How to swim in the cold water of decentralized clinical trials

June 22, 2020

(The water in the pool in Cascais in October was < 12 degrees)

There are over 135 COVID-19 vaccine candidates in the pipeline at the time of writing this post – June 20, 2020.

Although there are always personal, political and emotional preferences, we should probably not select a clinical data management platform like we choose villas in exotic vacation spots.

The reality is that simplification of the protocol and the study conduct will have a much bigger impact on time to completion than choice of a particular eClinical platform.

COVID-19 has much wider ramifications for the clinical trial industry beyond the next 12-18 months.

COVID-19 signals a driving concern to use technology to acquire valid data from clinical trials in the fastest way possible.

Virtual clinical trials

One approach is to recruit and collect data from patients online in what is called a virtual trials model.

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.

Hybrid clinical trials

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 hybrid trials running right now on the 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.

Connected medical device vendors understand the value of merging patient, clinical and device data into real-time data streams.  Once you have a real-time data stream, you can use real-time automated monitoring.

However, bringing merged real-time streams of patient, device and clinical investigator data into the domain of mainstream drug trials is hugely challenging because the data sources are highly heterogenous.

Combining patient outcome reporting with mobile apps, passive data collection from wearables and phones and site monitor data entry creates a complex distributed system of data sources.   Such a complex distributed system cannot possibly be monitored by assuming that there is a single paper source document. That assumption is no longer valid.

Observability of events

We need to correlate and group events across different systems, applications and users. We need to achieve low level observability of a patient while attributing it to top level cohorts and sites in the study.

This is especially difficult since the different EDC systems and digital appliances were not designed for monitoring.

You can see a presentation here on using pivot tracing for dynamic causal monitoring of distributed systems. This is work done by Jonathan Mace while he was a PhD student at Brown.   The work was done on HDFS but the concepts are applicable to virtual and hybrid clinical trials.  is a cloud platform that automates detection of deviations in clinical trials using these general concepts.

Flask provides an immediate picture of what’s going on. The picture can then be grouped by patient, physician, principal investigator, project managers all the way up to the  VP Clinical and CEO. In political terms, you might say that  we  democratize the process of observing clinical trials using metrics and automation.

Automation can be used to speed delivery of valid data to decision makers in 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.

You can see my talk here: Automated Detection and response for clinical trials.

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