P312: Navigating New Frontiers: Next Generation Clinical Trial Quality Assurance Using Advanced Analytics
Poster Presenter
Maria Baier
Data Scientist
Boehringer Ingelheim Germany
Objectives
This poster shows how two IMPALA Consortium member companies are using advanced analytics to augment traditional methods for assuring quality in clinical trials, moving from lagging to leading indicators of quality and limited/sampled to holistic risk and quality oversight.
Method
Case-Studies from two IMPALA member companies will demonstrate how advanced analytics methods were implemented at scale and the effect on the strategic approach to assuring quality using quantitative and qualitative effect measures that demonstrate the impact on quality assurance oversight.
Results
Sponsor companies are evolving along with the changing regulations, moving away from fixed quality assurance strategies towards adaptable ones that respond to incoming signals which point toward the need for more attention. The IMPALA (Inter coMPany quALity Analytics) Consortium not for profit consortium supports the shift toward dynamic quality assurance in clinical trials by providing publicly available data analytics packages to measure changing risks to patient safety and data reliability. Two member companies who have implemented these packages at scale will present case studies that demonstrate how they maintained oversight of the factors most critical to quality at both the clinical trial and investigator site level. One member company will present data that compare various dimensions of quality and how their impact was different on those clinical trials where advanced analytics were applied together with traditional quality assurance methods vs those where traditional methods alone were used. The second member company will share how they have implemented the use of data analytics to develop a data-driven audit strategy that follows a pre-defined methodology while maintaining industry standards. This enables them to obtain evidence and draw conclusions on a large volume of data in a short period of time and identify issues that are systemic in nature. The process involves the systematic review of study documents and of operational site KRIs, some of which were calculated using open source tools developed by IMPALA. Applying this method targeted quality positions can be effectively answered at scale. The combined 1 year experience with periodic executions answering targeted quality questions for over 20 studies across 2 critical to quality factors will be shared.
Conclusion
Clinical quality assurance methods that harness data analytics, whether combined with traditional approaches or by themselves, demonstrate improved risk management and earlier detection of issues before they develop into critical quality events. This can have an impact on the quality strategy employed at the clinical trial and the enterprise level. Use of advanced analytic methods and tools allows Pharmaceutical companies to assure quality at scale across their entire portfolio. Traditional quality assurance methods are designed to use a risk-based approach to selecting entities for audit and then extrapolating quality conclusions based on the sampled entities. The retrospective nature of traditional quality assurance approaches by themselves and the limited view from sampling underscore the reactive approach to assuring quality by such methods. This represents a fundamental shift in approaches to quality assurance strategy. The role of the quality professional will necessarily evolve to include data pattern detection skills and a mindset that shifts from fixed to adaptive and reactive to proactive approaches to managing quality.
The IMPALA Consortium is driving the innovative shift towards use of advanced analytic methods by developing, testing, and validating cutting-edge quality analytic and process-related tools and making them publicly available.
