Clinical data managers play a key role in clinical trials, ensuring the integrity of the clinical data and bearing ultimate responsibility for preparing the data for statistical analysis. As clinical studies evolve, data management is becoming more complex with the use of multiple data sources, as well as the increased volume of data through case report forms, patient reported outcomes, laboratory data, electronic health records, imaging reports, and more.

Here, we discuss various ways artificial intelligence has the potential to accelerate clinical trial data management as well as some of the benefits and challenges of using these groundbreaking tools.

Transforming data cleaning in clinical trials

The traditional method of data cleaning involves manual checks and review of data listings. Data that falls outside the expected results is queried, which leads to time-consuming communications that are often prone to error. Additionally, a significant amount of time is spent programming and validating data checks and listings.

AI has the potential to transform data cleaning: AI tools can quickly spot outliers, inconsistencies, and errors in datasets that may be missed with traditional methods. An example of this is an exception listing, which compares elevated laboratory parameters with adverse events. This listing would seek to strike a correlation between laboratory values and concurrent adverse events. AI can also detect possible missing or duplicate data. Ultimately, AI can lead to faster data availability by improving clinical trial data analysis and cleaning.

Furthermore, AI can be used to detect fraudulent data. As data managers review one patient data at a time, AI tools can look at data at a site collectively and look for potential fraudulent data (for example, dosing for all patients at the site is on the same day and time).

Improving database development in clinical trials

The use of AI is becoming more prevalent in the software industry: electronic data capture (EDC) companies are using AI technology to translate plain text for edit check requirements (e.g., temperature should be within 36–40 Celsius) into the programmed edit checks. This will significantly improve the timelines for database development within the clinical trial space.

Shortening database lock timelines

AI can also shorten database lock timelines. Data managers are often tasked with manually reviewing data to ensure data accuracy as well as verifying whether there is missing data. An example of this is considering a study participant with colon cancer, but there is no prior therapy within the timeframe of the condition. Data managers require a significant amount of time to perform such reviews. AI can examine the data holistically, flag possible discrepancies, and reduce manual effort and human error, allowing for a cleaner database at a much faster rate.

The need for human involvement

While AI can process large amounts of data, identify suspicious patterns, and analyze images, human involvement is required to validate the AI functionality. Humans will also be required to perform ongoing reviews of the output and adjust the AI tools as the amount of clinical trial data increases. With the use of AI, individuals involved in the clinical trial can focus their skills and time on evaluating complex factors and decision-making.

Benefits and challenges of AI

The use of AI in clinical trial data management and EDC development has several benefits:

• AI can automate time-consuming manual tasks for a faster and more accurate database.
• AI can reduce the possibility of human error while continuously monitoring clinical trial data. This allows data issues to be detected quickly, which can improve data accuracy and safety for trial participants.

However, challenges remain:

• Maintaining participant confidentiality while using AI tools can be a significant challenge.
• AI systems must be configured and monitored to ensure there is no risk of accessing unauthorized or unblinded data.
• Large amounts of data are required for the AI to be properly trained to deliver high-quality results.

Ultimately, the decision must be made of whether the benefits of AI outweigh the challenges.

The pharmaceutical industry is a heavily regulated industry. Validity of AI tools must be established before these tools are deployed for database development and data cleaning. This will ensure the sensitivity and specificity with which the AI tool is expected to perform with a negligible error rate.

Final takeaways

AI will play an important role in many aspects of clinical trials, including and beyond data management. From identifying potential compounds to automating routine tasks, innovating statistical programming, streamlining medical writing, and creating digital twins, we will only continue to see advancements in AI tools in the coming years. AI can be a groundbreaking tool to shorten drug development timelines and improve patient outcomes.

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