Eversana, Thread connect RWD and decentralized trial platforms

By Nick Taylor

- Last updated on GMT

(phuttaphat tipsana/iStock via Getty Images Plus)
(phuttaphat tipsana/iStock via Getty Images Plus)

Related tags Decentralized trials RWD Real world data Clinical trials software Thread Eversana

The two companies, along with Datavant, will combine real-world data and decentralized platforms to yield better patient information management and insights.

Thread and Eversana have teamed up to connect their real-world data (RWD) and decentralized clinical trial platforms. Working with Datavant, the two companies have connected their existing platforms to provide a fuller overview of the health of the patient.

Brigham Hyde, president of data and analytics at Eversana, explained the need to bring down the silos that have traditionally separated RWD from clinical trials results.

In the evolving world of RWD, registries, and clinical trials, it has become increasingly likely that a patient could end up participating in all three through the course of their care journey. This raises the question of how to think about this journey holistically and maximize the research value that can be created by thinking of these three mediums along a single continuum. In addition, at the backbone of this convergence are the concepts of consent, identity management, and research ethics​,” said Hyde.

Eversana, Thread, and Datavant responded to the convergence by bringing together their platforms. The collaboration combines Eversana’s RWD platform, Datavant’s de-identification technology, and Thread’s decentralized trial platform.

The convergence enables the secure management and connection of patients through the mediums. In doing so, Eversana and its collaborators are combining active, prospectively collected information such as patient-reported outcomes with electronic medical records and claims data.

Brigham Hyde, president of data and analytics, Eversana

What this allows for is a dataset that can enable multiple analysis types from a single study effort. It reduces the burden of data collection, while also keeping consent and identity management at the center of the process, putting the patient in charge of how their information is managed. The effect to this is research and evidence acceleration throughout the lifecycle, leading to better cures for patients and increased evidence for care​,” said Hyde.

Eversana envisions the combined platform reducing the data collection and security burden on sites and participants. Datavant is providing data linkage technology to support patient privacy and data security.

Other claimed benefits included reduced registry and startup costs. Eversana pitches its platform as a way to increase evidence generation earlier in the launch cycle and sees additional benefits from the Thread connection.

The combination with Thread’s decentralized trial platform lessens the burden of trial start-up costs and site-centric data collection. This adds efficiency to the deployment of trials and registries, as well as increasing access to patients and collection of critical clinical endpoints​,” said Hyde.

Related news

Show more

Related products

show more

Saama accelerates data review processes

Saama accelerates data review processes

Content provided by Saama | 25-Mar-2024 | Infographic

In this new infographic, learn how Saama accelerates data review processes. Only Saama has AI/ML models trained for life sciences on over 300 million data...

More Data, More Insights, More Progress

More Data, More Insights, More Progress

Content provided by Saama | 04-Mar-2024 | Case Study

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data in a single location

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Related suppliers

Follow us