CTMS applications are the heart of trial operations and must be interoperable with all clinical systems in order to ensure complete transparency throughout the trial process. Recent surveys found that CTMS applications are not well integrated and can slow down study execution. They are often unable to support key functions such as monitoring and issue management, study performance metrics, reporting, monitoring and reporting. They also prevent stakeholders from having a full view of study progress, which can lead to problems with oversight and governance.

It is possible to reduce process and system fragmentation, which can limit visibility, quality, speed, and accessibility. Modern CTMS systems can be integrated on a single platform to eliminate silos and improve study analytics. They also allow for central monitoring and oversight, which is crucial in order to optimize trials. Figure 1 below shows the top drivers to improve CTMS

Figure 1 courtesy of Veeva Vault

Top Drivers to Improve CTMS

1. Improved Study Analytics and Reporting
2. Better Visibility into Study Performance
3. Proactive Risk Identification Mitigations
4. Improve Integrations with EDC, eTMF and Study-Start-Up
5. Better Internal and External Collaboration
6. Improve Governance and Oversight
7. Ability to Track Site Feasibility Metrics
8. Reduce the Cumbersome Aspects and ‘Fewer Clicks’ to Get to Data

Risk Based Monitoring (RBM)

RBM is becoming more important in clinical trials management as they struggle with delays and stoppages due to the COVID-19 pandemic. RBM is a method that can be used to improve trial outcomes. It requires prior research and a greater understanding of the benefits.

The Evolution of Clinical Monitoring Systems

Clinical monitoring was heavily dependent on the old approach before the FDA published guidelines for RBM. Non-risk-based methods are still valid today. They rely heavily on on-site monitoring and 100% source data verification (SDV) to ensure the reliability of trial data. This strategy has its own challenges.

• When the monitor is not present, trial monitoring takes place outside of their view
• It is costly and resource-intensive to monitor the clinical trial at-site.
• Trial monitoring management is reactive. This can lead to delays in responding to data anomalies and other challenges.

Central monitoring is a different approach than traditional methods. Central monitoring analyzes data from multiple clinical sites. This flexible approach allows for a central team to monitor the trial remotely, and then move to on-site monitoring if necessary. This method requires more flexibility and expertise with remote technology and processes.

A hybrid strategy can combine the best of central and traditional monitoring. It offers flexibility, prioritization of data points, and more options for RCT. A hybrid monitoring strategy could have adverse effects on data standardization or issues with uniformity in data analysis.

Risk-Based Monitoring Solutions

RBM offers a better monitoring option than other options because it provides both real-time clinical data review and a reduction of budgetary and risk requirements for on-site oversight. RBM is a strategic approach to trial-data monitoring. The amount of resources used to monitor data depends on its importance. Critical data points receive more attention and resources, while less important data may not be reviewed or monitored. This allows for early detection of clinical trial problems in real time, proactive highlighting data trends and potential risk areas, as well as proactive monitoring.

RBM allows you to monitor key risk indicators in real time by:

• Patient Safety Improvement – Early identification of critical patient safety processes allows for faster risk mitigation and detection. It also highlights the need to intervene on-site.
• Focusing Data Analysis – Defining processes essential to mapping trial data and proactive setting alerts for data thresholds and indicators
• Maintaining Trial Compliance – Defining and educating on risk mitigation and training, as well as creating “what-if” scenarios to test current systems.

Implementation RBM

RBM, like traditional, central and hybrid monitoring models has its difficulties. To best implement this new strategy for clinical trial monitoring, prospective adopters need to pay attention to the following areas.

• Change Management – Internal and external support will be required to transition from existing monitoring strategies into RBM. This type of implementation requires a change champion to review 1) the roles affected by the switch from RBM to 2) potential effects on existing clinical trial management processes and systems, and 3) integration and third-party vendors.
• Flexible Resources – Assessing current clinical trial teams is necessary to implement a new clinical monitoring program. Recognize that RBM will change the roles of some members, and may require additional training.
• Data Assurance – Adding RBM into your monitoring toolbox will help you identify and understand potential risks. This will require updating existing processes for analysis, standardization, and revisions to existing data storage and tracking solutions.
• Monitoring – Each clinical trial has its own requirements for data intelligence and monitoring. This paradigm will require technology and teams to adapt to new technologies that use digital capabilities, as well as the ability to make rapid changes based upon data trends.
• Patients – RBM drives technology based efficiency. However, it is equally important that this efficacy also applies to the patient. Patient safety and comfort remain top priorities throughout the entire life cycle of a clinical trial. Collaboration with patients, sponsors, and sites ensures that RBM integration is not compromised on quality and compliance.

Additional Modernization of RBM

RBM is becoming the preferred monitoring method for clinical trials. It combines the best of existing monitoring methods and the flexibility required to keep them on track in the face common obstacles and unique challenges. Recent developments in remote monitoring and decentralization have led to a greater understanding of RBM and a more robust application. This will further propel the evolution of monitoring solutions.

RBM has the ability to adapt to changes across all stages of the clinical trial lifecycle, making it an attractive candidate for modernization. This includes predictive analytics and AI as well as greater interoperability and integration with other formats. Data integrity and oversight will remain constants when analyzing clinical trial data.