A Health Authority (HA) inspection of a clinical trial can occur at any time, and preparation is critical to an efficient process that is often a precursor to marketing approval. Ideal preparation for an inspection begins with an inspection readiness (IR) assessment before crafting a solid inspection plan, then moving through the steps before conducting a mock inspection. A mock inspection implements all the preparatory steps and highlights any activities or processes needing remediation.
In an IR assessment, a study team can lean into the insights of AI to locate areas of risk in their clinical trial processes and then establish remediation activities. For example, AI tools such as Power BI, Smartsheet, or other data analytics packages can continuously monitor and analyze data and processes to identify potential issues early on.
While it is the sponsor’s responsibility to oversee all data and processes for their trial, study teams can still tap into AI tools to support their inspection readiness activities. Here are three common findings during inspections and ways to lean into AI along your inspection readiness path.
Protocol Deviations
AI can play a role in streamlining the review of clinical protocol deviations by automating processes, reducing manual efforts, and enhancing efficiency. AI algorithms can be programmed to detect and classify protocol deviations by analyzing clinical trial data across various platforms such as electronic medical records (EMRs), electronic data capture (EDC) systems, and monitoring report data stored in an electronic system.
Natural Language Processing (NLP) can be particularly valuable in extracting deviations from clinical narratives, reports, and documentation. These deviations can be monitored and generate standardized reports in real-time so that relevant personnel can be notified for further investigation. Through data analysis, patterns of protocol deviations can be identified which can lead to improvements in training, or even trigger a risk-based monitoring visit. This type of oversight and follow-up are key to highlighting proactive measures to prevent or mitigate issues.
Implementing AI for the assessment of protocol deviations can lead to increased efficiency, faster response times, and improved overall compliance with study protocols, and support inspection readiness. However, it is important to pair AI with human expertise to ensure comprehensive and contextually accurate management and oversight of protocol deviations.
Vendor Selection and Oversight
AI analytics can compare vendor performance against industry benchmarks to ensure vendors meet or exceed standards. Initiating such a process prior to vendor engagement marks the establishment of effective vendor oversight by documenting a sponsor’s thorough vendor review process.
AI can significantly enhance vendor inspection readiness by providing real-time monitoring of vendor activities, tracking compliance metrics, and generating alerts for any deviations from the predetermined standards. AI can assist in assessing competency levels and documenting the required training of vendor personnel. By identifying gaps in knowledge or skills, organizations can take corrective actions to ensure vendor teams are well-prepared for inspections.
Communication tools that are AI-powered can simplify collaboration between sponsors and their vendors. Improved communication ensures expectations are clear, and any issues or updates can be addressed promptly. By leveraging these AI-driven capabilities, organizations can enhance their vendor inspection readiness and establish a culture of continuous improvement and proactive risk management.
Trial Master File Quality and Completeness
Electronic systems can be integrated with AI to ensure a seamless exchange of data, contributing to a more cohesive and complete electronic Trial Master File (eTMF). Implementing AI enables proactive measures to address common issues and enhance overall eTMF management. This constructive collaboration between AI technologies and document management systems strengthens the overall document control process, helping to prepare for regulatory compliance and efficient clinical trial management.
AI-powered document management systems can organize and classify documents based on their content, making it easier to retrieve and present requested information during inspections. AI can automatically tag documents with metadata, making it easier to search, retrieve, and organize information within the eTMF.
Furthermore, AI is expected to review filed documents for completeness and confirm the filing location, including checking documents against study templates to confirm adherence to the format. If data is missing, alerts can be generated to notify study teams for prompt remediation. AI algorithms can also build upon current tools to better assist in version control and tracked changes to avoid discrepancies and maintain a thorough audit trail.
Looking Forward
AI systems continuously learn from new data and feedback and can identify trends and patterns for analysis. AI systems can monitor regulatory changes and updates, ensuring processes are aligned with the latest requirements. This proactive approach helps organizations stay ahead of regulatory expectations and minimizes surprises during inspections.
The goal of adding AI into already established systems is to allow organizations to become more efficient by preventing, identifying, and correcting issues promptly – all of which highlight study oversight during an inspection.
There are still many concerns surrounding AI use and adoption, however, we cannot emphasize enough the value of human expertise and oversight with AI use in your risk-based approach to inspection readiness.
Halloran has supported numerous clients with inspection readiness activities including remediation, developing inspection readiness plans, performing mock inspections, and inspection readiness training, all of which have yielded product approvals.
Contact Halloran to learn their seven-step inspection readiness process.