The Pandemic Has Proven a New Approach to Clinical Trials is Necessary

Contributed Commentary By Katherine Vandebelt 

December 22, 2020 | The COVID-19 pandemic has accelerated the need for a new approach to how clinical trial teams work with patients. At the onslaught of the health crisis, restrictions, and heavy precautions on personal interactions brought clinical trials to a veritable standstill. This left the life sciences industry looking for alternative ways to maintain patient participation in clinical trials, primarily, as it relates to patient interaction and data collection. 

With more than 1.1 billion connected devices expected in the market by 2022, patient-facing technology like video, audio, AI, advanced analytics, smartphones, the cloud,  is starting to enable the expansion of decentralized trials, in which patients will minimize clinical site visits because the data will come directly from devices., a global database of clinical trials, currently lists nearly 200 trials with “wearable devices” or “wearable technology” in the description. Wearables are making patients’ lives easier and disrupting the dated system for collecting data in clinical trials, through in-person visits and forms.

Thanks to wearables, smartphone and tablets, we’re moving to a world where traditional site visits will be supplemented by data from intelligent devices used by patients that may send readings multiple times a day — or even continuously. In the future, that information could be augmented with external data such as environmental factors like the weather, air quality, a patient’s location, or even their activity level at any given moment, to better understand how outside factors are impacting outcomes. Through AI and advanced data analytics, scientists will advance the development of digital biomarkers for remote assessment and improve the understanding of disease and patient behavior. 

According to IDC’s 2020 Life Sciences Trends report, “With continuing rapid advances in IoT-related health technologies, we are beginning to see new sensor/detection advances that could begin to deliver new valuable insights to life science R&D. It is only a matter of time before the next health sensor technology becomes a core metric in the IoT-enabled conduct of some disease-specific clinical trial.”

These emerging technologies support a wide variety of data collection, from simple Bluetooth blood pressure cuffs to electronic watches that monitor your heart and activity rate, to devices that can autonomously measure blood glucose levels. Already, we are seeing this data being integrated with everything from company-sponsored health programs, to health insurance apps, to electronic health record (EHR) integrations via Fast Healthcare Interoperability Resources (FHIR). The wide-scale use of longitudinal data collection through IoT-related health technologies in clinical trials is the next logical progression.


Managing the Deluge of Data

The use of this kind of real-world data in clinical trials represents a huge paradigm shift. These emerging mediums for collecting data not only allow studies to expand the data they are collecting for the trial but also help expand the potential participant pool and give more compassionate options for those geographically-challenged or seriously ill participants. But, with all this real-time data coming in, how does a study team manage it?  

The key to processing and analyzing the plethora of data is Artificial Intelligence (AI) and Machine Learning (ML). The amount of data that personalized, connected digital devices produce is far more information than humans can process or manage, and outsourcing or throwing more people at the problem is no longer sustainable or effective. Not only is there more data, but it is also much more complex. In addition to traditional data collected from wearables and sensors, digital-assistant conversations, photos, and other information is also being collected and needs to be managed. AI and ML can point to patterns and trends that humans can’t see and will ideally lead to a more accurate and detailed view of how patients are responding in trials, which can lead to better treatments in the long run.

For example, do patients’ heart rates spike two hours after taking a new medication? Are their activity levels rapidly dropping, signaling that the treatment is causing extreme fatigue? Is there a connection between an adverse outcome and the air-quality where participants live? Wearables can provide this data and AI can analyze and predict patterns at scale. This will be invaluable as the treatment moves from testing to production and adverse effects are monitored on a much larger scale.

AI and ML are already being incorporated into advanced, cloud-based life sciences technology platforms to support trial design, data monitoring, and safety case management. But this is only the beginning. Five years from now, a patient’s clinical trial experience could be very different. Wearables combined with cloud technologies will enable continuous and instantaneous data collection and advanced analytics that is fed back to the sponsor study teams developing new treatments. Each enrolled patient could be creating millions of data points a week—or even per day! That could mean more accurate assessments as the data will reflect the patient’s everyday experiences. Digital biomarkers also hold great promise for the scientific community to inform disease characteristic and increase clinical trial objectivity.

The opportunity is before us to leverage the experiences and learnings from an unimaginable 2020 to embrace and accelerate change in our industry. Removing much of the burden on patients by collecting real-world data that is managed and analyzed by AI and ML—has vast potential. It can speed the time in which treatments can go from bench to bedside, but also positively change the experience for patients who are counting on the success of these trials.


Katherine (Kathy) Vandebelt is Global Head of Clinical Innovation at Oracle Health Sciences. With over thirty years of experience in clinical research working in different geographies and across various TA, Kathy has worked with various organizations to advance their clinical operations and business processes to a better operating model. Kathy believes patients are the most important constituent in clinical development and provide the necessary information to assess the safety and efficacy of new medicines. She strives to introduce new experiences and making the clinical research ecosystem better for patients, healthcare professionals, sponsors and regulators using the power of technology. Join her in the movement to make clinical research an accessible care option to all. She can be reached at

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