Data management
In efforts to tighten up performance, many in the industry have turned their attention to AI. Could it help run better trials? Judging by the promise of AI-powered tools that are already available, it certainly seems so. For example, we can now recruit patients with unprecedented speed or predict trial failure before it happens so that there’s enough time to intervene. And, according to some in the field, we’ve barely scratched the surface of which AI is capable.
Patient recruitment
Recruiting enough participants for a trial is where you’ll sink most of your cost, plus a good amount of your team’s time. Typically, clinicians would need to sort through patient records to find suitable candidates, while patients would scour clinical trial databases and try to make sense of them, says Stefan Harrer. As the chief innovation officer at Digital Health Cooperative Research Centre explains, “Going through this manually is a huge burden. That’s the first thing that AI brings to the table: efficiency.” With AI, we can automatically identify eligible patients and match them with trials they may benefit from – a technique that’s called clinical trial matching. Two processes are happening here, notes Harrer. On one hand, clinical trial descriptions (which are publicly available) are mined and their eligibility criteria is extracted, while patient information is scanned to see who meets the criteria. Any online information describing their medical history can be considered, from electronic health records to social media profiles. However, you do need to manually assess the quality of the information that’s going into the model to reduce the risk of bias, Harrer adds. Researchers should look for any indications that a patient’s profile has been misrepresented, whether there’s enough data to work with, and if there are any errors in the dataset that need to be fixed. AI can also reduce the amount of people you need to recruit for your trial. One way to do this is by creating a “digital twin”, an AI model of a patient group that acts as a synthetic control and can account for a portion of the people required. The model is built using data on that cohort, for example, data from previous trials on similar groups or other records you have. “It’s really extracting those features from the population that are relevant to the trial, in terms of criteria and so on, and modelling those,” says Harrer. A predictive model is then used to forecast how the synthetic cohort would react had they been treated with the drug. But digital twins don’t eliminate the need to use real people as well. “You can’t completely substitute the real control arm with synthetic digital twin components, you complement it” adds Harrer, otherwise you wouldn’t have a truly randomised trial.
Clinical Trials Insight /
www.worldpharmaceuticals.net
Collecting better data To find out how a participant is responding to a treatment during a trial, the methods of choice are still largely to take their word for it or have someone else be present to monitor them. “This is prone to error like you wouldn’t believe,” Harrer stresses. With unreliable data, researchers may not be able to identify whether that intervention is beneficial, meaning the trial is more likely to fail. Instead, what if we could automatically collect data that gives a truer read on how someone is faring? Enter digital disease diaries, cloud-based systems that can collect patient data via wearable sensors or video. The AI system detects symptoms of disease and logs them, without the patient or a supervisor needing to report anything.
Using wearable tech in clinical trials may record data that could otherwise go unrecognised by the patient.
“Going through this manually is a huge burden. That’s the first thing that AI brings to the table: efficiency.”
Stefan Harrer
Using wearables, clinicians can identify how a participant is doing in ways that conventional assessments can’t, explains Aldo Faisal, professor of AI and neuroscience at Imperial College London. For example, a standardised test for physical fitness such as the six-minute walk test, measuring how far someone can walk in six minutes, wouldn’t pick up that a patient can turn around in bed again. Faisal
32%
The percentage of overall spending in a clinical trial on patient recruitment Deloitte
41
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