Why Every Organization Needs a Clinical Data Strategy

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February 17, 2021

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The different types and sources of data produced by clinical trials have been expanding in recent years, creating new challenges for consolidating, standardizing and analyzing the trial results. The tools for integrating data analysis methods have not kept pace with the advances in research technology, causing time delays and inefficiencies in the clinical trial process. Optimizing data review and analytics, together with establishing an efficient clinical data pipeline, is essential for streamlining the clinical research process.

Organizations need a clinical data strategy to handle disparate data sources and shorten clinical trial timelines. Choosing the right clinical data platform can be a transformative step in delivering more representative trial results.

To learn more about developing a clinical data strategy using purpose-built software, watch this free webinar presented by eClinical Solutions, a leading provider of cloud-based enterprise software and software-driven clinical data services.

Clinical Data Pipelines and Strategies

In 2019, eClinical partnered with the Tufts Center for the Study of Drug Development (Tufts CSDD) to investigate the state of data strategy and transformation in organizations that run clinical trials. The study is published in the Drug Information Association (DIA) publication, Therapeutic Innovation & Regulatory Science. A striking result was that the average life sciences company was using more than four sources of data in their clinical trials.

A 2017 study also demonstrated a 40 percent increase in the last patient last visit (LPLV) to database lock cycle time metric for companies that were using five or more data sources. Additionally, the more data sources that were used, the longer the cycle time tended to be. There was a strong correlation between data sources and cycle time, likely because the tools to manage the data have not kept pace with the increasing number of data sources and the variability between them.

Many organizations rely on software, such as SAS and Microsoft Excel, to integrate data and map it to the desired format. This often results in excessively complicated spreadsheets or a dependence on the use of programmers to manage the data. Both of these strategies can lead to a “black box” approach, where sponsors are unable to provide the details of their data analyses to their stakeholders. It is also time-consuming and labor-intensive, due to the over-reliance on the manual processes of data analysis, integration and review.

Sheila Rocchio, the Chief Marketing Officer of eClinical Solutions, suggests that all companies participating in clinical trials should have a clinical data strategy. “Some of the survey questions asked organizations whether or not they had a clinical data strategy, and in response, we have been asked many times, ‘What is a clinical data strategy?’” Rocchio says. “It’s really a roadmap for a definition that is shared throughout an organization about what data is collected, the flow of data through the different systems, and how the data is governed in the organization,” she explains.

According to Rocchio, the survey conducted by eClinical and the Tufts CSDD found that members of organizations that did have a defined clinical data strategy perceived many tasks as less challenging, labor intensive and time consuming than those in organizations that did not. They also had lower LPLV to database lock cycle times, even with higher numbers of data sources, and they rated their competencies on analytics maturity curves as much higher than their counterparts who did not have a strategy. These companies had more advanced analytics capabilities, including the ability to integrate the principles of data science and artificial intelligence into their data analyses.

The purpose of a data pipeline is to automate the flow of data and to have an infrastructure set up for the data, so that it is ingested from the source without excessive reliance on manual components. A data pipeline also involves having standards for the data format and establishing the ability to integrate data from different sources.

Having the ability to source data from multiple platforms, including from past and ongoing clinical trials, as well as having the capability to combine these many disparate types of data, are the first steps in developing predictive and descriptive analytics for the results analysis of any given clinical trial. The survey conducted by eClinical and the Tufts CSDD demonstrated that organizations who did have a data pipeline strategy and a clinical data platform also had greater capacities for predictive analyses and data review.

The elluminate® Clinical Data Platform

The elluminate Clinical Data Platform by eClinical is a core technology that automates the clinical data pipeline and enables advanced data analytics using data science, machine learning and artificial intelligence. It begins with the sources of data inputs. As trials become more complex and more targeted, data is being sourced from multiple systems, including electronic data capture (EDC), clinical trial management systems (CTMS), electronic patient reported outcomes (ePRO), interactive response technologies (IRT) and wearables.

All of this data comes to elluminate in a number of different formats. The greatest challenge of a data pipeline is to aggregate all of it. As elluminate is an agnostic system, it can ingest data from any source, including Excel spreadsheets, SAS datasets and comma-separated values (CSV) files. There are a number of pre-built application programming interfaces (APIs) to choose from, including financial systems and CTMS. The elluminate system can also ingest data from secure file transfer protocol (SFTP) servers.

Trial Complexity Demands a Flexible Clinical Data Strategy

As the volume of data generated by clinical trials continues to increase, along with the number of different types and sources of these data, a distinct clinical data strategy and pipeline are essential for any organization conducting clinical trials. This not only reduces cycle times, which can lower operational costs and bring products to market faster, but it also enables the organization to utilize advanced analytics and machine learning when interpreting trial results or conducting data reviews.

eClinical’s elluminate software and services platform enables organizations to develop their clinical data pipeline with ease through pre-built APIs and out-of-box reports that are already established for clinical data analytics.

To learn more about the elluminate Clinical Data Platform, register to watch this free on-demand webinar.

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