Collecting, processing and analyzing the ever-growing volume of online data has been one of the main forces driving advances in IT platforms for clinical trials. The wealth of data that can be obtained through everything from clinical sources to patient-worn sensors can help investigators identify potential trial participants, discover potential adverse events, and even provide data to regulators to support approvals for new indications.
As they look to incorporate this vast pool of information, clinical trial managers have found that legacy EDC technology isn’t up to the task. Incorporating the full breadth of data into the trial data set, including from mHealth devices, requires more advanced systems built to deal with all the challenges this trove of data represents.
Former Oracle Health Sciences General Manager Steve Rosenberg spoke about this need in a recent Clinical Leader Live event. Rosenberg noted that there is “definitely a big push” to incorporate patient-generated data in trials; in some cases, from consumer-level sensors, but particularly from medical-grade sensors and devices that produce more accurate and precise data.
There are a plethora of such devices that capture information about things like movement, sleep, blood glucose levels, oxygen saturation, and more – information that has significant potential clinical value. These devices can be worn or used by patients at home or during their daily routines to allow continuous monitoring. They also can be at the clinic itself and make the collection of routine information easier and faster. Being able collect and process data from mHealth devices at the point of action is very valuable, Rosenberg said, “and to the extent it can be made convenient, the more important it becomes.”
Something as simple as the familiar question “how have you been sleeping?” illustrates the value of this data, Rosenberg said. When a patient answers the question in a clinic, or even on a written diary at home, chances are the information will be vague or imprecise – “I didn’t sleep well” or “I felt tired when I got up.” A person may not even remember waking up during the night, or may not be able to remember how they slept on previous nights.
If the patient was using a sleep tracking device, though, researchers would have much more precise information about the patient’s sleep patterns and issues. That could be critical to studies where sleep is a primary endpoint, but it also applies to a growing number of studies where quality of life is a secondary endpoint. That’s the case in many therapeutic areas, Rosenberg said.
Because of the increased need for incorporating this data into trials, Oracle Health Sciences has built a complete infrastructure within its Clinical One platform that supports the collection of mHealth data. The platform is now able to provide that information to trial managers along with a complete lineage of the data to understand where it came from and how it was collected.
The value of such an infrastructure becomes clear when you consider the significant challenges the range of mHealth devices pose. As Rosenberg noted, there are many different device vendors and hence just as many different ways the data is provided. This creates provisioning problems for companies who want to decide if there are enough benefits to employing a given device, or even if it can be used in a particular country, as well as if data can be collected reliably. That puts the onus on software providers like Oracle to sort through the challenges and make it easy for customers to make the right decisions.
“As this data becomes more ubiquitous and more available for use, it’s up to us tech vendors to make sure it can be gotten,” Rosenberg said. “It’s not up to us to say what it’s good for, but it is up to us to make sure you can get it.”
You can listen to Rosenberg’s interview here. We also invite you to download a white paper exploring the role EDC plays in today’s clinical trials and the importance of new technology platforms in enabling its effective use.