Benefits of Remote Patient Monitoring

Remote patient monitoring (RPM) uses "connected peripherals" (wearable devices and sensors) to collect and track biomarker data outside of traditional care settings. This isn’t new, but the advent of cloud computing and more seamless integration has improved the patient experience and expanded the use cases for RPM.

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In clinical trials, RPM is playing an increasingly important role to track the safety and efficacy of interventional drugs or devices, with many ensuing benefits:

• Improved patient experience: The use of RPM can enable participants to have a smoother user experience and lower reporting burdens, helping clinical research teams to recruit and retain a more diverse population of participants who might not have the time, budget, geographic proximity or flexibility to go to a clinic.  RPM can thus expand access to promising treatments and help address healthcare disparities.

• Better, easier data capture: Traditional site-based studies rely on the patient’s ability to recall and record information. By contrast, RPM provides objective data to capture more accurate outcomes. By using sensors and wearable devices, sponsors can collect continuous data on vital signs such as heart and respiratory rate – data that is much richer than the once-per-clinic-visit measurement that occurs during most traditional studies. This continuous data also enables sponsors and clinical teams to document, display and review health events in real time, a capability that helps rapidly detect adverse and serious adverse events.  

• Ability to capture novel signals: RPM also enables teams to capture non-vital signals, such as acoustic and visual measurements, in the patient’s natural environment. For example, study teams can use passive listening to track the frequency and severity of a baby’s cries. The device can track and report this data, ultimately illustrating the efficacy of a new colic treatment. Teams can interrogate new physiological symptoms within new contexts—for example, the relationship between snoring and sleep quality. They can also integrate quality of life metrics, such as time spent walking compared with sedentary activity, to understand a participant’s daily routines and quality of life (QoL).

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Five things to know when incorporating RPM into your DCTs

Naturally, when new tools and trial approaches are introduced – especially ones as significant as remote patient monitoring in a DCT – there will be challenges. A recent Oracle report surveyed clinical operations professionals at biopharma companies and found the top concern with remote data collection and patient monitoring to be the “quality of the data.” While some of these concerns may be fear of the unknown, there are real challenges that need to be addressed in order to deliver better data.  Here are five key things to keep in mind when planning for RPM in your next DCT:

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1. Take the time to identify the right devices

With the proliferation of wearables, there’s no shortage of consumer-oriented vitals monitoring devices. But identifying the devices that are pharma-grade, FDA-approved, and clinically and scientifically validated for the desired endpoint can be tricky. Regulatory approvals are another issue. They can vary across different geographies, posing a challenge for international trials. Finally, devices need to be appropriate for the target population, accounting for differences in gender, body type and age, as well as the equipment’s ease of use. For example, when conducting a trial in warmer climates, ObvioHealth discovered women were more reluctant to wear a heartrate monitor on their chest due to its visibility underneath lightweight clothing. It is important to consider all of these variables and carefully review them and test them before launch.

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2. Don’t underestimate the challenges of seamless integration

In clinical trials, RPM is playing an increasingly important role to track the safety and efficacy of interventional drugs or devices, with many ensuing benefits: Depending on the vitals that need to be measured, study teams may need to partner with several vendors—each of which has different metrics, data models, APIs and proprietary cloud-based solutions. Some devices measure continuous data, while others capture point-in-time data, making effective comparisons challenging. Effective study design and employment of a common data model can mitigate some of these challenges.

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3. Too much data can be as bad as too little

Remote patient monitoring makes it possible to gather much larger volumes of data than is possible in a site-based setting. Rather than visiting a site to report data every six weeks, patients can be monitored 24/7. That poses the challenge of determining what information should be captured and displayed to track the metrics properly and efficiently.  In the same way that data must be properly integrated, it must also be easily visualized. RPM studies must be designed with easy to navigate dashboards that highlight key indicators at a glance. ObvioHealth is careful to design these dashboards up front, integrating the needs of both the study team and the sponsor to identify the most relevant visualizations (eg: daily or weekly averages + any peaks or valleys that are out of acceptable range).

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4. Always design with the patient in mind

As mentioned above, participants need to be comfortable wearing a device. But they also need to understand how to use the devices properly.  For example, they need to remember to charge them to maintain performance and to check them to ensure they are registering properly. With that in mind, study teams must carefully design—and proactively offer—training and support for participants. This might include making the team available to trouble-shoot issues via online chat. On a positive note, wearable device usage is a helpful leading indicator for other compliance issues, which gives investigators an opportunity to intervene more quickly and effectively when an issue arises.

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5. Look for novel outcomes

By combining disparate data points, RPM allows teams to identify new ways to measure data and detect trends that might not emerge in a site-based setting. This can lead to meaningful new outcomes that provide insight into progression, safety and recovery. For instance, teams could use temperature, oxygen saturation and coughing frequency to identify early symptoms of respiratory ailments. Or measuring coughing amplitude over time may allow teams to screen patients for tuberculosis or COVID-19 sooner than they would otherwise.

Machine learning and AI can help detect these trends in the data. The capture and notation of unstructured data, annotated by experts, can train machine learning algorithms, making them “smarter” over time. For instance, in a pediatric study currently under way, the ObvioHealth platform is integrating a clinician’s expertise into a stool-rating tool and thereby reducing the burden on physicians to review stool samples. In turn, researchers can identify new patterns and new connections in disease progression and symptomology.

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Looking ahead to the future of RPM

While RPM has evolved rapidly over the past decade, we’re only beginning to understand its full potential. Looking ahead, RPM will continue to improve through:

• Clinical validation and partnerships with the academic research community
• Development  and validation of digital instruments to allow teams to explore new observational measures
• Creation of an open ecosystem with third-party devices and digital instruments within an “app store” environment
• Further integration of clinical studies into the continuum of care.

The more studies implement RPM, the sooner sponsors and clinical teams can realize the true promise of this approach. RPM is the next frontier for clinical trial evolution in the COVID-19 era and beyond—with the potential to revolutionize access to life-changing new treatments.

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