Measuring More of What Matters
Technological innovations are allowing for improvements in the clinical trial sector and providing greater volumes of data; however, companies should be mindful to ensure they are measuring meaningful data.
“In terms of categories of innovations [that have had an impact], I would say digital is a big one,” specifies Mikesh Udani, CEO and co-founder of Albus Health — a medical device spinout from the University of Oxford that has developed a passive and non-contact respiratory monitoring solution. “We saw about 10/15 years ago a transition from patient paper-based workflows and manual workflows to more digital ways of recording information, which seemed like breaking new grounds 15 years ago, but now it's mainstream.”
The advantages of this digitalization are becoming apparent in other areas as well, such as remote patient monitoring of data that would otherwise require visits to the clinic, Udani continues. “There are site-based workflows that are now more digital, and a key area of development are digital endpoints. So, over the next 5 to 10 years, [digital endpoints] are going to be the next wave of transformation that comes,” he says.
Another innovation that is having an impact is artificial intelligence (AI), Udani notes. “AI is touching so many different aspects of clinical trials, even parts before clinical trials and drug development,” he states. “When it comes to clinical trials, [AI is touching] everything from data management — having better data quality, doing quality checks, data analysis — to supply chain, and now, from what I understand, even protocol design and protocol writing.”
Net Positive for the Industry
When considering if technological innovations have helped to accelerate the adoption of a decentralized clinical trial (DCT) model, Udani considers the perspective of the most important stakeholder, the patient. “One of the most valuable things [these technologies] have done for the patient is that they’ve removed burden,” he explains. “Instead of having to note down symptoms every day, we have technologies that can measure that without patients needing to do anything, when earlier we were asking them to come into the clinic every week or every fortnight for six months, we can now do home-based assessments.”
Additionally, the potential patient population for trials can also be expanded thanks to technological innovations and DCTs, Udani confirms. “About 15 years ago, it would have been difficult to think how an 80-year-old person with dementia could have participated in a clinical study or how a 6-year-old child with a neurological rare disease could have participated in a clinical study,” he asserts. “These are all very important populations, so, technologies and decentralized medical trials have allowed us to go to these patients, they’ve allowed us to improve our recruitment in clinical trials. So, from the patient's perspective, there are many different areas of value that technologies and DCTs have unlocked.”
However, it is also important to ensure that certain parts pf the population aren’t excluded, Udani specifies. “We have to be careful that in our attempts to remove bias in one area, we're not adding bias in a different one. This is something that is on the technology and technology developers,” he says. “Technology should be developed to meet the needs of the patient and should work to meet those needs. So, it's a very important consideration if the technology is adding burden.”
Other stakeholders also benefit from innovative technologies and DCTs, Udani remarks. “Whether it comes to data managers, sites, right up to the regulators, we can now see higher quality data, higher compliance data to take their decisions,” he says. “So, I certainly think that this has been a net positive for the industry.”
Measuring More Meaningful Data
As technology innovations are being used more for clinical trials, there is an inevitable increase in data that also needs to be handled, raising some potential challenges. “There are two aspects to the increased data issue,” Udani continues, “one is balancing signal with noise, and the other is, of course, data privacy.”
From Udani’s perspective, more data in general is not better, rather industry needs to ensure that it is measuring more data that matter. “Let’s take sleep as an example,” he comments. “Say 15/20 years ago, we were asking patient to write down every morning how well they slept in a six-month study, and it’s very natural for people to forget to do that or to not remember how well they slept. Now, we can measure that every day, more objectively, so that is better.”
However, there are other potential data streams adding to the clinically meaningful data that don’t add value, such as the information about sleep that can be gained from a smartwatch — a sleep score — Udani adds. “So, we have to be careful about when we measure a lot of information to ensure that we measure information that is clinically meaningful, that is meaningful to patients,” he asserts.
“The other aspect is privacy. It’s great that over the years we’ve become more aware of privacy, not just in clinical trials in general, but any technology that we interact with,” Udani notes. “Privacy should be built in by design rather than being an afterthought. It is very important that we have technologies that do not collect patient-identifiable data unless it is absolutely needed. It’s important that patients feel comfortable using the technology over long periods of time and that they have control over their data.”
A Pivotal Innovation
For Udani, an area of innovation that will be looked back upon in a few years as being pivotal would be the ability to measure sleep. “Sleep is just so fundamental to every disease, to our quality of life, to life in general. that measuring that is just a no-brainer,” he says.
“About 10–15 years ago, we didn't really have reliable ways of measuring sleep, and now we do. Now we have a way of measuring what is happening in that eight- to 10-hour period of the day, which so far has been in the dark,” Udani explains. “And this measurement goes beyond just sleep.”
Measurements during a patient’s sleeping period go beyond just measuring when they are asleep, Udani stresses. For example, it is also important that measurements of when a patient wakes up and understanding the reasoning behind such awakening, he notes.
“These [measurements] will just become mainstream,” Udani says. Sleep measurements are becoming critical in many different areas, including respiratory, oncology, cardiovascular, and obesity, he adds.
“So, in five or seven years' time, we will get so much value from measuring this parameter in clinical trials, that it's going to become mainstream,” Udani summarizes. “And we can look back and say, ‘well, this was obvious, we obviously had to do this, we were missing out on about half of the day's information’.”
About the Interviewee
Mikesh Udani is the co-founder and CEO at Albus Health, providing remote monitoring solutions to pharma clinical trials globally. Mikesh studied Mathematics at IIT and Computer Science at Oxford. He was also an Oxford Biodesign Fellow, following a needs- led approach to medical technology, innovation and research. This led to him co-founding Albus Health with William Do. Prior to Oxford, Mikesh worked in derivatives trading at Deutsche Bank.
Image Credit: © Andrii Yalanskyi - stock.adobe.com
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“Measuring more of what matters” in clinical trials refers to focusing on high-quality, meaningful data that improves patient outcomes and research decisions, rather than simply collecting large volumes of information. Advances in digital technologies and monitoring tools allow researchers to capture more precise clinical data, but the priority should be identifying metrics that provide real clinical insights and support better decision-making.
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Digital technologies are transforming clinical trials by enabling remote patient monitoring, digital workflows, and automated data capture. These innovations allow researchers to collect continuous and objective data without requiring frequent clinic visits, which can reduce patient burden and improve data accuracy. As a result, digital tools are helping clinical teams generate richer datasets and more reliable insights during drug development.
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As clinical trials adopt digital tools, the amount of available data is increasing rapidly. However, more data does not always lead to better insights. Researchers must balance signal versus noise, ensuring that the data collected directly supports study objectives and patient outcomes. Focusing on meaningful metrics helps improve trial efficiency, data quality, and the reliability of regulatory and clinical decisions.