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Good clinical trial risk management involves good protocol design and study operational design at the sites.
In my current role liaising with sponsors and academic research institutions, I have had the opportunity to design a clinical operational infrastructure, which required not only fitting the protocol into the research institution’s business operations, but also understanding how to pinpoint critical risk factors, and translating those risk factors into interactive quality management systems. In this article, I will describe how sponsors can incorporate quality/risk management systems via strategic backend data design, and site collaborations in order to enhance and risk-based monitoring (RBM).
Define Study Risk
Defining study risk that we can monitor centrally can be a challenge. Many believe that study risk involves medical-related data collected on case report forms (CRFs) (i.e., labs, vitals, AEs, etc.), however, overall study risk can be much more widespread than our initial perceptions. While TransCelerate’s RACT tool can be helpful in allowing us to uncover study risk factors, they are oftentimes difficult to visualize. Study teams can address these challenges through efficiently defining and understanding study risk.
To elaborate, in a sponsored medical device trial, we used a free cloud-based technology, @RACT, to better visualize study risk, and it became apparent to us that the data quality from a device procedure (carried out by site staff) critically impacted study endpoints. Moreover, proper data transfer from the device into electronic data capture (EDC) was also a notable risk; for example, coordinators could improperly place a device reading from one patient under another patient during data transfer in EDC. Another risk factor that impacted study endpoints included whether patients exhibited signs of a specific adverse condition, as this AE critically impacted the adjudication process.
Properly identifying risks upfront enables study teams to obtain a better perspective on how study execution and site operations intertwine, and understand how they can strategically structure their data collection systems to easily identify and address study risks during study execution and risk-based monitoring, which we will describe in the following section.
CRFs to Enhance Centralized Monitoring
One of the primary purposes of RBM involves enhancing data quality, and that requires proper feedback systems (be it resolving data discrepancies, issuing CAPAs, conducting on-site monitoring visits, more training, etc.). CRFs are typically designed to collect study data, and centralized monitors can access this data in aggregate to conduct risk-based assessments and uncover statistical abnormalities in critical data points. However, CRFs can also be strategically designed to enhance the detection of critical study risk factors, and enhance data quality through feedback systems and behavioral modification with site staff.
To expand, in a sponsored study, we created eCRFs specifically for organizing and transferring data files from the medical device to the sponsor. This form contains several built-in notifications to confirm that study staff properly organize medical device files under the correct patient. Additionally, this form contains a sponsor approval, rating and feedback system for sponsor staff to actively measure and convey areas of improvement to site staff to improve device measurement quality.
From centralized monitoring and quality management standpoints, this system enables centralized monitors to analytically evaluate device measurement quality, and allows project management to convey feedback to site staff, and retrain them if necessary. Moreover, with adverse event risks, we incorporated a check mark in the AE eCRF to easily detect and analyze AEs that critically impact the adjudication process. In this case, we leveraged strategic CRF and backend data design to easily identify and mitigate critical study risks during trial execution.
Work with Study Sites to Optimize Operations and Training
Many sponsors design clinical trials expecting them to fit into study site operational infrastructures. This approach can lead to many issues involving lacking operational infrastructures for properly managing data quality, and study procedures that are incompatible with how study sites operate, which increase study risk on many fronts. In order to reduce operational risks, it is important for sponsors to collaborate with study sites during the study design phase to obtain feedback on how to better operate the study.
To demonstrate, in a sponsored study, we engaged site staff during training sessions to understand study procedures where site staff felt proficient, and hesitant. Correspondingly, we created additional training materials, and planned dry run sessions to ensure that the study personnel felt comfortable, and to uncover additional operational hindrances that may impact study execution and data quality.
While Eli Lilly has designed a site simulation platform to evaluate feedback from site staff and patients on clinical trial operational design, not every sponsor can access such tools and resources to conduct these types of evaluations. Correspondingly, it is important for sponsors to consider engaging prospective study sites during the trial design phase to create clinical trials that work operationally while reducing risk.
To summarize, good clinical trial risk management not only involves good protocol design, but, also study operational design at sites. While it is important to identify study risks upfront, knowing how site operations impact study risks can be critical for designing proper quality and risk-based management systems via strategically designed data collection forms/CRFs. Engaging sites during the study design phase, and gathering feedback from sites during training can yield significant benefits in improving quality during clinical trial execution.