How AirBnB can help us deliver COVID-19 vaccines to patients faster
There are I think, 135 COVID-19 vaccine candidates in the pipeline at the time of writing this post.
I am not going to suggest that we choose a vaccine like we choose villas in exotic vacation spots.
I will try and connect the dots between COVID-19 vaccine development and technology. We can learn from the work that AirBnB (and Uber and Google) do in system engineering to help us automate monitoring of clinical trials
At flaskdata.io — we automate detection of deviations in clinical trials.
We give everyone an immediate online picture of what’s going on. Everyone means everyone — from patient to physician to principal investigator to project managers to president of the pharma.
In political terms — we want to democratize the process of observing clinical trials.
There is a gigantic amount of buzz on virtual clinical trials because of COVID-19. The idea is to go direct to patient with digital tools and engagement. This is theoretically supposed to cut out all the friction for recruitment and and overhead of research sites.
With all the buzz on virtual trials, no one seems to know how many virtual trials are actually being conducted. (There is a well-known axiom that technology adoption is inversely proportional to PR).
It may be that in the future, fewer than 5% of trials would remain all paper. Maybe 5%, will go fully virtual.
The action will be in the middle in “hybrid”. Trials are moving away from paper, “virtualizing” a process here, a step there. I am looking to see how much of this is taking place, and to what extent COVID-19 accelerates it, and which processes and steps are virtualizing the fastest.
Based on observing 12 virtual/hybrid trials running right now on the flaskdata.io platform, today, I can assert that hybrid trials are complex distributed systems with a whole new set of challenges that make the old site/investigator-centric model look like a stroll in the park.
Automation can be used to speed delivery of valid data to decision makers in clinical trials.
We can learn lessons from people like Google and AirBnB who run complex distributed systems of their own.
Here is a talk I gave on Automated Detection and response for clinical trials.
The basic idea is to monitor with alerts. Some of the ideas from the talk:
Alerts are metrics over/under a threshold
Alerts are urgent, important, actionable and real
In the world of alerts, symptoms are better than causes
Validate: Are we calculating the right metric?
Verify: Are we calculating the metric right?
Do it Fast. fast. fast.