Posted 6 June, 2016 by Alix Dunn

Drawing Inspiration from the Use of New Tech in HIV Clinical Trials

In April, I spent a day  with 35 medical researchers who carry out clinical trials of HIV vaccines, largely in East Africa. The get together was called ‘Innovative Uses of Technology in HIV Clinical Trials’ and I was invited to lead a session to temper discussions about areas where new technologies might improve the quality and efficiency of clinical trials.

This is a quick overview of technical developments in clinical trials that I, as an outsider, found most interesting. In a follow-up post, I’ll share the three tactics I suggested for more robust responsible data planning when innovating with new tech in clinical trials.

New developments in clinical trials

There are big innovations in using technology to improve the quality of clinical trials. Here are some to give you an idea:

  • Transmitting pill containers now make it possible to track precisely when (and if) a pill was taken. This makes a big difference, as measuring ‘adherence’ to the pill regimen is key to determining a medicine’s effectiveness.
  • New methods like using biometric data to track patients throughout studies are now sufficiently cheap and practical to consider. This could help prevent ‘co-enrolment’: where someone signs up for several trials at the same time, endangering their health and undermining studies’ quality.
  • CRF (case report forms) have long been the main form of data collection about patients during a trial. Now, electronic CRFs (eCRFs) are making it possible to collect, recall, and preserve more and more patient data.
  • Daily diaries are a key method of tracking patients’ experiences when testing drugs. Instead of asking patients to write them by hand (which usually means that data doesn’t comply with data standards), researchers can now collect information digitally through snap SMS polls and mobile app surveys.

Overall, I was amazed at the level and precision of innovation the researchers displayed. They have spent considerable time deploying new techniques within trials, as well as conducting thorough studies to research the effectiveness of these new tactics.

The broader social change sector could learn a lot from this more measured and outcome-focused adoption of new technology. However, I found some of the discussion about the potential implications of those new technologies overlooked potential concerns, and focused instead on the pragmatic, short-term value of incorporating the tools.

To be sure, biometrics could make research studies more efficient by making it easier to authenticate participants throughout a study. But researchers should also be thinking harder about privacy and security implications beyond the requirements of slow-moving institutional review boards (IRBs) or legal restrictions.

There are serious privacy implications to using biometric data from populations who are involved in clinical trials, particularly clinical trials for HIV medications. The data could be used in future to target or otherwise harm study participants, or stigmatise data subjects if there is a data breach. 

During the event, three speakers discussed the rate of refusal for biometrics in particular communities and settings(the percentage of people who decline to provide biometric data), and how to reduce those rates by adjusting study settings and practices. If you ask all participants in a large-scale study to submit biometric data and 0% refuse, I think it is likely you have an issue with your informed consent process.

Overall, I found much inspiring about the clinical trials communities’ pragmatic innovation and overall approach. In the face of dramatic change of the technology landscape surrounding them, there will likely be increasing overlaps and learning opportunities between their work and that of the responsible data community. I’m looking forward to learning more from their methods and mitigating strategies in the future.

Part two of this post is about the three tactics I shared with the researchers to support their responsible data practices and decision-making.

(Image by Mattza – CC-BY-SA)

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