Using RWD to bridge gaps in trial access

By Maggie Lynch

- Last updated on GMT

(Image: Getty/whuwhanfoto)
(Image: Getty/whuwhanfoto)

Related tags RWD trial transparency Clinical research Clinical research services Elligo Health Research Data collection Data management

Integrated research organizations aim to bridge the gap between clinical research and clinical health care with the use of RWD, yet, the data may not be ready for research, according to Elligo Data Scientist.

Integrated research organizations enable expanded access to clinical research by bringing together health care data, community physicians, and patients in one place.

We spoke to Chad Moore, CEO of Elligo Health Research, alongside Michael Ibara, the company’s vice president of data science before their upcoming presentations at the Bridging Clinical Research and Clinical Health Care conference. Both will speak on the use of data and how integrated research organizations can “close the gap”​ between research and health care.

Elligo Health Research, an integrated research organization, addresses the challenge of patient enrollment​ by bringing research to the physician’s office​.

Ibara told us that real world data (RWD) is used every day “to find patient’s best suited to clinical trials, so that we know when we find a patient it’s not a guess – we know based on their clinical data that they are good candidates. So RWD has a long history of making an impact, and continues to add value in new ways.”

Finding trials best suited for patients through this type of information can limit the sacrifices patients may have to make to participate in clinical trials, like leaving their own physician or traveling long distances.

“When patients learn about a clinical trial through the trusted relationship with their primary physician and/or participate in a trial in an office close to home, not only does patient enrollment and retention rise but the study’s engagement and data quality increase as well,”​ Moore explained.

Creating virtual patients

When engagement and data quality increase, Moore said researchers are able to meet the “evolving needs of the drug discovery industry – from prioritizing the patient voice to supporting better health outcomes.”

RWD is raw information including claims, clinical, registry, and electronic medical record information, all of which can impact trial design and protocol, which in turn can improve trial access and patient recruitment.

“There are tons of innovative approaches in this area, to new types of data sharing networks in academia, to new ways to create and use registries, novel data collection schemes in clinical research, to collecting data directly from the EHR for running research,” ​Ibara explained.

The US Food and Drug Administration (FDA) is currently working with researchers to find ways to benefit patients by using already generated information through its recent guidance​ addressing EHR data use in clinical trials.

Outside of the technological advancements that have enabled RWD to become a major trend in clinical research, is the application of these innovations to enable EHR data to be used without bias. Using these innovations in tandem with statistical approaches could create what Ibara refers to as a ‘virtual patient.’

This ‘virtual patient’ could be used as a point of reference in clinical trial design, protocol, and research, enabling the placebo arm to be eliminated – which is imperative in trials where the use of placebos is unethical, or to match patients to studies based upon previously collected health care information. Yet, innovations are not yet at a place to create this ‘virtual patient.’

Not yet ‘research-ready’?

Still, RWD is not without its problems, said Ibara, “The way I put it is that RWD is fit for care, but not yet fit for research. It's very important to remember that there is an important, often very long, and sometimes very difficult process to bring 'raw' health care data to the point where you will be able to use it to perform research.”

By fit for care, Ibara explained that RWD was created to help patients and ensure that health care information was streamlined.

Despite the issues, Ibara said that there is work being done to make RWD “research-ready”​ by standardizing, curating, and removing bias. One of these solutions has been the application of machine learning.

“The application of machine learning to this type of data is truly going to produce fundamental breakthroughs, but not without real, slowly, methodical working through of very complex and challenging issues,”​ he explained.

“But in the end, I believe this work will eventually usher in a new era of better clinical insights, more efficient clinical studies, and better treatments and outcomes for patients.”

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