Among the many casualties of COVID-19 lockdowns have been clinical trials for drugs and other medical treatments. A recent survey shows that 69% of ongoing trials are affected by COVID, while the initiation of new trials has dropped 78%. With many trial participants unable or reluctant to visit clinics, new treatments will fail to reach the market, life sciences companies won’t recover the tens or millions of dollars they have invested in these products, and — most importantly — those products will not be available to help patients.
Decentralized clinical trials (DCTs) allow for the collection of required data from patients’ homes rather than from sites. They also require more involvement from trial participants, their caregivers and visiting professionals than from the site staff.
For sponsors, the first step toward DCTs is using a framework to assess which trials are best suited to leverage a decentralized approach. They should then identify the new digital capabilities and processes needed to meet three vital requirements:
Access to Care
Patients need the same expert advice, information and answers to their questions and concerns when participating at home as they would at a trial site. Providing this in an at-home trial requires applications that make it easy for participants to share information with caregivers and provide those caregivers with a complete understanding of the patient’s condition, capabilities and needs.
Consider, for example, integrating existing telemedicine systems with the videoconferencing and data gathering systems used for clinical trials. The fewer data-entry applications and devices a patient uses, the lower their burden and the more likely they will stay in the trial and provide accurate, timely data. Faster and near real-time integration of this data lets caregivers spend less time gathering information and more time understanding the patient’s condition, providing “warm care” that focuses on the patient’s needs instead of just objective clinical outcomes.
Integrating this patient data and the accompanying applications may require the use of cloud-based SaaS applications, data storage and application programming interfaces (APIs). Its analysis may require the use of cloud-based analytic tools and potentially artificial intelligence and other technologies to better assist in risk-based monitoring and decision making.
Timely, accurate and consistent data capture about a patient’s condition and response to treatment is one of the primary challenges of shifting to decentralized trials. In some cases, such as collecting a blood sample, there’s no alternative to collection at a local clinic or by a professional visiting the home. In other cases, as with movement tracking or blood pressure testing, the data can be collected by a wearable sensor, a mobile device supplied by the sponsor, or an app on the patient’s smartphone. Wearable sensors, however, must mature further to be accurate and dependable enough for use in clinical trials, and — for now — should be used for secondary, rather than primary, data collection.
Such sensors can generate hundreds of times more data than a traditional test. Network bandwidth, such as home Wi-Fi, is usually not an issue, as many devices can store data until it is needed or until the network is free to transmit it most quickly. Larger concerns are the accuracy of the wearable sensors and the need for back-end capabilities to analyze the data and present it in usable form to caregivers and trial sponsors.
For some information, such as patient-reported outcomes, patients can enter data themselves through their phones or via devices provisioned by the sponsors. Making interfaces easy to use and including images and language appropriate to the patient will improve the accuracy of data collected and help keep patients engaged in the study.
Cloud-based device provisioning and data-gathering applications can reduce the cost and implementation time for such devices. So can cloud data platforms that support multiple data management workflows, secure access for all study team members, and built-in data extraction and reporting tools.
Many trials use medical images to evaluate eligibility for a study or to analyze treatment results. The need for secure and easy image transmission will only grow with the adoption of DCTs. Consider cloud-based image management systems that provide features such as de-identification to preserve patient privacy and configurable workflows that match the trial protocols. This requires a robust mobile health technology platform such as Medidata Patient Cloud to bring together data captured from patients and sites, as well as data collected from sources such as connected devices and labs.
Preserving the connection between patient and doctor is essential if patients are to trust caregivers, follow instructions and candidly report symptoms. Keeping patients engaged with clinical trials reduces abandonment and increases the likelihood they will follow the trial protocol and faithfully record data.
The remote nature of DCTs makes it more difficult to sustain these person-to-person connections. Video calls with investigators and site personnel are helpful and certainly preferable to traditional communication channels, such as email, but are not sufficient. Augmented reality (AR) and virtual reality (VR) technology that “immerses” the patient in what seems like a clinical setting is one option. Early indications are that AR/VR would be most useful when highly personalized care is required for complex conditions, such as in oncology or behavioral health.
Mobile applications and wearable devices, such as sensors, can send patient schedule reminders to take medication or exercise, as well as issue digital “rewards” such as messages for achieving certain compliance levels. They can also secure consent (e-consent) from patients for new trials, and re-consent for existing trails for which protocols have changed to accommodate new processes due to COVID-19-related lockdowns — all while meeting regulatory requirements. When evaluating e-consent applications, look for features such as guaranteed signature compliance, remote consent monitoring, and localization capabilities for multiple languages and regulatory environments. A platform such as Rave eConsent combined with Rave eCoA is essential to achieve this.
Building a New Model for Clinical Trials
With social distancing required and as COVID-19 concerns linger, it may be months at least before large patient populations volunteer to travel to central sites to evaluate the safety and effectiveness of new treatments. Building a user-friendly, scalable and compliant digital infrastructure for decentralized clinical trials will speed the delivery of important new treatments to market, as well as hasten the sponsor’s return on investment for their development.