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From Tedious to Top-Speed: Leveraging AI for Clinical Data Collection

In recent years, Artificial Intelligence (AI) has transformed various aspects of our lives, simplifying and accelerating daily routines like optimized GPS navigation saving us travel time, or personal wellness apps that track physical activity or sleeping patterns empowering us to take steps toward a healthier lifestyle. However, the biopharmaceutical industry, due to its heavily regulated nature, has been relatively slow to fully embrace cutting-edge technologies. Nonetheless, promising developments in AI, particularly in Generative AI, are now taking center stage, offering exciting possibilities for the future of clinical research.

AI's potential in the biopharmaceutical industry spans almost all areas, including image and data analysis, patient identification and recruitment, early cancer detection and novel therapy exploration. These advancements are indeed exhilarating and hold the promise of propelling the industry forward over the next few years.

Yet, it is essential to recognize that AI can also bring significant value to the more mundane and administrative aspects of clinical trials.

It seems there are a lot of these tedious tasks in clinical trials. Documentation, forms, and data collection are critical components of clinical trials, demanding meticulous attention to comply with regulatory requirements. Both biopharmaceutical companies and their contract research partners invest countless hours and substantial resources in designing data collection forms, questionnaires, and corresponding technology systems aligned with the clinical protocol.

So how can we minimize the time we spend on these tasks? How can we use AI to accelerate the time it takes to start up a study, as well as free up time for the study team to focus on bigger picture efforts and the care of their participants?

We can start with the protocol.

The clinical protocol serves as the blueprint for the study, meticulously outlining every aspect, including study procedures, participant inclusion and exclusion criteria, data collection methods, measurements, and more. Implementing this protocol involves collaborating with multiple vendors who often operate their systems under a barrage of acronyms (e.g., eCOA, EDC, eCRF, CTMS, eTMF, XYZ, ABC). This complexity can lead to delays and challenges in achieving study milestones.

To address this, advanced Generative AI offers an opportunity to streamline the process and enhance efficiency. By leveraging the clinical protocol as a guide, AI can read and interpret the document, identify relevant data elements, encode them with industry-standard metadata like CDISC, and automatically generate various study-related documents and templates.

Some of the advantages that Generative AI brings to the clinical study process can include:

1. Uniform Interpretation of Protocol Requirements:

Generative AI can ensure consistent and accurate interpretation of the protocol, reducing the risk of miscommunication between vendors and study stakeholders.

2. Rapid and Automated Documentation:

AI can reduce timelines and resources by facilitating swift and automatic creation of design specifications and data collection forms, such as eConsent, eCOA, EDC, eSource, and eCRFs.

3. Seamless Data Capture from Connected Devices:

AI can accelerate integration and simplify the process of capturing data from connected devices, improving accuracy and efficiency in data collection.

4. Automated Scheduling and Notifications:

AI can automate scheduling and notifications for remote data collection tasks, such as home health visits or telehealth consultations, improving patient engagement throughout the study

5. Effortless Translation and Localization:

Generative AI can automatically translate and localize all participant-facing documents, forms, and questionnaires, supporting global clinical trials, so that time and money isn’t wasted waiting for long localization processes.

By leveraging Generative AI to address the myriad of time consuming documentation & form building requirements in the clinical study process, we can foresee a future where the time from a "final protocol" to enrolling the first patient is dramatically reduced. Furthermore, the application of AI technology in late-stage protocol amendments could have a transformative impact. The clinical study process is a crucial aspect of our healthcare system, and with the power of AI, it can be significantly expedited.

In conclusion, the integration of Generative AI in the biopharmaceutical industry holds immense promise for accelerating data collection and streamlining the clinical study process.

As we embrace this technology, we can look forward to faster delivery of life-saving drugs and advancements in disease treatment, ultimately benefiting patients and healthcare systems alike. The future of clinical research is bright with the potential of AI, ushering in an era of unprecedented efficiency and innovation. Author: Jeff Sager, Chief Commercial Officer, TRYAL To learn more about how TRYAL can help streamline your clinical studies, request a demo today.

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