\nOnce data has been aggregated, data review often takes place in silos. Reviewing \u2018offline\u2019 listings from SAS or similar programs takes a lot of manual effort and can result in inconsistencies. The biopharma’s head of eClinical programming describes the challenge when errors occur: \u201cHaving to get our programmers to rerun their programs for SAS listings\u2026you don\u2019t want to waste their time on that. We can much better utilize their time.\u201d\n<\/p>\n
\nVeeva DQS is a clinical database that is designed to address these challenges, by automating data aggregation, harmonization, and cleaning.\n<\/p>\n
Enablement and process optimization<\/h2>\n
\nThe late-stage biopharma started using Veeva DQS in 2023, and at the time were fully outsourced with a CRO. The company moved to a more hybrid model following implementation, working with Atorus as a functional service provider (FSP). After consultation with Veeva, the biopharma decided to take an iterative approach to implementing Veeva DQS, with two studies currently rolled out and a third underway, with several listings and auto checks in production. \u201cIt\u2019s nice we can use an iterative approach \u2014 you can prioritize the data you most want to take advantage of Veeva DQS for, and get the experience of getting things into production while you expand,\u201d explains the head of eClinical programming.\n<\/p>\n
\nThis form of enablement allows the programming teams at the biopharma to learn the system and optimize processes for the next batch. The Atorus team is embedded within this model: \u201cOur staff feel like they are part of the team,\u201d says Richie. \u201cThey are part of the enablement and learning more about Veeva DQS. Our programmers and data managers are learning directly from the biopharma team .\u201d When introducing a new system, managing the required changes to internal processes can be daunting. To facilitate this, Veeva provides guidance on the required updates to existing standard operating procedures (SOPs). As part of enablement, the biopharma has used Veeva\u2019s templates and documentation to update its processes. The biopharma\u2019s head of eClinical programming explains: \u201cThus far, we\u2019ve worked through Veeva\u2019s process, which has been great. Now we\u2019re going to use that to help us update our processes internally and decide how we want to document things and move forward.\u201d\n<\/p>\n
\nAs the late-stage biopharma launches new studies using the clinical data workbench, they plan to onboard data providers in Veeva DQS to remove the need for offline tracking of data issues. Their head of eClinical programming notes how the change in process so far has been minimal for data providers: \u201cIt has been great having them be able to provide that data directly, and it\u2019s as simple as how they were providing it previously.\u201d\n<\/p>\n
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Minimizing manual tasks with automation<\/h2>\n
\nThe first challenge the biopharma wanted to address was aggregating their large volume of trial data. \u201cThis is a really good use case for Veeva DQS,\u201d notes their head of eClinical programming. \u201cHaving that data in Veeva DQS aggregated with our EDC data makes the review much simpler and faster. That\u2019s been really helpful for us,\u201d they comments.\n<\/p>\n
\n\u201cHaving that data in Veeva DQS aggregated with our EDC data makes the review much simpler and faster. That\u2019s been really helpful for us.\u201d
\n– Head of eClinical programming and solutions, late-stage biopharma <\/span>\n<\/p><\/blockquote>\n\nHaving a centralized clinical workbench means the biopharma\u2019s team can reconcile and review data from all sources in one location. Veeva DQS\u2019s change detection feature automatically identifies data that is new or changed, allowing teams to avoid spending time on data that has already been reviewed: \u201cIn Veeva DQS you can see exactly what\u2019s changed, and that really helps,\u201d says the head of eClinical programming.\n<\/p>\n
\nThe team also uses Veeva DQS\u2019s auto checks feature to automatically identify errors and create and close queries. The head of eClinical programming outlines the impact this has had: \u201cWe have eliminated about 3600 manual queries that we were previously carrying out, which has saved us around 300 hours of manual effort.\u201d Listings and auto checks refresh hourly, meaning the Atorus team can access clean aggregated data with fewer transformations and less data lag. \u201cOur SAS programmers really find value,\u201d notes Richie. \u201cBy using Veeva DQS, they have cut 90% of the need for SAS listings.\u201d\n<\/p>\n
\n\u201cWe have eliminated about 3600 manual queries that we were previously carrying out, which has saved us around 300 hours of manual effort. Batch querying is one of our data manager\u2019s favorite features of Veeva DQS.\u201d
\n– Head of eClinical programming and solutions, late-stage biopharma<\/span>\n<\/p><\/blockquote>\n\nThe ability to open and close queries in batches has also saved teams a lot of time: \u201cBatch querying is one of our data manager\u2019s favorite features of Veeva DQS,\u201d notes the head of eClinical programming.\n<\/p>\n
\n\u201cBy using Veeva DQS, SAS programmers have cut 90% of the need for SAS listings.\u201d
\n– Head of eClinical programming and solutions, late-stage biopharama<\/span>\n<\/p><\/blockquote>\nImproving access to complete and current data<\/h2>\n
\nFor Richie\u2019s team at Atorus, working with \u2018offline\u2019 data produced a lot of challenges. Data managers would need to wait for programmers to re-run listings, whose time could be more optimally spent on other activities. Listings refresh automatically on the latest data when they are opened in Veeva DQS or exported downstream, saving programming resources and giving data managers better visibility of available data.\n<\/p>\n
\nRichie outlines the advantages of this: \u201cVeeva DQS allows us to do more real-time data cleaning and move checks upstream. Having the case report form (CRF) and external data in one place adds visibility and provides that holistic picture. That has definitely given some time savings, and has been a great tool for data managers.\u201d The filtering and sorting capabilities in Veeva DQS also give data managers the same options to manipulate data in the ways they would have done previously in Excel spreadsheets.\n<\/p>\n
\nHaving a unified set of complete and concurrent data that can be easily accessed opens the door for partners to create custom views that are tailored for downstream audiences. Leveraging Shiny, Richie aims to use data from Veeva DQS to create targeted visualizations for downstream audiences. This will allow analytics and engineering teams to deliver the right reports to stakeholders based on what they need to see for their individual deliverables and objectives, using data that has already been aggregated and cleaned in Veeva DQS.\n<\/p>\n
Discovering untapped potential<\/h3>\n
\nThe biopharma and Atorus are already thinking of ways to expand their use of Veeva DQS\u2019s capabilities to unlock further efficiency.\n<\/p>\n
\nThe biopharma sees additional opportunities for automation. They currently use Veeva DQS\u2019s ability to pull coded information from the medical dictionary to create auto checks for prohibited medications (something that would not be possible using medication verbatim names entered by sites into EDC). Today, this check generates a query when a prohibited medication is identified. But the biopharma\u2019s head of eClinical programming sees potential to extend the use case to automate identification of protocol deviations in the future. \u201cThat\u2019s a great place where we can expand our usage,\u201d they comment.\n<\/p>\n
\n\u201cWe\u2019re able to do a lot of complex things, and I\u2019m sure there\u2019s even more,\u201d the head of eClinical programming notes. \u201cThat\u2019s one of the great things about Veeva DQS \u2013 we\u2019ve done a lot, but there\u2019s so much more that we can do. We\u2019re definitely looking forward to expanding that,\u201d they concluded.\n<\/p>\n