Airline travel. Blood pressure regimens. Amazon in health report.
I have run out of Amazon puns
The full report on Amazon in healthcare is out. Read it here.
We talk about:
Its overall playbook and strategy in other sectors and how it might apply to healthcare
What areas it should enter and how its existing moves might suggest future strategy
Which types of companies are most at risk should Amazon become a significant player in the space
And MUCH more
Tell me what you think. Do you agree with our analysis? Think we missed anything?
Get rich or supply trying
Amazon is clearly targeting is the supply chain, both in the pharmaceutical and medical supply side. However, there are several parts that the company is missing if it wants to enter this space (e.g. cold chain logistics).
Several smaller companies are attacking different parts of the pharma supply chain with their own solutions and could be promising acquisition targets for Amazon. Expert intelligence clients can see the full map here.
Escape the Eroom
Healthcare has something called “Eroom’s Law,” where new drugs are getting slower and more expensive to find.
This is the reverse of the more popular “Moore’s Law”, which says processing power doubles at a predictable clip of every two years. This has been a massive enabling force because companies can now do much more in a smaller space (like personal computers in our pockets!).
What would be the Moore’s Law/AWS equivalent that would enable an explosion of new companies in the healthcare space by reducing the cost to start a company or let companies do more with less?
In tech, by shifting a lot of the big upfront costs (data centers in this case) of starting a company from an “own” to a “rent” model, companies could get started for less. AWS, for example, lets you pay-as-you-use for compute power instead of setting up your own servers.
Bio definitely faces this as well, with a lot of upfront costs in the form of lab space, capital equipment, etc. We talk in the report about Amazon potentially being able to help smaller labs outsource some of these costs by creating a backed lab-as-a-service.
Another way to drop costs is by improving recruiting for clinical trials. The cost of recruiting for clinical trials is incredibly expensive both from a time and capital perspective, especially for rarer diseases.
We look at the clinical trial process, and identify areas that AI could potentially be used to make things more efficient (and areas where it was still a long ways away). See the full report here.
