Health IDs, synthetic biology, and quantified self
With a relatively commentary light week I wrote about the other fields I'm paying attention to
Every week I write about blockchain technology, healthcare, and related topics. On Sundays I analyze the latest updates and ideas from that week and every few weeks I publish additional essays on related topics.
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Blockchain and healthcare updates
From the press release:
GE Aviation is offering a new health credential application, Health Application ID, for the aviation industry to address COVID-19 related safety concerns. The solution focuses on three important areas:
Employee Control: screening workers to facilitate a safe return to the workplace
Passenger Control: screening passengers for responsible and safe aircraft occupancy
Object Control: clearing objects on aircraft as disinfected to improve customer trust and confidence
It seems that they have integrated two solutions into one offering: a health identity solution that is similar to immunity passports, and a way of tracking objects a la standard track and trace use cases. Together these are more comprehensive for airlines, and the identity solution can be easily updated to support vaccination or antibody records.
Not sure this one checks out. As a guest at a hotel how do I know the cleaning records I see on a blockchain weren’t put there fraudulently in the first place?
Back in December of 2019 Fantom signed a memorandum of understanding with the Afghanistan Ministry of Public Health. Here was the scope:
Under the MoU, the blockchain technology would help Afghanistan Ministry of Public Health identify fake medications, create medical records in the hospitals and computerize the patients' files, the ministry said in the statement posted on its official website.
Now Fantom is gearing up to start a 3 month pilot with a few partners to track prescription drugs and a handful of other health products in Afghanistan.
Other things I’m spending my time on
As this was a relatively commentary-light news week, I thought I’d outline some of the non-blockchain places that I’ve been spending my free time on that my readers might be interested in:
Synthetic biology - this is one of the most exciting and underrated fields I know of. Synthetic biology seeks to engineer biological organisms, literally manufacturing biological components or systems that don’t exist in the natural world. For example, creating micro-organisms that break down pollution, engineering yeast to produce an anti-malaria drug, or turning the air into protein. There is great potential to solve a lot of big problems in this space.
Beyond the captivating idea of engineering biology, I am very interested in the social structures and norms of the synthetic biology space. Synthetic biologists put a premium on knowledge by doing and Feynman’s famous line “What I cannot create, I do not understand” is a community favorite. But, they reject the notion that science must happen in big government or corporate labs and focus on “small science” as much as “big science.” The result is a mostly open community that encourages collaboration, validating the findings of others, and broad participation.
As someone who works with open source technology this ethos has obvious appeal, and there is a surprising degree of overlap with the crypto space at large:
Crypto gives anyone the tools to create currencies, synthetic biology gives anyone the tools to create biological organisms.
Crypto encourages everyone to verify financial systems, synthetic biology encourages everyone to verify scientific findings.
Crypto rejects the notion that money must be made in central banks, synthetic biology rejects the notion that science must be done in giant labs.
One could go on further, but I will leave that for a future essay. Although there are clear challenges to overcome, I am hopeful that this different way of doing science could be a solution to the crisis of productivity that ails the life sciences today.
Quantified self - I got an Oura ring back in March to track my sleep and monitor my body temperature as a potential early warning that I could have COVID-19 (the NBA later had the same idea). Outside of those purposes I have found it powerful to just have metrics on my health, and I finally “get” wearables. Given concrete numbers on my health I naturally dig into my data for insights and seek to improve my metrics. For example here’s my 7 day rolling average heart rate (red line), the date I started lifting weights again (purple line, and at home gym, mind you), and the date I added running to my exercise routine (blue line):
Nothing earth shattering, but it is very cool to me to see that data, and the improvement encourages me to keep at it. Honestly, it feels a bit like playing a game, but with the benefit of the game being good for me. As another example here’s a box plot that confirmed my suspicion that I often sleep worse on weekends, as indicated by the lower bounds:
So of course I’m doing something about that: experimenting with changes in my habits, capturing that in data, seeing how my body responds, etc. It’s been interesting for me and I can see the potential of the quantified self movement now. I don’t need much coaching — for me it’s really just having the data and I’ll figure it out on my own — but I can imagine how a digital coach nudging someone could be really powerful.
It also occurred to me that I’m doing little experiments in a silo, but using federated learning/analytics those experiments could be scaled to many other people, thus unearthing more powerful insights. Perhaps the next step after “quantified self” is the “quantified community,” where like minded people use wearables and federated learning/analytics to do data science across their community.
What I’m reading this weekend
I greatly enjoyed this thesis. The idea of user ownership was one of the reasons why I got into this space, and I still think it holds great promise.
This paper was sent to me and it looks interesting, but I haven’t had the chance to read it yet.
It seems they are also offering APIs that make it so you don’t need to write new code to use their confidential compute services (which you usually need to do to use that tech). That would significantly lower the barriers to using confidential compute.
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