Top Four mHealth Advantages for Clinical Trials

Note: The following situation is imagined. The response to this kind of situation is real.

The Situation

The patient had an aggressive form of lung cancer and traditional treatments weren’t working. His doctor recommended him for a clinical trial that was testing a new therapy. Initially he turned down participation.  The problem was he had trouble breathing when he went to out to buy groceries. The idea of traveling to a distant trial site on a regular basis filled him with fears of an ongoing, exhausting situation that was possibly fraught with additional obstacles he couldn’t even imagine.

The Response

Patient Centricity & Site-less Trials

The research team had a solution to the lung cancer patient’s problem.  They gave him a wearable wireless sensor that allowed him to participate in the trial from his living room. This made study participation easy and seamless, and because he didn’t face obstacles; the patient stayed with the study until it was completed.  The sensor connected by a mHealth application to his phone sent his vital signs via Bluetooth to the cloud, which connected the data to a clinical trial application that organized, managed, and analyzed it for the study.

mHealth, the use of wireless technology and wearable sensors, makes prioritizing patient needs in clinical trials, or “patient centricity,” possible. Wireless apps like Oracle Health Sciences mHealth Connector Cloud Service  can monitor patients remotely on an ongoing basis and upload their data to the cloud.  Rather than a once a month “site visit snapshot,” the continuous tracking of vital signs (such as blood oxygen, blood pressure, temperature, etc.)  can provide a truer picture of response to a therapy with real time data on adverse events.  Due to its effortlessness for the patient, mHealth methodology may also lower the trial dropout rate. 

Remotely monitoring a patient’s vital signs multiple times a day, combined with collecting pain scores via a mobile app, could also lead to identifying new digital biomarkers that could accelerate disease understanding.

Fast Access to eSources and Trial Scalability

The point of a clinical trial is to evaluate the safety and efficacy of a new drug for the market by monitoring patients' reactions to the new therapy.  However, the trial’s population group may have limited participant criteria that don’t represent all potential users. The relatively new availability of big data eSources (such as drug safety report databases, EMRs, and other medical literature) can expand and amplify the information on patient reactions/adverse events developed during the trial. But, incorporating and analyzing these additional terabytes of eSource data can pose major technology challenges for clinical teams.

Oracle’s mHealth Cloud Service makes it easy for researchers to scale the trial, as well as acquire, monitor, and distribute those terabytes of real world data via a single platform in near real time.  Using a federated metadata approach, the app delivers the data to other cloud based eClinical capabilities for data capture and management  to gather, store, clean, organize, and prepare in comparable formats for a variety of advanced analyses, visualizations, and use cases offering enhanced trial results. In addition, as new digital workflows mature within an organization, they can be quickly deployed at scale across many trials using the same mHealth Connector platform, without the need for complex integration projects.

mHealth has the potential to change the face of clinical trials. It is ushering in a new and exciting era of site-less trials that enable remote patient participation and continuous monitoring for more accurate insights. With its seamless ability to include third-party big data eSources at the click of a mouse, mHealth also offers the promise to enhance trial results, accelerate the regulatory process, and speed life-saving drugs to market faster.

Learn how the Oracle Health Sciences mHealth Connector Cloud Service can empower your trials with big data support and make your studies more patient centric.

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