While the potential use cases of AI have increased and its adoption has already gone up in the past few years, the pharmaceutical industry has ample headroom to leverage AI to its fullest. The reason for the lag is, although the technology is new, the methodology driving the processes is still old.
One of the major contributors toward the high cost of clinical trials is the need to perform Source Data Verification (SDV) frequently to ensure the highest quality of data. Constant checks to manage compliance processes are equally necessary and therefore resource and time intensive. With AI-driven tools, it is possible to bring down the cost without compromising on quality or accuracy.
High-quality data is typically defined as data that is standardized, satisfies protocol parameters, and is ready for statistical quantification. To ensure the quality of data, it is critical to build the most efficient processes, appoint the most qualified resources, and leverage the most cutting-edge technological tools.
The Key to Ensuring High Data Quality and Compliance: An End-to-End Data Management Strategy
The road to ensuring the highest quality of data begins with using AI capabilities to manage and store all clinical data. With an AI-powered platform, it is possible to monitor all types of inputs – patient data, site performance metrics, SDV completion metrics, and data from all other sources, and run regular checks to ensure that processes follow the procedures necessary to comply with industry regulations.
An AI-enabled solution makes it possible to manage data reliably since it standardizes data so that it is a single source of truth. As the trial progresses, the AI platform can easily keep track of the trial’s Key Performance Indicators (KPIs) and identify potential protocol deviations. With the stringent regulations that are common in the life sciences and healthcare industries, it is prudent to build in processes to ensure compliance.
An efficient AI solution that has focused capabilities can offer course correction recommendations and record the relevant discrepancy for future reference. The platform’s machine learning algorithms would learn the pattern, and become ready to detect similar discrepancies with more accuracy and much earlier in the cycle.
A successful clinical trial that stays within budget meets its timelines, maintains high data quality, and adheres to compliance processes is an outcome that every pharmaceutical company strives to achieve. With the application of AI capabilities, it may now be possible to deliver this desired result.
Saama’s Life Science Analytics Cloud (LSAC) is a platform powered by AI capabilities that has the ability to ensure data quality and integrity across sites while maintaining all compliance standards.
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