Integrating Real-World and Clinical Trial Data to Improve Patient Outcomes

August 29, 2022 - In a world where remote patient monitoring platforms, devices, and tools are becoming increasingly popular for managing several chronic conditions such as diabetes, researchers are looking to integrate real-world data (RWD) into randomized clinical trials (RCTs) and to analyze RWD alongside RCT data. Their goal is to boost clinical trial efficiency, reduce the cost of clinical studies, and gain more insight into the effects of therapies on patient outcomes.

According to Glooko Vice President of Data Science and Clinical Research Ed Nykaza, PhD, RWD and RCTs are mutually complementary. With a daughter diagnosed with type 1 diabetes in 2009, he understands the struggles of dealing with chronic diseases.

Nykaza describes real-world data as “patient data captured in a real-world setting, which could come from a variety of sources, such as medical devices, wearable biosensors, electronic health records, and web and mobile apps. This includes device and sensor data, patients’ use of apps, and patient reported outcomes.”

RWD can validate efficacy-based outcomes observed in RCTs, as well as provide supportive evidence to inform the design and operations of future clinical trials. However, as with any emerging field, there is a gap between the promise of RWD analysis and the current state of their use in randomized clinical trials. Additionally, the quality of data collected from real-world situations can vary based on the device, the user, and the method of sharing with clinical trial teams, Nykaza observes.

Benefits and Limitations of Real-Word Data

Interest in real-world data has grown as the healthcare industry continues to implement electronic systems to better document and coordinate care.

“Real-world data collection using wearable technologies has a significant advantage over traditional clinical data methods in healthcare as it captures data from more diverse patient populations as they traverse the ‘real’ world, naturally,” Nykaza maintains. This type of data — compared to data obtained in RCTs — can be observed over longer periods of time and can provide more frequent, granular observations than the typical data collection approaches used in RCTs.

For most new treatments, a typical approval path would require one or more RCTs. “In that controlled RCT, one might see that a certain treatment is efficacious, but it ultimately may not produce the same results in the real world for a variety of reasons. Real-world data can provide insights and expose gaps to inform future technology and treatment developments,” Nykaza explains.

“In RCTs, the biggest costs are recruitment and dropout,” as failure to recruit and retain patients can cause costly delays. Approximately 30% of participants drop out of clinical trials, and on average, it costs $6,533 to recruit one patient to a clinical study, with the cost of replacing participants being even higher.

“Randomized clinical trials have their place in the industry but come with one large caveat: they’re set in laboratories or highly controlled environments, which may not capture important factors or confounding variables that make treatments more or less effective in the real world,” says Nykaza.

On the other hand, RWD has its limitations just like RCTs. For instance, internet connectivity issues, time zone changes associated with travel, and calibration problems are some common drawbacks plaguing older data-capturing devices.

“Luckily, some of these issues are older sensor problems that have been resolved in newer device models. However, devices that allow users to input data manually are prone to errors. Overall, the real-world data quality can vary by both device and user,” Nykaza observes.

“Most of these limitations can be overcome. Some happen over time as technology improves, and some limitations can be overcome with accepted data cleaning techniques. And in all cases, researchers have data quality assessment metrics to assess the quality of each individual signal they capture.”

Another common concern around RWD is data privacy, a top concern for Nykaza and his team.

“It is important for people to understand why the data is being collected in the first place,” he emphasizes. “Is the company using the patient’s information to sell to advertisers and boost revenue, or is the company looking to use the data to accelerate innovation and the adoption of new technologies that will benefit the users and community they serve?”

The answer to that question is vital to patients weighing RWD-capturing devices.

The proliferation of devices on the market can also create an administrative nightmare for patients, providers, and researchers alike.

“We need a single platform to organize, aggregate, and display all the patient’s data from different data sources. Having a single platform reduces the burden of switching between multiple device platforms, while facilitating better harmonization of data and improved data analysis,” Nykaza argues.

Impacts of Combining Real-Word Data and Randomized Clinical Trials

Combining RWD and RCTs to develop and validate new therapies and treatments could be the next step in the evolution of healthcare delivery and clinical research.

Although RWD and RCTs each have limitations, Nykaza believes a synergistic data relationship produces the most actionable insights. For example, when using real-world data to inform RCT operations, real-world data can more easily identify clinics serving patients that meet specific study requirements.

In most clinical trials, one major barrier is meeting enrollment quotas in a sufficient time frame to prevent premature trial closure. While most researchers are aware of untapped patient pools, a map of where to locate them remains elusive.

The extensive data pool generated by combining RWD and clinical trial results would significantly diversify user groups, patients, and clinicians, covering many socio-demographic dimensions. Additionally, this information has the potential to accelerate the process of effectively identifying and matching trial participants with the appropriate study to reduce the time and cost needed throughout the clinical process.

RWD can significantly help clinicians studying chronic conditions while reducing the burden of chronic diseases and accelerating the development of new technologies and treatments. Ultimately, Nykaza notes that the potential of RWD could help identify inequities and enrich virtually every step of the clinical trial process.

Although access to RWD is often hindered by patient privacy, regulatory restrictions, and disparate data structures, “getting real-world data into the hands of researchers and clinicians is a powerful first step,” he concludes.

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About Glooko

Glooko improves health outcomes of people with chronic conditions through its personalized, intelligent, connected care platform. Our proven technologies make lives better by revolutionizing the connection between patients and providers, driving patient engagement and adherence via digital therapeutics, and accelerating the speed of clinical trials.