Innovative strategies paving the way for enhanced data quality, integrity and streamlined data validation and cleaning.
The way we clean and validate data within clinical trials is continuously evolving, more than ever now with the advancements in technology that we are seeing across this industry year on year. These technological developments are reshaping the way data is handled, leading to improvements in the accuracy and efficiency of data. As technology progresses, it introduces new methods and tools that enhance the management and analysis of clinical trial data, reflecting the ongoing changes of data management practices in clinical trials.
The Current Approach to Clinical Data Integrity
Data cleaning across unified platform Electronic Data Capture (EDC) systems and integrated systems like electronic Clinical Outcome Assessments (eCOA) and wearable devices, currently involves a dual approach of automated and manual processes. The automated aspect typically includes validation and edit checks within the EDC system to spot outliers, inconsistencies, and missing data. Parallel to this, manual data cleaning tasks are also crucial, involving activities such as reviewing query reports, resolving discrepancies, and conducting checks for data accuracy and completeness. Although integral to ensuring data integrity, these manual tasks can be time-consuming and potentially more prone to a higher level of errors, especially if the review guidelines or data outputs lack consistency or clarity.
Additionally, clinical data managers and data scientists may use statistical methods and data visualisation techniques to identify and address data anomalies. These can be very helpful while having to deal with third party data, which is increasingly making up a large quantity of the clinical data that we need to validate, and of course integrate along with the EDC data to help with the end goal of clean and consistent data that is ready for submission.
Data from various external sources, such as electronic health records, wearable devices, or genomic data, often need to be standardised and integrated into the clinical trial database. This involves establishing consistent formats and data structures to ensure compatibility with the existing data. The Study Data Tabulation Model (SDTM) is the standard structure for human clinical trial data tabulations and for non-clinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
The Future of Data Handling
As an industry, we are progressively shifting towards operating under just one umbrella or integrated system. This transition aims to facilitate user-friendly and effect data viewing, while also ensuring comprehensive data capture and efficient, correct data handling.
There is also the matter of the emergence of AI (Artificial Intelligence) and ML (Machine Learning) that will surely play an ever-increasing role within our industry. These technologies could help with implementing automated data quality checks, statistical analysis and visualisation techniques using tools and algorithms that can swiftly detect patterns, inconsistencies, and missing data across the large volume of external data streams to quickly identify data quality issues and outliers. Additionally, aligning these external data streams with the corresponding fields in the clinical trial database and resolving any emerging discrepancies or conflicts is key to ensuring data consistency and accuracy. While this is on the tip of many companies’ initiatives and thought leadership, as an industry we are not there as a whole.
Navigating the Now with Integrated Systems
Given that we are still not in grasp of AI and ML, integrated systems will play a pivotal role in the near future of how we handle and validate clinical data, both in a biometrics and the clinical sphere.
Due to this requirement and adoption of progressive technology, we at Quanticate have noticed the fantastic possibilities data workbenches can offer a company in managing their clinical data, such as the Veeva CDB system.
Veeva CDB provides a centralised repository for clinical trial data, allowing users to aggregate and manage a wide range of clinical data, including patient data, study data, and operational data. This centralised repository facilitates data validation by providing a single source of truth for all data-related activities.
There are 3 primary areas of benefit to clinical data management that we see:
- Automated Ingestion and Error Detection
Integration with EDC Systems: Veeva CDB can integrate with electronic data capture (EDC) systems, allowing seamless transfer of data between the EDC system and the centralised data repository. This integration facilitates data validation by enabling the automated validation of data as it is collected and entered into the EDC system. The tight and seamless integration between Vault EDC and Veeva CDB allows for the true bidirectional flow of queries between the two systems. This grants the user increased capabilities for validating their data by using the benefits that CDB offer, while still maintaining that core link to the EDC due to the true integration between the systems.
- Automated Validation Rules That Reduce Manual Cleaning Effort:
- Data Quality Monitoring: Veeva CDB enables users to define and monitor data quality metrics and rules. It provides capabilities for implementing automated data validation checks to ensure that data meets predefined quality standards. This can help identify and address data quality issues in real time.
- Custom Data Validation Rules: Veeva CDB allows users to define custom data validation rules based on specific study requirements. These rules can be used to validate data at the point of entry, during data transfers, and throughout the data management process to ensure compliance with predefined standards.
- Data Reconciliation: Veeva CDB facilitates data reconciliation by providing tools to compare and reconcile data across different sources, such as EDC systems, external data sources, and operational databases. This helps ensure that data consistency and accuracy are maintained throughout the study.
- Data Query Management: Veeva CDB includes features for managing data queries, allowing users to create, track, and resolve data discrepancies and discrepancies efficiently. This supports the data validation process by ensuring that data inconsistencies are addressed in a timely manner.
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- Reporting and Analytics: Veeva CDB offers reporting and analytics capabilities that allow users to generate data quality reports, identify trends, and monitor data validation metrics. This is prevalent with the use of the embedded Clean Patient Tracker (CPT) which improves efficiency and enables streamlined planning towards your critical deadline such as database lock.
Advancing Data Management for Modern Clinical Trials
The continuous advancements in data validation and cleaning in Clinical Data Management (CDM) is a testament to the dynamic nature of clinical trials and the growing complexity of data sources. With the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), and the implementation of integrated systems like Veeva CDB, the industry is moving towards a more streamlined and efficient approach. These developments not only enhance data quality and integrity, but also facilitate compliance with regulatory standards. The ability to automate data ingestion, validation, and error detection, coupled with the power to effectively manage data queries and perform comprehensive data reconciliation, positions systems like Veeva CDB at the forefront of this transformation. As the industry gradually embraces these advanced tools and methodologies, we can anticipate a significant improvement in the handling and validation of clinical data. This evolution is crucial for the success of clinical trials, ensuring the accuracy and reliability of data for regulatory submissions and, ultimately, for the advancement of medical science and patient care.
Improve the accuracy and efficiency of your clinical trial data
Quanticate’s Clinical Data Management Team are dedicated to ensuring high quality clinical data and have a wealth of experience in data capture, processing and collection tools. Our team offer flexible and customized solutions across various unified platforms, including EDC's. If you would like more information on how we can assist your clinical trial submit an RFI.
