashboards for personal health management

• Data sharing with doctors, health coaches, or family members

• Decentralized clinical trial platforms (e.g. BYOD wearable)

• Patient recruitment services for clinical trials

• Citizen science platforms

• Health data marketplaces

• Open health databases for research

• Algorithm and scores development (e.g. in-silico trials)

• Niche health applications with specific requirements or custom integrations

The platform consists of two primary components:

• Core Open-Source Platform - The core platform is open-source and includes only universally necessary features. This primarily consists of user authentication, data owner access controls, data storage, data validation, and an API for storage and retrieval. The DAO will compensate contributors to the core platform.

• Plugin Framework - Plugins are modules that provide additional functionality. This includes data import from specific sources, data mapping to various formats, data analysis, data visualization, notifications. These may be free or monetized by their creator or even be integrated into the core based on community voting.

3.1 Core Components

3.1.1 Data Ingestion API

In theory, any kind of human-generated data which can be ingested and used for deriving health insights should be defined as health data and be made accessible for further analysis.

The challenge is to acquire, extract, transform, and normalize the countless unstandardized data export file formats and data structures and load them into a standardized structure that can be easily analyzed.

Proposed is the development of an application programming interface (API) and OpenAPI specification for receiving and sharing data with the core database. Software development kits (SDK’s) made available for 3rd party applications allow the interaction with the API. SDK’s will enable developers to implement easy automatic sharing options in their applications.

Separate plugins will enable spreadsheet upload/import and scheduled imports from existing third-party APIs. The API connector framework will allow the ongoing regular import of user data after a single user authorization.

Data Sources

• Laboratory and Home tests (Standard Blood panels, Metabolomics, Proteomics, Genetics, Urinalysis, Toxins, etc)

• Wearable (Sleep and Fitness trackers, etc)

• Health apps (Meal tracking, Fertility, etc)

• User reported symptoms and intervention application

• Electronic Heath Records

• Imaging

• Questionnaires

• Functional tests

• Environmental and context data (Twosome)

• Life events, Calendar, Social media, and Lifestyle

• Digital biomarkers

• Location

Data Formats

• FHIR

• openEHR

• LOINC

• SNOMED

• RXNORM

• MedDRA

• ICD-10

• Open mHealth

EHR Policies

EHR Technical Standards

Implementation Support for EHR APIs

3.1.2 Raw Data and Files Storage

To preserve originality in case of data processing errors or protocol changes the ingested raw files like CSV files, PDF reports, and the raw API responses are stored separately in a binary data and file storage system. Data will be encrypted and stored in its raw format in flat files on a secure cloud provider defined in the framework instance platform settings. Preservation of the data in its original format will allow for:

1. Asynchronous Queued Data Parsing Jobs - This is necessary to allow for the data to be parsed in parallel offline and avoid overloading the observer.

2. Storage of data incompatible with a time-series relational data store.

3. Storage of data formats that do not yet have defined parser plugins. This will allow for the data to be imported at a later date when the data mapper has been defined.

4. Updating parsers to support changes in the response format for a particular API.

The original raw data and files can be accessed at any time by the owner, independently from any other process involved with the structured data storage.

3.1.3 Data Mapping

To make the standardized structured storage of health data and the envisioned queries possible, the data has to be ingested from files or API requests and mapped from many different standards and proprietary formats into a standard schema. These will be executed in an asynchronous queue to map the raw data to a standardized format and provide it to the validator. The most common data mappers will be integrated into the core. Less common data mappers will be available as plugins from 3rd party developers.

Core data mappers (Initial proposal):

• FHIR

• LOINC

• SNOMED

• RXNORM

• OpenEHR

The database schema will be consistent with existing formats while addressing some gaps of missing definitions. The goal of a simplified, universal standard format is to make multi-omics data as well as environmental, social, digital biomarkers easily analyzable.

3.1.4. Data Validation

In order to ensure a level of quality required by healthcare and clinical trials, data quality and consistency must be ensured. The data validation middleware will validate the data before it is stored in the time-series database.

Validation involves the following steps:

• Ensure values are within allowed ranges for a variable and unit

• Outlier detection

• Strict data type checking for all properties

Invalid Data Handling

• Data deviating from allowed value ranges will be flagged for review and deletion or correction

• Outliers will be be flagged

• Invalid data types will be ignored and the data ingestion plugin developer will be notified

• Duplicate data for a given timestamp will be ignored and the data ingestion plugin developer will be notified

3.1.5. Reference Data Definitions

Mapping data from different formats into one standardized format suitable for a measurements analysis requires a reference database with tables of definitions and descriptions to be used by the data mappers and by the API for displaying this information in applications. The type of data to be defined includes biomarkers, health-related variables of any kind, interventions, therapies, outcomes, conditions, etc.

Examples of currently used existing reference databases include LOINC, RXNORM, ICD-10 but are partly not suitable enough and have to be unified for the scope of more efficient data handling and analysis. Especially definitions for environmental factors, natural supplementation or therapies, digital biomarkers, and social and lifestyle data sources have a lack of integration.

The proposed solution for overcoming challenges with interoperating with data formats like FHIR is a single measurements table with all definitions query-able by beforehand mentioned categories and types. The main reason for this solution is the complexity of the nature of the definition of a health-related measurement.

Often a variable can be either be both an input factor or a health outcome with respect to the black-box system human body. Thus, whether or not a variable is a controllable input factor and/or a health outcome is stored in the variable settings table. All measured values are thrown in one "pool" and can be retrieved in a flat universal format without having to worry about transforming complex nested data structures for compatibility.

Units of Measurement

3.1.6. Time Series Data Storage

After validation and mapping, the data will be stored in a standardized and structured time-series database.

Functional Requirements:

• Large scale data storage for time-series data

• Standardized format

• Safety and Security

3.1.7. Data Ownership

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. The data owner shall have the unrestricted ability to manage their digital health identity.

Ownership management functionalities will allow the individual to manage their data and access control settings for sharing purposes. It will allow them to:

• View and Access their data

• View the OAuth clients with access to the data

• Modify read/write permissions for specific OAuth clients

• Restrict data access to specific users, groups, researchers, or applications

• Restrict data access to specific data categories, types, and markers

• Restrict time and expiration of data access

• Configure security measures such as encryption or 2-factor authentication

• Overview of statistics of data (amount, averages, sources, etc..)

• Export stored data or the original files

• Delete data

This feature can be used by user-centered applications and dashboards for personal health management, for data sharing with care providers, research, or for participation in trials.

3.1.8. Data Compensation

Health data is a sensitive and valuable commodity.Therefore the handling of the data alongside its attached value is proposed to be built natively into the core. Value stream management functionalities will allow the exchange from data against tokenized value assets in different scenarios. It will allow:

• Individuals to share data and receive defined compensation

• Groups create and attach insights from grouped data sets to values and exchange to buyers against value assets

• Researchers apply, formulate and visualize values of data sets

• Connect data to value in general for administration purposes

• Applications to create a value-based feedback loop for research or behavioral outcomes

Data Value Scenarios:

• Raw data sets or streams of individuals

• Cohort raw data sets of grouped individuals

• Interpreted data, scores, and recommendations

• Generated insights and IP out of data analysis

• Specifically aggregated data according to requested needs from buyers

• Phenotypic, demographic, lifestyle, conditions, environmental context

This feature can be used for exchanging data on marketplace applications or clinical trial platforms.

3.2 Plugins

Defined interfaces will allow 3rd party development of software modules that interact with the core and provide additional functionality. They may be free or monetized by their creator.

Plugins will be stored in their own repositories based on a plugin template repository. The plugin template repository will contain defined interfaces required for interoperability with the core.

3.2.1 Data Analysis Plugins

The impact of effective and detailed analysis is

• The discovery of root causes of disease

• Development of new interventions

• The precise and personalized application of these interventions

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:

• Personalized Effectiveness Quantification - Determination of the precise effectiveness of treatments for specific individuals

• Root Cause Analyses - Revelation of hidden factors and root causes of diseases

• Precision Medicine - Determination of the personalized optimal values or dosages based on biomarkers, phenotype, and demographics

• Combinatorial Medicine - Discover relationships between variables or combinations of interventions

• Effect Size Quantification - Quantification of effect sizes of all factors on symptom severity

• Optimal Daily Values - Determination of the personalized optimal dosages of nutrients or medications

• Cost-Benefit Analysis - Determination of the most cost-effective interventions by weight clinical benefit against costs in terms of side effects and financial impact

This will mitigate the incidence of chronic illnesses by informing the user of symptom triggers, such as dietary sensitivities, to be avoided. This will also assist patients and clinicians in assessing the effectiveness of treatments despite the hundreds of uncontrollable variables in any prescriptive experiment.

Large cohort clinical analysis could reveal new molecules for longevity.

3.2.2 Data Presentation and Management Plugins

Data visualization plugins convert data from its raw form into useful insights. They may be used to display data from individual or multiple subjects. Some regular ways to visualize data are scatter plots, timeline charts, heatmaps, or novel ways like the in the following proposed outcome labels. Visualizations can be embedded in studies, publications, or personal dashboards.

Tasks of data visualization plugins:

• Query the database according to filters and sorting commands

• Handle the processing of data processing functions like statistical analysis

Example Data Presentation Plugins

3.2.3 Application Programming Interface (API) Connector Plugins

Many applications and service providers offer a direct exchange of structured health data through an API, which upon user authentication allow access to automated and scheduled exports of the generated data.

So far the proprietary silo developments have produced many different data formats, which could be replaced with the data standard proposed within this project. Until the success of a common language for all types of health data and between all stakeholders, many API connecting plugins are necessary for this interoperability.

An API connector plugin handles:

• User interface and tokens for authentication and authorization with the 3rd party applications

• Automation and the periodic fetching of health data

• Mapping to the standard specification

• Providing the responses to the origin raw storage module

• error handling

• communication with the user

Connector Technical Flow

API Connector plugins will be called by the webserver to:

1. Handle the OAuth2 authorization flow and store their credentials in the relational database

2. Provide the original raw response to the core platform for encryption and storage

A job scheduler will call the API connectors periodically (usually daily) to:

1. Refresh the user's OAuth access token

2. Fetch new data or data that has been modified since the last import

3. Map the response to the standard format as defined by the OpenAPI specification for the framework API

4. Provide the processed data to the framework's validation middleware.

5. All valid data will be stored in the relational database.

6. Invalid data will be rejected and the plugin developer and data owner will be notified.

3.2.4 File Importers

File importing plugins are needed for specific sources or devices, where APIs are not available and the user only has access to raw files. Types of files include spreadsheets, PDFs, and raw genomic data.

1. The file passed by an upload action to the data importer plugin user interface on the frontend application

2. The core framework will encrypt and store the raw file

3. The file ID and importer plugin ID will be added to a queue for processing by the job scheduler

The background job scheduler will:

1. Retrieve the file from the encrypted storage

2. Pass the file to the matching file importer plugin

3. The importer plugin will extract the data from the file

4. The importer plugin will map to the standard format as defined by the framework OpenAPI specification

5. The processed data will be provided to the framework's validation middleware.

6. Valid data will be stored in the relational database.

7. Invalid data from the importer plugin will be rejected and the plugin developer and data owner will be notified.

A link between the created structured data and the original file allows backup and reprocessing (e.g. if the data import plugin functionality is expanded in future versions).

Challenges include changing proprietary formats, spreadsheet column matching, long upload times with raw files like from genomic testing.

API Specifications

DAO Lists

Decentralized Clinical Research

OpenCures

Blockchain in Healthcare

VitaDAO

George Health

BLOCKPHARMA

Substandard and fake products are one of the challenges confronting the health sector globally. Substandard and fake drugs affect every region of the world but are more predominant in low and middle-income countries. The use of substandard and fake drugs contributes to drug-resistant infections, and antimicrobial resistance, ultimately causing over 800,000 deaths annually.

DENTACOIN

It also promotes the patients’ long-term oral health instead of short-term care that requires frequent subsequent visits. It is a subscription-based platform that connects patients to dentists and pays the dentists for treating the patients. It also rewards the patient by giving them incentives in the form of ‘Dental coins’ to take care of their teeth and eat healthily. It also promotes patients’ oral health in the long run through its oral health apps and games.

BLOCKPILL

As the global life expectancy continues to improve, the elderly population will continue to increase. Along with this increase, there will be a rise in the number of people taking several medications for various chronic conditions. In that case, the risk of drug-to-drug interactions is also bound to increase, and that is where BlockPill steps in.

BlockPill aims to eliminate this challenge by providing a solution that ensures safer prescriptions and facilitates communication between different patient management team Citizens. This will reduce the incidence of unwanted drug interactions and allow the modification of medication with ease.

ENCRYPGEN

They are doing this in a way that allows the owners of the DNA to profit from the use of their DNA for research, especially research that can lead to the discovery of treatments for genetic diseases.

Encrypgen pays the providers of DNA (which could be anyone) for their contributions, de-identifies these DNA samples (removing all identifying information), and stores them in a private Blockchain network (to ensure their security). Researchers can purchase these samples from the company and use them for their studies.

All transactions are carried out using ‘DNA coins’. This process is expected to enhance DNA research and reward the donors of the DNA samples too.

POKITDOK

The increase in AI-powered medical devices that accompanied the internet-of-things revolution led to the fragmentation of medical records. Electronic medical records were no longer the sole preserve of healthcare providers. Software companies, smart devices, and cell phones became custodians of significant amounts of patients’ health data too.

Pokitdok uses blockchain technology to aggregate these data for patients who subscribe to their services through its Dokchain service. The company also secures these data ensuring double verification for anyone to access any patient’s data.

This data can be made available to healthcare providers, insurance companies, or any third-party companies the owners allow.

CLINICOIN

Clinicoin is a blockchain-based wellness and fitness community. It seeks its subscribers’ health and connects them to providers while rewarding them for participating in healthy activities. Clinicoin promotes the prevention of diseases by encouraging participation in physical activities and mindfulness exercises.

This participation is rewarded by tokens offered to users when healthy activities are logged on the Clinicoin app and verified via third-party tracking applications connected to the company’s app. It also provides an opportunity to reward developers whose health tracking applications are connected to its app.

The data gathered can be easily collected by healthcare providers or researchers. Users can also be encouraged to meet health or research goals by offering them tokens.

IRYO

Afterward, patients can choose who (providers, researchers, or other third-party agents) can access their records, granting them partially or full access. Patients are also rewarded in the process as these providers have to pay them ‘IRYO tokens’. Patients can also use these tokens to pay for the services that the providers have rendered.

SOLVE.CARE

Healthcare is expensive everywhere. And especially expensive in America compared to other high-income countries, one of the reasons for this is the high costs of health administration. For instance, research shows that an average of $2,497 per person was expended in administrative costs in the USA in 2017, while an average of $551 per person was expended in Canada in the same year. Solve.Care aims to solve this problem using blockchain technology to drive down administrative costs in healthcare.

It uses multiple tools including ‘Care.Coins’, ‘Care.Protocol’, ‘Care.Cards’, ‘Care.Wallet’, ‘Care.Community’ and ‘Care.Marketplace’ to achieve its purposes. Although Solve.Care was developed for the American healthcare system.

It is also useful for other countries health systems. ‘Care.Coins’ can handle complex healthcare payments, simplifying them and eliminating the need for complex third-party transactions.

Despite its rather simplified process, ‘Care. Coins’ ensure payments are accurate and timely. ‘Care.Protocol’ synchronizes all other Solve.Care platforms: wallet, card, and coins to ensure their smooth integration. ‘Care.Cards’ are used to make payment into the ‘Care.Wallet’. They also ensure smooth integration of different cards from other wallets to ensure its versatility.

DOC.AI

Scientists can then use this data for predictive modelling and other types of research. The company has partnered with other companies to use artificial intelligence to predict the incidence of allergic reactions. Doc.ai uses blockchain encryption to guarantee medical data safety and security.

Healthcare Big Data

BC Platforms

Digital Health

• GenomeDAO

• CrowdfundingCures

• LabDAO

• Nectar

• Doc.ai

• BC Platforms

• Basis Health

• Precision Health Club

• OpenHumans

• OpenSci Foundation

• Citizen Health

References

Sources

29. 45 CFR § 164.514 (b)(1)(i); As set forth in the HIPAA Privacy Rule

30. https://www.bloomberg.com/graphics/infographics/reidentifying-anonymous-medical-records.html

31. Na L, Yang C, Lo C, Zhao F, Fukuoka Y, Aswani A. Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning; [https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2719130

32. https://www.nytimes.com/2018/09/20/health/memorial-sloan-kettering-cancer-paige-ai.html

Health Data Brokers

Digital Health Companies

ETL Companies

DAOs

These automatically generated reports are intended to help you and your physician to gain insight into the root causes and effective solutions to help you minimize your symptoms.

• PDF Example

• HTML Example

Optimitron is an AI assistant that asks you about your symptoms and potential factors. Then she applies pharmacokinetic predictive analysis to inform you of the most important things you can do to minimize symptom severity.

A half-billion are suffering from autoimmune diseases like Crone's disease, psoriasis, and Fibromyalgia. There’s also a great deal of mounting evidence that treatment-resistant depression and other mental illnesses are strongly related to immune dysregulation. What all these diseases have in common is that they can be exacerbated or improved by hundreds of factors in daily life.

Probably the most significant is the 3 pounds of thousands of chemicals that we consume every day through our diets. Since the thousands of chemicals in our food have been GRAS or “Generally recognized as safe” by the FDA, there’s no incentive to do research.

We've built a connector framework that imports data on diet, physical activity, sleep, social interaction, environmental factors, symptom severity, vital signs, and others.

Then we apply pharmacokinetic predictive analysis that accounts for onset delays and durations of action. This enables her to obtain personalized effectiveness values (similar to recommended daily values) for treatments and reveal potential root causes of chronic conditions.

The accuracy of the results obtained from quasi-experimental techniques and observational data is highly dependent on the quantity and quality of the data. To maximize the amount of available data, we're currently in the process of creating a decentralized autonomous organization called CureDAO. Its mission is to create an open-source platform for crowd-sourced clinical research. It incentivizes collaboration and data sharing by competing entities by issuing non-fungible tokens to any contributor of intellectual property or data.

Contributors of data and intellectual property will receive ongoing royalties for their contributions linked to their non-fungible tokens. We are currently meeting with investors and recruiting members. We plan to hold a fair and public auction for CureDAO tokens in May 2022.

References

Data collection can be done using wearable sensors, third-party applications, and client applications.

Data Types

Ultimately, the system should be able to handle the following data types:

• Omics data (e.g., genomics, proteomics, metabolomics, etc.)

• Image and physiological data (e.g., CT, PET/SPECT, sMRI, fMRI, rMRI, DTI, EEG, MEG, ultrasound, cellular level imaging, multi-electrode recording, etc.)

• Clinical data (e.g., lab tests, pathology, imaging, diagnosis, electronic health records, etc.)

• Multiscale data (genomic, epigenomic, subcellular, cellular, network, organ, systems, organism, population levels)

• Multiplatform data (desktop, cloud-based storage, etc.)

• Data from multiple research areas and diseases (e.g., common inflammatory pathways in cancer, obesity, immune diseases, and neurodegenerative diseases)

• Data with special considerations (e.g., sparse data, heterogeneous data, very large or very small datasets)

• Human-computer interfaces and visualization

Automated Data Acquisition

At the present time, it requires a great deal of effort and diligence on the part of the self-tracker to gather all of the data required to identify the triggers of mental illness and quantify the effectiveness of different treatments. Tracking one’s mood, diet, sleep, activity, and medication intake can be extremely time-consuming. The present invention automatically pulls data from a number of data sources (adding more all the time).

The data sources would include:

• Biometric Devices: that could measure vital signs and biomarkers

• Purchase Records: Data regarding consumption of foods and supplements could be automatically collected by and inferred from receipts or other financial aggregation services like Mint.com.

• Auditory Records: Voice recognition may be used to quantify emotion through conscious verbal expression, spectral analysis of the magnitudes of different frequencies of speech would probably be a better means of quantifying unconscious human affect and thus providing more accurate data for the machine learning process. CommonSense is a cloud-based platform for sensor data.

• Visual Affect Data via Web-Cameras: By tracking hundreds of points on the subjects’ faces, InSight can accurately capture emotional states.

• Prescription Records: Microsoft HealthVault can automatically collect lab results, prescription history, and visit records from a growing list of labs, pharmacies, hospitals, and clinics.

Currently, all foods carry nutrition labels such as this one:

But how useful is it to the average person to know the amount of Riboflavin in something? The purpose of nutritional labels is to help individuals make choices that will improve their health and prevent disease.

Telling the average person the amount of riboflavin in something isn’t going to achieve this. This is evidenced by the fact that these labels have existed for decades and during this time, we’ve only seen increases in most diseases they were intended to reduce.

Data Quantity

We've collected over 10 million data points on symptom severity and influencing factors from over 10,000 people. Predictive machine learning algorithms are applied to reveal effectiveness and side-effects of treatments and the degree to which hidden dietary and environmental improve or exacerbate chronic illnesses.

These analytical results have been used to publish 90,000 studies on the effects of various treatments and food ingredients on symptom severity.

Although 10 million data points sound like a lot, currently, the usefulness and accuracy of these Outcome Labels are currently limited. This is due to the fact there are only a few study participants have donated data for a particular food paired with a particular symptom. In observational research such as this, a very large number of participants are required to cancel out all the errors and coincidences that can influence the data for a single individual.

For instance, someone with depression may have started taking an antidepressant at the same time they started seeing a therapist. Then, if their depression improves, it’s impossible to know if the improvement was a result of the antidepressant, the therapist, both, or something else. These random factors are known as confounding variables. However, random confounding factors can cancel each other out when looking at large data sets. This is why it’s important to collect as much data as possible.

Data Sources

Several types of data are used to derive the Outcome Labels:

2. Macro-Level Epidemiological Data – This includes the incidence of various diseases over time combined with data on the amounts of different drugs or food additives. This is how it was initially discovered that smoking caused lung cancer. With macro-level data, it’s even harder to distinguish correlation from causation. However, different countries often enact different policies that can serve as very useful natural experiments. For instance, 30 countries have banned the use of glyphosate. If the rates of Alzheimer’s, autism, and depression declined in these countries and did not decline in the countries still using glyphosate, this would provide very powerful evidence regarding its effects. Unfortunately, there is no global database that currently provides easy access to the incidence of these conditions in various countries over time and the levels of exposure to various chemicals.

3. Clinical Trial Data – This is the gold standard with regard to the level of confidence that a factor is truly the cause of an outcome. However, it’s also the most expensive to collect. As a result, clinical trials are often very small (less than 50 people). Exclusion criteria in trials often prevent study participants from being representative of real patients. There are ethical considerations that prevent us from running trials that have any risk of harm to participants. Due to the expense involved we have very few trials run on anything other than a molecule that can be patented and sold as a drug.

Mega Studies

Mega studies can be focused on either:

• an outcome of interest such as the severity of a disease or

• a predictor such as a food or a drug

Outcome Mega Studies

Outcome Mega Studies summarize all available analyses to determine the factors most likely to improve or exacerbate a symptom of chronic illness.

Predictor Mega Studies

Predictor Mega Studies summarize all available analyses to determine the most likely positive or negative effects of a given factor such as a food or a drug.

Data Sources

2. Macro-Level Epidemiological Data – This includes the incidence of various diseases over time combined with data on the amounts of different drugs or food additives. This is how it was initially discovered that smoking caused lung cancer. With macro-level data, it’s even harder to distinguish correlation from causation. However, different countries often enact different policies that can serve as very useful natural experiments. For instance, 30 countries have banned the use of glyphosate. If the rates of Alzheimer’s, autism, and depression declined in these countries and did not decline in the countries still using glyphosate, this would provide very powerful evidence regarding its effects. Unfortunately, there is no global database that currently provides easy access to the incidence of these conditions in various countries over time and the levels of exposure to various chemicals.

3. Clinical Trial Data – This is the gold standard with regard to the level of confidence that a factor is truly the cause of an outcome. However, it’s also the most expensive to collect. As a result, clinical trials are often very small (less than 50 people). Exclusion criteria in trials often prevent study participants from being representative of real patients. There are ethical considerations that prevent us from running trials that have any risk of harm to participants. Due to the expense involved we have very few trials run on anything other than a molecule that can be patented and sold as a drug.

Causal Network Graphs

Network graphs indicate the likely predictive strength of the effect of various factors on an outcome. Clicking the link between two nodes will take one to a more detailed analysis of the relationship.

Causal Flow Charts

Sankey charts indicate the likely predictive strength of the effect of various factors on an outcome in terms of the width of the link. Clicking the link between two nodes will take one to a more detailed analysis of the relationship.

Intuition-Based Likely Cause Survey Results

Individuals may provide their own intuitive reports as to the most likely factors exacerbating their symptoms.

Predictor Search

The Predictor Search allows one to search for the likely effects of a specific factor on the current outcome.

Outcome Labels

Currently, all foods carry nutrition labels such as this one:

But how useful is it to the average person to know the amount of Riboflavin in something? The purpose of nutritional labels is to help individuals make choices that will improve their health and prevent disease.

Telling the average person the amount of riboflavin in something isn’t going to achieve this. This is evidenced by the fact that these labels have existed for decades and during this time, we’ve only seen increases in most diseases they were intended to reduce.

Study Index

Overall Mood Mega Study

Diagnosis / Symptoms

LOINC

Loinc Databases

Aggregated user data is used to determine the factors that most influence any given aspect of health, powering the QM Search Engine.

Anyone wanting to optimize any quantifiable aspect of their life is able to search and see a list of the products that are most effective at helping the average user achieve a particular health and wellness goal. For instance, if one wishes to improve one’s sleep efficiency, go to our site and search for “sleep efficiency”, where one is able to select from the list of products that most affect sleep efficiency.

Impact: Clinicians and those suffering from chronic conditions will have access to the personalized effectiveness rates of treatments and the percent likelihood of root causes.

The search engine is available in a variety of layout settings.

Network Graph Style

Embeddable Cards

Embeddable Simple List

Study Cards in Ionic App

Nutrition Facts Style

Bar Chart Style

Sankey Chart Style

The Unified Health API integrates the disparate health data standards in order to accelerate clinical research.

Contact [email protected] if you desire access.

6. Clinical Trials

7. Medical Codes, Terms, and Synonyms

• anatomy

• diseases

• findings

• procedures

• microorganisms

• substances

• etc.

SnoMed Databases

Data Schema

Wearables

EHR

Synthetic Data

Dietary Supplement Label Database

The Dietary Supplement Label Database (DSLD) includes label-derived information from dietary supplement products marketed in the U.S.

https://www.dsld.nlm.nih.gov/dsld/index.jsp

Dietary Supplement Ingredient Database

These data are appropriate for use in population studies of nutrient intake rather than for assessing the content of individual products.

https://dietarysupplementdatabase.usda.nih.gov/

Use Dietary Supplement Label Database above if you need data on specific branded products.

Data Download

FoodData Central

Contains 260k branded products

All of these datasets—including:

• the Food and Nutrient Database for Dietary Studies (FNDDS),

• SR Legacy

• the USDA Branded Food Products Database have been transitioned to FoodData Central which also includes expanded nutrient content information never before available as well as links to diverse data sources that offer related agricultural, food, health, dietary supplement, and other information.

Food and Nutrient Database for Dietary Studies (FNDDS)

Contains nutrient data for 9k general non-branded foods.

USDA Branded Food Products Database (BFPD)

BFPD is deprecated as it's now included in FoodData Central.

The USDA Branded Food Products Database (BFPD) is the result of a Public-Private Partnership, whose goal is to enhance public health and the open sharing of nutrient composition of branded and private label foods provided by the food industry.

Contains 239k products