Healthcare Research

Web 3 use cases - Healthcare Research

The problem

The problem with Healthcare Research is that most research involves white people. And more specifically, white males. And non-white races, mainly black and brown races, are nonexistent. Women are more “complicated.”

Now Web 3 and the blockchain won’t solve having other races be in RCT, random controlled trials - the gold standard in scientific research. Researchers need to recruit other non-white races for their studies to solve that. Also, RCTs are expensive, and many countries with money tend to be the US and Western Europe.

Not every recommendation or advice that comes out of the healthcare research would apply to women or non-white races. Most health recommendations don’t apply to women and non-whites.

But there could be a solution that uses the blockchain and AI to analyze health data, find correlations, and develop better recommendations for specific races, gender, or even patient.

The solution

If we can get a patient’s health data onto the blockchain that involves all genders and races, then we can have data scientists and AI analyze that data and find the correlation. The more patients and their data we can get on their blockchain, along with a greater diversity of genders, races, and ages - we can get recommendations on better health specific to gender and ethnicities.

The advantage of the blockchain is that it is public, and we can’t edit the data - once it’s on the blockchain.

Any researcher or AI can look at the data and analyze it. Researchers have biases that do affect their analysis. The researcher may not think they have any bias, but they are human, and humans have biases even if we don’t think we do. The data could be the same, but two researchers could come up with two different conclusions.

Having the data on a blockchain allows anybody, academic researchers or just someone interested in data science, to look and analyze the data. 

We can see competing companies or individuals that would create their own AI to research the data. Some AIs could be better than others.

This could create a marketplace of AIs so patients can submit their data and get a health risk assessment along with any specific treatment recommendations or lifestyle changes.

Potential Problems

Since the blockchain is public, no one wants their health data for all to see - mainly if it is used to discriminate against you. It could be a job that may discriminate against you based on your health because they may not want to pay for your health insurance, or it may affect your career. Terrible, but that could happen.

One way would be to make the data anonymous, so there is no identifying information that can be tied to you - no name, driver’s license, or social security number.

The patient must also consent to submit their health data to the blockchain. And the patient could also choose which data they wish to withhold - like age, race, height, or weight - even though that data would help researchers and the AI find any specific health recommendation by race, gender, or age. 

But the patient should have to consent and be able to decide which data gets onto the blockchain.

How will it work

1. Assumption - The patient uses the blockchain and off-chain to store their medical records.
2. A health research company wants to research cardiovascular disease and wants access to many patients across many genders, ages, and biomarkers.
3. The patient hears about this and decides they want to participate in this research.
4. The patient goes on a website and selects the cardiovascular research they want to submit their health data.
5. The patient then enters their patient ID.
6. The patient then selects which data they would like to submit.
   1. The patient selects all their data or some of their data.
      1. This includes:
         1. Gender
         2. Age
         3. Height
         4. Weight
         5. Medical History
            1. Surgeries
            2. Current and Past medications
            3. Allergies
            4. Family Medical History
            5. Past doctor visits
            6. Supplements
7. The patient then consents and submits the data but not their patient ID.
   1. That way, the research company will not be able to tie the patient’s health data to a specific person.
8. The research company will take that data and store if off-chain
9. Data scientists and AI programs can then analyze that data.
10. With more patient data, the better for the data scientists/AI to find correlations on what causes cardiovascular disease.
    1. They may find that a specific r