Before the pandemic, many sponsors were hesitant to embrace decentralized clinical trial (DCT) elements in their research. They were concerned that technology-driven research might not meet regulatory standards, and that data captured on personal devices would not be secure.
Covid-19 changed all of that. The closure of sites forced sponsors to embrace telehealth, wearables and remote monitoring. It was a difficult transition, but it helped life sciences companies see the value that DCT technology brings to trial designs.
That experience accelerated the industry’s technology adoption by years, and we expect the transformation to continue in the year(s) ahead. One report found that in 2019 just 38 percent of industry professionals expected virtual trials to be a major component of their portfolios. But a year later, that number jumped to 100 percent.
Pandemic-driven innovation
The need for pragmatism drove the initial adoption of technology during the pandemic.
With very limited access to patients, and accelerated timelines to launch Covid-related trials, technology became a lifeline that sponsors couldn’t ignore. It helped them keep existing trials moving forward, and rapidly accelerate recruiting for vaccine and treatment trials. In Janssen’s Covid-19 Phase 3 vaccine study, the sponsor was able to recruit 40,000 patients in less than two months. Recruitment was accelerated through the use of telehealth technologies, virtual oversight, and digital patient engagement strategies.
We expect that the benefits sponsors experienced in these trials will make them more open to considering new technologies that promise to make trials less expensive and more accessible for all patient populations.
While it’s always unclear what the future holds, these are the technologies we expect to see more of in 2022.
Personal devices
Sponsors have historically been slow to let consumers use their own devices for data collection due to concerns about data safety and lack of control. That has led many of them to spend millions of dollars provisioning clunky, often outdated devices for trial patients to record relevant data. The need for rapid trial start-up in the pandemic meant they had to let go of these concerns, and give patients the freedom to complete eConsent forms, electronic clinical outcome assessments (eCOAs), questionnaires and other data collection tasks on their own devices.
It proved that data collection apps were just as secure and unbiased when used on a patient’s smart phone as they are on a dedicated clinical trial device. And because patients carry their phones everywhere, they were more likely to respond to alerts reminding them to complete reporting tasks. The benefits not only increased the quality and consistency of data collected, it cut time and cost from the trial because sponsors didn’t have to wait weeks to acquire and ship thousands of devices to patient populations. As a result, sponsors today regularly wish to include a bring your own device (BYOD) option in future studies, signaling the industry is finally ready to make personal devices an accepted tool in the trial plan.
Smart voice assistants
For many consumers, Alexa, Cortana, Siri and other intelligent assistants are already embedded in their daily lives. These tools act like virtual assistants, selecting music, researching queries and reading texts.
It’s not a stretch to think these assistants could soon play a role in clinical research. The easy voice access offers a compelling tool for patients who aren’t able to leverage their smart phones to ask questions or record data. For example, consider a patient in a study for migraine treatments. The pain and nausea of migraine can be exacerbated by bright lights or the need to focus, which can make using a smart phone to record symptoms impracticable. But if patients could lay in the dark and dictate those symptoms to Alexa, it gives them a conduit to the investigators to share their experiences in real time, rather than after the event subsides.
Conversational chatbots
The current generation of chatbots use artificial intelligence (AI) to communicate with consumers using natural language and can respond to questions with high degrees of accuracy. They typically include built-in verification steps as a way to ensure the data exchanged is accurate, and to ask if consumer requires additional human assistance.
These tools are already commonly used to reply to medical information queries, and they could soon be part of the clinical trial environment. Like intelligent assistants, AI-driven conversational chatbots could provide patients and caregivers with a channel to ask questions and record data verbally any time of the day or night.
Among older populations, those with small motor skill issues or vision impairments, such conversational technology could make clinical trial interactions more accessible and further remove barriers to participation.
Consumer wearables
Millions of people already wear Fitbits, Apple Watches and other health trackers that automatically capture valuable healthcare data throughout the day. These real-world data points could provide additional insights to sponsors about a patient’s mobility, sleep patterns, heart rate, exercise, gait and other health statistics.
Sponsors already use medical-grade wearables in clinical trials to capture specific patient outcomes. Including data from consumer wearables could give them access to more robust data streams without commissioning extra devices or requiring patients to manually track and record these data points.
Ambient data
Ambient Intelligence (AmI) uses sensors and AI to monitor the patient’s environment and care. These tools are already being used in “smart hospital rooms” and elder care facilities to alert visitors if they fail to sanitize their hands, or to identify behavioral clues in patients that could indicate health crises. Such sensors could be leveraged in trial participants’ homes to track environmental factors relevant to the disease and treatment.
In some cases, that data may already exist. For example, utility companies capture detailed data about individual user rates and local trends, tracking changes in water and energy use over seasons and among different demographics. And consumers with smart thermostats can capture detailed data about their home environment, including temperature, humidity, energy use and traffic patterns. Some of that data could be of value to clinical researchers. For example, patients with anemia may raise the temperature to address feeling cold, and spikes in water consumption can be a sign of untreated diabetes. It is one more way to use existing real-world data to better understand the patient experience.
All of these technologies already exist, and most have been designed by leading edge technology firms that have the talent and resources to create safe, reliable and deeply intelligent data capture tools. The life science industry has an opportunity to benefit from their innovation, rather than trying to reinvent that wheel.
The pandemic helped the industry get over the initial hurdle of using consumer technology to capture clinical data, and we’ve seen the benefits it can deliver. The question now is how we will apply these lessons in the future to capture more robust data while improving the patient experience.
Photo: gabort71, Getty Images
