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As discussed in Part 1 and Part 2 of AI in Clinical Trials, to process a large and continuously flowing stream of data, the pharma industry will need to employ an equally swift platform to ingest, standardize and manage the data, i.e. a holistic clinical data management platform. With the help of AI, MaxisIT’s Clinical Trial Oversight Platform ingests data from different sources, aggregates, and stores them into a repository. It also runs analytics without the need for coding and delivers actionable insights.
With improvements in electronic data capture, human errors in data capture will be eliminated or at the least be reduced drastically to enable instant integration with databases. Such seamless data management should reduce the amount of time and manual effort put into clinical data management processes.
AI will also help in reducing the overall burden of clinical data management by generating queries and reducing unnecessary and low-impact queries. This reduction in unnecessary queries will give clinical study stakeholders more time to concentrate on higher-value clinical tasks.
Modernize the clinical development process by integration with MaxisIT’s AI-based Clinical Trial Oversight platform
At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical trial operations as well as patient data, allows efficient trial oversight via remote monitoring, statistically assessed controls, data quality management, clinical reviews, and statistical computing. Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring.
MaxisIT’s Clinical Trials Oversight System (CTOS) enables “data-driven digital transformation” by its complete AI-enabled analytics platform from data ingestion, processing, analysis to in-time clinical intelligence by establishing the value of data; which empowers clinical stakeholders to mitigate risks or seize the opportunity in the most efficient manner at a reduced cost.