How digital transformation is driving change in clinical trials
In 2012, clinical trials seemed to be struggling. A detailed look of clinical investigations found that out of the studies examined, only 25.1 percent met or exceeded their stated recruitment goals.
While it’s important to note that recruitment failure does not necessarily mean that these studies failed overall, it’s still a concerning statistic. But it’s also one that new technology is starting to change.
Identifying and attracting the participation of qualified patients for a research study is critical to effective recruitment, feasibility determination and the design of the study itself. The problem is that the percentage of a patient population that meets the criteria is often limited and, depending on the nature of the study, can be extremely small. This means qualified participants for a study could be inundated with too many requests, and the study authors could have difficulty obtaining a well-rounded and comprehensive cohort upon which to conduct their research.
But there is a silver lining to the cloud of uncertainty surrounding clinical trials. Today's digital transformation is providing new tools to identify cohorts of patients that are the right candidates for a study—candidates that might have been missed by traditional patient identification and outreach.
Effective use of machine learning and other forms of artificial intelligence (AI) enable navigating streams like EMRs and similar data sources to query for required inclusion/exclusion criteria and identify eligible patient cohorts. Also leveraging the power of Internet of Things (IoT) enables a broad category of wearable devices to collect information on everything from heart rates to glucose levels.
Massive amounts of data are also available from a wide variety of additional sources. This data is very useful in determining the eligibility criteria and identifying patients better than the traditional approach of manual identification.
Embedded within a clinical management system, AI algorithms then can sort through the vital data- sets—including anything from wearables data, to claims information and surgery outcomes—to identify cohorts of patients that are the right candidates for a study.
This type of customized analytics can answer questions, such as, how many patients between 2 and 15 are currently being treated for childhood leukemia? Or, how many patients in the last 5 years are diagnosed with hypertension, but are not currently taking medications as a treatment?
Once the right patients are found, mobile technology can be deployed as part of a highly effective patient outreach and engagement program. Highly interactive presentations can show patients exactly how they will benefit from a study, and once becoming a participant, they can track their health progression right through their mobile device.
The Louisiana Public Health Institute (LPHI) is providing clear evidence of just how powerful digital transformation can be in a clinical trials setting. Through the Clinical Data Research Network (REACHnet), this non-profit has unveiled targeted patient engagements that use an app suite to deliver educational and research content.
The system encourages patients to engage with studies through visual cues and analysis on items such as current clinical evidence and treatment options to show the patient how the study is moving forward, and how this progress could impact them. Patient have their own portals to share data and access information, and receive information on the opportunity to participate in new studies.
Study data is collected at various sites and associated with a Global Patient Identification (GPID) system to match patients for studies while protecting their personal information. These patients can then have specific engagement content delivered to them based on their GPID data. With so much data now available across healthcare systems, comparative effectiveness research can also be run to help physicians and patients find the right treatment options for any unique health condition.
LPHI is a very encouraging example of the right way to apply technology to embed entirely new levels of performance into clinical trials. But obstacles still remain in the overall industry. The biggest challenge is that traditional clinical trials management systems keep their data locked away in silos.
In the short-term, this existing independent data can still be run through automated processes and be used to improve patient targeting and recruitment. But to really unleash the power of a true digital transformation, all systems must ultimately employ interoperability with data available in the cloud to enable the information they collect to become part of a worldwide network that benefits researchers everywhere.
Digital transformation is driving incredible innovations in how we match patients to studies and track their progress, and providing us with solid clinical evidence and understanding of widespread medical conditions, as well as insights into the rarest of ones. Leveraging IoT, AI, clinical management systems and mobile technologies the path to greater understanding is much shorter, and the road to better patient outcomes far closer.