How Can You Bring Analysis-Ready Clinical Trial Data to Your Fingertips?

Time spent preparing clinical data for analysis could be better spent delivering insights to inform decisions, yet a highly variable mix of sources and formats often mean that the data preparation process is slow, complex, and error-prone. Modern software tools can resolve many of these challenges, and create a unified, centralized resource, ready for you to explore.

Oftentimes, clinicians and trial organizers struggle with timely access to data held in multiple repositories, and face issues around quality, complexity and format. To address the problem, the pharmaceutical sector has invested large sums in data warehouses and data lakes, but despite spending significant sums, data management clearly remains a serious challenge for non-specialists.

Some of the difficulties themselves stem from prior approaches. Manual solutions for data extraction, such as custom programs or scripts, can be costly to develop, deploy and maintain. A network of point-to-point data exchange services soon becomes inordinately complex as new sources are added, and the lack of centralized management can hinder or totally prevent team collaboration and efficiency.

For example, a cross-study analytics project to review drug safety information across a portfolio of oncology products would naturally need access to clinical data from multiple trials. The combination of different protocols, cohorts, data gathering, and formats soon lead to ballooning complexity as each trial is added to the project.
In short, solving – or at the least, reducing – the data management challenge(s) would immediately release time for analytics, enabling highly qualified clinicians to focus on what really matters: delivering insights to inform clinical and operational decisions.

Closing the Data Prep Gap

To reduce the time preparing clinical data for analytics means removing manual processes and instead automating data management wherever possible. The key is to enable a centralized information hub, acting as a single, unified resource. This change of architecture removes the complexity and tedium of manual point-to-point connections, and embeds a simple structure that provides self-service access to robust, reliable data.

Signals Clinical provides exactly this information architecture. The input sources can be almost any data type, from flat files, EDC, and multiple formats, with automated collection from a wide range of industry-standard electronic data-capture solutions. The centralized data hub enables clinicians to spend less time on data prep and more time on analysis, opening up the path to new insights and emerging clinical signals.

In addition, Signals Clinical is integrated with Spotfire®, acknowledged to be one of the best-in-class visual analytics solutions. Clinicians can use the self-service point-and-click simplicity of Spotfire to analyze data already held in Signals Clinical, using the intuitive visualization capabilities to spot patterns, explore anomalies, and test new ideas.
The cloud-native Signals Clinical platform is delivered as Software-as-a-Service (SaaS), removing on-site deployment delays and eliminating capital expenditure. The web delivery model provides continuous application updates and enhancements, offers near-limitless capacity and scale, and enables teams to devote more time to mining data for clinical insights.

Real-World Clinical Outcomes

The centralized, unified and integrated approach embodied by Signals Clinical delivers real-world outcomes. For example, a systemic analysis of trial data, which is often prompted by a clinician’s hunch, becomes a realistic possibility within a manageable timescale. Collaboration requests from the extended team can be handled from inside the solution, ensuring all participants are informed and are able to act on the same information.

Signals Clinical bridges the gap between access to timely data and the rapid delivery of results. By easing and automating the data preparation and management process, Signals Clinical brings discoveries to your fingertips.

Take the First Step

To find out more about how Signals Clinical streamlines data workflows and reduces the time spent preparing data for analytics, click here.

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Mark Weadon

Clinical Analytics Product Marketing Manager

Mark Weadon has been active in clinical development analytics and visualization for over 20 years. Mark currently serves as the Clinical Analytics Product Marketing Manager at Revvity Signals. Mark has thirteen years of pharmaceutical industry experience at GlaxoSmithKline. Mark pivoted into life sciences product marketing with roles at SAS Institute, IBM, Definitive Healthcare, and Revvity Signals. Mark holds an MBA from Elon University and a BA from Duke University.