To the uninitiated, the charge to balance participant safety, treatment efficacy, and operational feasibility in clinical trial design sounds practical and straightforward. Regrettably, subject-matter experts aren’t always available, informative data languishes in long-forgotten silos, patient identification and enrollment processes are painfully slow, and cost overruns threaten trials before they can even get off the ground.
Many of these bottlenecks can be cleared and any remaining challenges overcome by adding artificial intelligence (AI), machine learning (ML), and automation to carry out many tasks in a more streamlined, comprehensive, faster, and effective manner.
AI adds more than efficiency to clinical trial designs. The technology can also inform and impact how, where, and when clinical trials are performed.
For example, PwC reports that decentralized trials—which incorporate a combination of delivery channels such as pharmacies and retail outlets—“reduce operational complexities, cycle times, costs, and patient burden. In addition, decentralized trials can open access to more diverse patient populations, bolstering enrollment and offering increased flexibility that reduces patient burden and improves retention.”
Using a distributed physical approach that leverages the reach of retail chains supports trials in more disease areas and among more demographics than virtual studies do. A well-planned decentralized trial also presents opportunities in data collection and management that are not available in a virtual or centralized environment.
But this is only one of many ways that new thinking and new technologies can overcome clinical trial design challenges and improve outcomes. Here are three major impacts AI and ML will have on clinical trial designs.
Predict and resolve obstacles via automated AI scenario planning solutions
Combining AI and automation into scenario-planning solutions is an effective way to perfect protocol design and planning phases. These tech-enabled solutions – usually called accelerators – digitize and aggregate data from multiple sources such as sponsor and external protocols, as well as internal sponsor operational outcomes. The latter often includes data on major touchpoints such as costs and screen failures. The AI or ML model can then rapidly output recommendations on how to better refine and streamline the protocol design scenarios.
Improve participant enrollment and compliance
Virtual participant recruitment and reporting tools are not always as successful as hoped in clinical trials. Limitations due to age, illness level, and availability can impact study participants’ compliance. Even with a boost from wearable self-reporting devices like smartwatches, data collection can fall short of clinical trial requirements. AI-based tools can help more accurately predict issues surrounding participant reporting errors, noncompliance, and burdens that can be successfully addressed in the clinical trial design.
Leverage historical clinical data
Previous clinical trials have a wealth of information that can be mined to design future trials. More information is always better, but it takes AI-based tools to gather it all and analyze it at such a tremendous scale.
The result is a faster, more efficient, less burdensome clinical design that leverages everything learned previously regarding your study design and topics.
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