Insight by: Jon Warner
Clinical trials are a crucial but complex part of the new drug and medical device development process for life sciences companies. But inevitably, ensuring that trials are designed and run efficiently requires tapping into diverse expertise from what is often a multitude of people, usually in diverse geographies or locations and across many diverse organizations that may have suitable participants. This is where advisory boards, comprised of external experts, can provide valuable guidance.
Assembling the right advisory board has traditionally involved time-consuming manual processes like searching for specialist clinicians (relevant to the area of focus for the clinical trial, evaluating individual profiles, coordinating schedules and handling a multitude of paperwork. This is where artificial intelligence (AI) can help streamline the process and ensure boards are not only optimally composed but able be convened with digital technology to make the process smoother and/or less frictional.
AI offers several key advantages over manual methods for assembling digital advisory boards tailored for specific clinical trial topics and indications. Firstly, AI systems can analyze huge amounts of structured and unstructured data from multiple sources to identify the most qualified candidates to help with a given clinical trial. Natural language processing tools allow AI to extract relevant skills, experiences and areas of focus mentioned in profiles, CVs, publications and past projects. By mapping these attributes, AI can match likely candidates to the requirements of a clinical trial in areas like therapeutic expertise, methodology knowledge, technologies and more. This extensive screening and matching based on skills ensures advisory boards comprise members with directly applicable expertise. AI screening also helps reduce time spent sifting through unsuitable candidates manually.
Additionally, AI enables life sciences companies to optimize boards for diversity and balance. Systems can evaluate candidates based on factors like gender, ethnicity, industry versus academic experience, geographic locations and more. This helps create well-rounded boards that consider diverse perspectives. AI also mitigates unconscious biases that can creep in during manual selection processes.
Once qualified candidates are identified, AI also increasingly has the capacity to handle logistical coordination tasks. By accessing members’ calendars and availability, AI finds optimal meeting dates factoring in time zones. It can then send automated meeting invites, tracks RSVPs and follows up on absentees (and even send meeting notes afterwards for those people who miss a meeting). This helps to streamline scheduling across a geographically dispersed expert pool.
AI also facilitates paperless onboarding and collaboration. Digital platforms powered by AI can securely enroll board members, share documents and record minutes of discussions without physical paperwork. AI meeting assistants further help capture action items and next steps to stay organized.
Post board meetings, AI drives continuous improvement. Systems analyze contributions and feedback and identify patterns around what worked well and areas for enhancement. Over time, AI learns to assemble even more impactful boards by applying insights from past experiences. AI reports also provide transparency around board composition metrics.
In summary, leveraging AI’s powerful data processing and quickly evolving abilities allows life sciences companies to cost-effectively form tailored digital advisory boards optimized for clinical trial needs. By screening huge candidate pools, mapping skills, ensuring diversity and streamlining logistics, AI assembles well-rounded expert teams in a much faster way than has traditionally been the case. It further enhances boards over time through well-designed analytics and the insights this offers. This positions companies to efficiently design trials and expedite development of new treatments for patients.
