A Practical Business Approach to Data Management for Clinical Trial Efficiency

Today’s clinical trials have become more complex and expensive, pressuring pharmaceutical companies to further improve their clinical trial operations. Clinical trial data management is one area where both sponsors and contract research organizations (CROs) can uncover new efficiencies, increase cost-saving measures and better meet diverse operational reporting needs across the clinical development cycle.

In this blog we begin to examine the current issues with traditional electronic data capture systems and other current “big data” approaches that attempt to address complex operational reporting needs in this historically stagnant and underserved area. We also discuss the use of two distinct data repositories – an operational data warehouse and a clinical data warehouse – the Xcellerate® Clinical Data Hub as part of a new data model through the Xcellerate Informatics Suite to provide a significant technological advance in clinical trial operations.

Understanding current Inefficiencies

Even though all clinical data can be captured electronically, trials rely on several disconnected systems to acquire the data, enable operational oversight, track quality and ensure safety monitoring. With systems like an Interactive Response Technology (IRT), Electronic Data Capture (EDC) system, Clinical Trial Management System (CTMS), electronic Trial Master File (eTMF) and quality management system (QMS), etc., each is likely supplied by a different vendor or hosted externally.

Managing trial execution to track study milestones, monitor site performance and generate reports requires navigating all of these sources and manually assembling data – a non-scalable and costly scenario. Not only is this process inefficient, it introduces the potential for delays, errors and variability in information gathering and creates gaps in trial oversight.

Several market offerings claim to solve operational reporting needs through various “big data” approaches, but we argue that their use in operational reporting is premature and misguided. While some technologies can ingest large amounts of data very efficiently, they also postpone data mapping/normalization until query time. In addition, many query and reporting capabilities are still evolving and are not as powerful as the use of Structured Query Language (SQL), the standard language for relational database management systems. Given that the majority of operational reports are standardized, we believe it is more efficient to normalize the data up front and simplify efforts at reporting time for a robust, scalable solution.

Evaluating operational reporting needs

Depending on individual study needs, process differences, sponsor preferences and source system capabilities, operational reporting needs can be quite diverse across the clinical development life cycle. However, most needs can be satisfied with standardized reports on study performance as compared to the plan. This includes metrics like site activation, recruitment and unit performance by country, region and function. Standardized reports can also track recurring issues and resolution, such as GCP, SAEs, protocols deviations, data quality, financial portfolio performance and CRO performance through key performance indicators (KPIs). Non-standard reporting requirements include more customized data views, such as trend analysis, root cause analysis, modeling and forecasting.

A successful solution for operational reporting needs to meet several requirements:

• Integrate with market leaders in clinical data capture
• Provide data integration options for niche data providers
• Address standardized reporting needs with a low-cost but effective solution
• Offer options for ad hoc reporting

Additionally, any operational reporting solution must be scalable and secure to meet the needs of a modern clinical development environment where multiple sponsors, CROs and data vendors often need to collaborate to ensure seamless trial execution and success.

A drive to unite disparate systems

Our solution to unite clinical development analytics involves uncoupling a trial’s operational and clinical objectives to better integrate the data sources. Here, operational objectives focus on achieving optimal execution of clinical studies from a data quality, patient safety, timeline and cost perspective through an operational data warehouse. Meanwhile, the clinical objectives focus on enrolling qualified patients, ensuring that the collected data are “fit-for-purpose” and monitoring drug-related safety issues through a clinical data warehouse.

Designing a scalable system to accommodate operational oversight

Created as a flexible, maintainable and extensible system, our model can efficiently combine data from a variety of sources. As new data sources emerge, they can be added quickly and with minimal effort. The end user works with a visual interface to query and view results through the Xcellerate® Informatics Suite.

Aggregated data is accessible through a visual dashboard designed to enable project teams to track the progress of their trials against milestones and performance targets. This interactive space offers longitudinal views of key performance indicators (KPIs) and metrics at the individual study level across sites and geographical regions, and includes specifics such as site startup and enrollment, site performance, country performance, protocol deviations, data management and grant payments.

With the Xcellerate Informatics Suite, project teams can view crucial performance objectives such as site activation, subject enrollment and study completion dates to see if they are on track as well as review key milestones and projections for meeting study objectives based on the observed trends.

While standard reporting covers most metrics tracked in a clinical study, we recognized that many sponsors are interested in exploratory data mining and ad hoc analysis of their operational data. These reporting efforts are supported through direct access to the operational data warehouse schema where popular analytics tools can directly query the underlying service database.

Making informed trial decisions

Complex trials have created data overload, hampering the ability to manage and extract meaningful data during the course of a study. Meanwhile, several inflexible data gathering systems compound the problem by requiring ongoing maintenance ‒ hampering the process of collecting clean data. The Xcellerate Clinical Data Hub represents a robust solution to address these inherent challenges and help sponsors make informed decisions during their trials.

To get more detail on this approach to operational reporting, download our white paper, “A Practical Business Approach to Data Management for Clinical Trial Efficiency.”