Data-Sharing Important but Difficult to Implement, Survey Says by eClinical Solutions

April 2020

A CenterWatch Publication

Volume 27, Issue 04

Data-sharing Important but Difficult to Implement, Survey Says By Brandon May

he clinical trials industry agrees on the importance of data-sharing, but few companies are willing to invest the time and money it takes to create data integration partnerships. In a new survey from the Tufts Center for the Study of Drug Development and eClinical Solutions, companies reported that hammering out business agreements with data partners and working to find common ground on the mechanism of data transfer as well as the format of those data can take too long. Approximately 30.5 percent of respondents reported that setting up these relationships with data providers — organizations or entities (such as sponsors of the study) that typically provide services or systems responsible for optimizing the study and submitting trial data to clinical trial registries and databases — would be too time-consuming and labor-intensive and would also be least suitable for automation. “That’s pretty consistent with the anecdotal reports of challenges we hear for qualifying vendors that are new to the clinical research enterprise,” says Ken Getz, director of sponsored programs and an associate professor at Tufts University School of Medicine. Although survey respondents indicated that data integration was the least time-consuming of all the challenges noted, 28.2 percent and 24.1 percent of respondents said that

performing data review and cleaning as well as transforming and mapping data, respectively, would be too time-consuming or labor-intensive. The survey of 149 companies in fall 2019 found that the vast majority of companies agree on the importance of integrating clinical trial data from multiple applications into a streamlined system. Most companies report having a high number of diverse data sources: approximately 87.3 percent of companies, for instance, reported using non-case report form data, while 74.8 percent reported using direct data capture and 69.2 percent reported using data from devices and apps. Less than half (43.4 percent) reported using electronic health record/ electronic medical record data. “Despite the overwhelming agreement on clinical trial data integration,” says Getz, “many companies still rely on disparate platforms to collect data.” The reliance on several different data sources, he added, can lead to inefficiencies in managing the performance of clinical trials at all levels. The use of holistic tools that can aggregate and integrate study data, rather than having to compare trial data across several different platforms, may help with the data integration challenges, says Getz. Clinical data hubs are currently being used by many companies to organize and integrate data from devices and apps, medical images and electronic health records. A clinical data hub includes three

main components: a data pipeline; automation; and analytics. Source data flows from the data pipeline like electricity into the automation phase, which imports and maps the data. Ultimately, this moves into the analytics component, offering insights into these data. More than half (54.5 percent) of companies reported using a clinical data hub/repository for integrating and analyzing data from devices and apps, whereas 53.7 percent reported using these hubs for data from medical images. According to the survey, the combination of a data strategy plus a data hub may enable more advanced capabilities, including helping companies move toward more predictive and descriptive analytics. Data providers are often small specialty labs, including labs that perform biomarker assays, niche diagnostics and esoteric testing of rare substances or molecules that are not routinely tested in a standard clinical lab. Specialty labs often have a narrow yet sophisticated technical focus. While these labs have the responsibility to ensure that submitted data are complete and accurate, these labs are typically new to clinical research with little to no processes in place to share real-time data with companies quickly. In terms of the lab data and the distribution of clinical research data being analyzed, approximately 60 percent of survey respondents said they relied on

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central or local labs. Central labs, which are solely responsible for laboratory assessments for a clinical trial, represent the gold standard for ensuring lab results data are standardized, consolidated and harmonized across different patients and sites. Comparatively, local labs are typically on-site or in close proximity to the investigator site and are generally used when data are urgently needed for randomization or treatment. Sheila Rocchio, chief marketing officer at eClinical Solutions, says another goal that companies should reach for in their data strategy is to move to an artificial intelligence (AI) data hub, which can help build different types of algorithms that augment decisionmaking with clinical, medical and data expertise. An AI hub, according to Rocchio, can help reduce the labor-intensive manual processes that are part of data management. Rocchio recommends to sites that they automate more data-related activities as much as possible. “Leveraging technology platforms and data hubs that deliver

data pipelines and map data to standard formats is possible at both the study and enterprise levels,” said Rocchio. Doing this may reduce reliance on “brute force” manual efforts that use either Excel spreadsheets or statistical analysis software to integrate data at the end of a trial, she added. While autonomy is integral to a smooth-running data analytics operation, Rocchio said that respondents didn’t consider full analytic autonomy of high importance. Only 17 percent of respondents said they want end-users to modify and publish their own analytics, whereas 41 percent said they didn’t want end-user autonomy. Organizations that reported having data strategies or data hubs self-rated their analytics and data science competencies as being more advanced than organizations that did not have established data strategies or hubs. For instance, 75.7 percent of companies that had analytics dashboards in place self-rated their data science competencies as being mature, whereas only 30.9 percent of companies without analytics dashboards believed their competencies were advanced. “The majority of

companies believe their data sciences are nascent within their organization,” says Getz, “with a higher prevalence of data sciences associated with company size.” Based on these survey results, defining an enterprise data strategy may be crucial for ensuring seamless data integration and analysis throughout a trial. Companies in the survey that had a clear definition of their data strategy appeared to have better cycle times as well as better perception of the difficulty related to common data management tasks compared to companies that did not have a defined strategy. Rocchio urges organizations not to the let the costs associated with new technology prevent them from beginning an enterprise data strategy. “This is not a ‘nice-to-have activity,’ but a strategic imperative,” she said. “A data strategy defines the data goals of your organization, how data flows from collection to analysis, what kind of analytics are being used and for what decisions, how data quality and integration is managed, and what technology infrastructure is used and needed both today and in the future.” To read more about the survey, click here: https://bit.ly/3d9wmmD.