CTMS Data Integration Approaches: Learnings from Four Top 20 Pharmas
CTMS is often referred to as the heart of clinical operations, connecting to several upstream and downstream applications such as EDC, IxRS, portfolio planning systems, accounts payable systems, and enterprise data lakes. This can be very complex, so architecting the right integration strategy is critical when implementing a modern CTMS.
I recently discussed this topic with four top 20 pharma companies and I wanted to share their integration approaches and learnings for those who might be considering a move to modern cloud CTMS.
Data Lake Pilot Program
One of the top 20 pharmas had a pilot program across two phases:
In R1, pilot studies went to Vault CTMS and non-pilot studies went to the legacy CTMS to leverage existing downstream integrations. A connection was built between Vault CTMS and the legacy system to filter the data. In R2, the data filter was deprecated, and all data now flows from Vault CTMS to the data lake.
Point-to-Point and Data Lake
The second top 20 pharma planned to rebuild all integrations at once to:
Due to time constraints and existing legacy connections, they implemented point-to-point integrations in addition to those that went to the data lake. They prioritized their safety integration and devised backup plans to mitigate any go-live issues.
A critical component of their strategy was rationalizing the number of integrations. They had deep dives to understand why integration was needed, what the process and data flows were, and if people were following the process. They discovered that oftentimes what people thought was happening wasn’t what was actually happening. They were able to reduce 45 direct integrations down to 23.
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To ensure continuity when new data points are added (they aren’t automatically transferred to the data lake), study teams collaborate with the data platform team to submit change requests, which are implemented using the agile methodology.
Point-to-Point and Data Virtualization
The third top 20 pharma split integration into two components: point-to-point integrations and data virtualization. While it was time-consuming to identify all the “consumers” of CTMS data across their various data lakes, the effort was worth it - they reduced 200 integrations to 75.
Their deployment strategy spanned a few releases:
Sharing contact data and study team role data with their IRT to dispense drugs to sites was a top integration they prioritized because it impacts shipments. They built SAP integrations that trigger payments to US vaccine sites and built financial controls to ensure a clinical trial is registered before a payment is made, which impacts disclosures.
The integrations mostly transfer data, but some have specific objects that share the audit trail as well, such as cycle times for monitoring visit reports (MVRs) so that data is reportable.
Data Lake and Data Warehouse
Another top 20 pharma currently implementing Vault CTMS connects to their data lake (which has point-to-point integrations) and their legacy data warehouse (which connects to other downstream systems). The company has spent significant effort assessing and prioritizing its integrations and anticipates pushing 80% of Vault CTMS data objects to the data lake.
It’s clear that data integration is an incredibly important workstream when implementing a new CTMS and I encourage you to explore what strategy is right for your organization.
I’m looking forward to hosting our next roundtable on change management on December 5. Please reach out to me if you’re interested in attending.