Direct to patient strategies and crafting patient centric trials are of increasing interest in drug development trials. Wearables and sensors are seen as key enablers of this strategy. In order to assess the feasibility of collecting and integrating data from a number of different devices we undertook a proof of concept study, the results of which we presented in a poster entitled Real-Time Monitoring of the Digital Patient in Clinical Trials (DIA USA 2016). This poster reports on a framework for the real-time monitoring of subjects using wearable and patient-centric devices from multiple vendors.
The sustainability of the current clinical trial design process has been under scrutiny for a number of years. A 2014 paper commissioned by the US Department of Health and Human Services identified multifactorial barriers to drug development with patient retention and recruitment identified as significant contributors to the problem, and the report identified the wider use of lower-cost facilities, including at-home testing, as having multiple benefits, including increased trial participation .
A number of recent innovations have created a digital health ecosystem that is facilitating the monitoring of patients in their home environment; the continued invested in mobile health (mHealth) by global health services, and a parallel explosion in the number of medical device grade, sensors that can measure a growing number of critical physiological indicators.
The shift of clinical assessment from the controlled environment of a trial site to the uncontrolled environment of a patient's home is a considerable challenge. The ability to authenticate source data while maintaining the anonymity and privacy of a patient offer considerable challenges. Connectivity and data security add additional complexity to the operations of remote studies.
The approach we explored was the creation of a suite of wearables that could be combined and deployed in a non-clinical setting and tailored to specific therapeutic areas (Figure 1), we refer to this as the Sensor Suite Approach.
We wanted to take this concept further and develop a methodology to facilitate the real-time collection of data from a sensor suite, the transmission of the data in a secure and timely fashion and the integration of the data into a compliance and monitoring dashboard.
The collection of real-time data in a home setting can give sponsors and trial managers greater visibility and assurance around GCP and protocol compliance in ways that were not previously viable. We believe that the real-time tracking of sensor suite data through compliance dashboards can be a key enabler for the wider adoption of remote wearables and sensors in clinical trials.
We deployed commercially available devices and sensors to a number of healthy volunteers located in non-clinical settings in 3 different countries. We used FDA-cleared and consumer devices to generate physiological data similar to that used routinely in clinical trials: heart rate, activity/sleep, oximetry, weight and blood glucose.
The data were integrated into an informatics hub via a digital health platform for central analysis alongside other user attributes. We used a unique security token provided by the digital health platform to maintain a de-identified flow of data. All users successfully used the unique security token provided to them to connect to an online data marketplace maintained by the digital health platform provider, allowing them to authorise the transfer of their data from the device vendor to the digital health platform.
We used the vendor's application programming interface (API) to pull data directly from the digital health platform into our own informatics hub. The time lag between data collection by the remote device and integration into our informatics hub was consistently less than 30 seconds.
However data collection without integration has a limited usefulness, so it was important to show how de-identified data collected through mobile devices can be re-integrated with the full set of clinical data on the patient in a secure environment in real time. Data collected in this way are more objective and have a more transparent audit trail than more traditional clinical outcome assessment instruments.
We think there is huge potential in this approach to transform the way we monitor clinical trials, improving the quality of patient data, reducing patient burden, increasing patient engagement and redesigning the thinking around how we conduct clinical trials. This approach also allows us to easily compare sensor suite data with electronic patient-reported outcome data to provide further valuable insight into and cross-validation of remotely captured patient outcomes
We created some demonstration compliance dashboards to show how the integrated data could be analysed routinely in the context of risk-based monitoring, safety monitoring, protocol compliance and subject engagement.
The dashboards display red/amber/green (RAG) icons for wear time, activity, sleep and heart rate, based on configurable compliance or key risk indicator rules, at both subject and site levels. They also display a weighted, balanced scorecard RAG icon across all categories, again based on configurable rules. We augmented the dashboard with maps to provide geographical context of overall compliance risk at the site level.
The value of this approach in a clinical trial is the ability to track in real time or near real time parameters that could impact patient safety, the quality and variability of the data being generated and patient engagement.
It is self-evident that ensuring that subjects wear the devices and sensors for the period of time required to generate a valid data set is a key success factor. Dashboards that track wear and non-wear can support early intervention and engagement, optimising the utility of mHealth technologies.
It is clear that to the creation of a patient centric trial design, where a patient can be monitored in a home environment for all of the outcome measures required by a protocol, multiple devices may be needed. This poster outlines the framework we implemented to securely collect clinically relevant data from multi-vendor devices into an informatics hub. This approach allows us to measure clinical outcomes objectively and remotely from patients in non-clinical settings going about their daily lives.
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