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3 Process Transformations to Address Clinical Data Complexity

Against a backdrop of increasing trial complexity and data volume, clinical biometrics and operations teams are adopting innovative technologies, and equipping teams with the skills to use them effectively. Alongside these initiatives, to benefit from the potential of clinical data transformation it’s also critical that leaders rigorously scrutinize and optimize their processes to ensure they are fit for purpose within the modern trial framework.

In this blog, we will explore three key process transformations that are essential for biometrics leaders to consider in navigating the challenges and opportunities of growing clinical data volume and variety.

Transformation 1: Harnessing automation

The eClinical Solutions’ Industry Outlook 2023 highlighted that harnessing automation throughout the clinical data lifecycle was the top industry focus for more than one-third (36%) of respondents.

Across the data life cycle, from acquisition to insight, there are multiple applications where automation can streamline routine tasks.

For example, at eClinical Solutions, our teams are applying automation to create efficiencies within database build, and automation and artificial intelligence approaches to complement manual data cleaning. Organizations can also use automation to support data integration and statistical analysis tasks, leading to higher data quality and efficiency. As biometrics leaders incorporate automation into their processes, they should focus on developing their team’s skills, while prioritizing the selection and implementation of tools that align with the organization’s goals and can integrate well into existing workflows.

Transformation 2: Incorporating risk-informed approaches

In the context of greater trial complexity, traditional monitoring and quality assurance methods are unscalable. Incorporating risk-based approaches enables biometrics teams to focus their resources on the most critical data, ensure overall quality, ensure patient safety and comply with regulatory guidance. As well, there is growing recognition of the value of risk-based approaches in improving efficiency and cost-effectiveness of clinical trial operations while reducing the burden on teams.

Transformation 3: Establishing external data processes

New data sources like genomics, imaging, biomarkers, wearables, electronic medical records bring highly promising opportunities to gain richer scientific insights. However, the volume and variety of data coming from outside EDC (Electronic Data Capture) also brings challenges that we need to address. In recognition of this importance, at eClinical Solutions, we have set up a Center of Excellence for External Data to drive best practices and create efficiencies through standardization and knowledge sharing, alongside leveraging elluminate.

For example, engaging closely with external data vendors to assess their data quality and reliability, building effective communication and relationships, and implementing standardized Data Transfer Agreements (DTAs) are all practical steps. For effective external data management, organizations may also set up systems for tracking KPIs (Key Performance Indicators) such as data quality, accuracy, and timeliness and use technologies to enable visibility.

Such ongoing process transformation has an instrumental role to play in overcoming the challenges posed by data complexity and will help to equip biometrics teams for the future-state. By incorporating automation, risk-informed approaches, and good external data practices biometrics teams will be well placed to make good on the potential of data expansion. In doing so, we can drive efficiency, enhance data quality, and ultimately accelerate development.

To learn more about the people, process and technology transformations shaping a new clinical biometrics blueprint, download our latest ebook.

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2024 Industry Outlook: Driving Tomorrow’s Breakthroughs with Clinical Data Transformation