New strategies to manage clinical trial risk
It is essential for healthcare and pharmaceutical companies to be aware of both critical and non-critical risks when conducting quality clinical trials. However, managing both takes time and money — resources that clinical teams are often strapped for. Additionally, the risks that organisations define at the start of the trial may change, meaning the data they need to collect will also change.
In order to address these challenges, researchers must break down silos and create a centralised process for monitoring and managing risk. Many organisations are turning to risk-based quality management (RBQM) practices to make that happen. RBQM technologies help manage data quality throughout a clinical trial and often leverage artificial intelligence (AI) and analytics to reduce human errors and improve data integrity.
RBQM is changing the way trial monitoring is done as it anticipates and integrates risk control and management during the course of a clinical trial, providing enhanced data quality as one of its many benefits.
Quality management historically
Historically, identifying critical risks for primary focus has not been a cross-functional process, making it difficult to holistically identify risks from multiple perspectives. Individual roles on the monitoring team have defined their own risks and review them with a siloed focus. Therefore, they may be unaware of risks that exist from other stakeholders, potentially causing duplicate work due to misunderstanding the risks cohesively.
Unfortunately, this lack of transparency and integration can lower the overall quality of the data collected.
Increasing adoption of risk-based quality management
Thanks to both COVID-19 pandemic restrictions and technological advancements, the remote risk monitoring process has garnered more attention due to the ability to review data remotely. Trials that deploy electronically captured assessments enable more data to be available in near real time. Trial subjects benefit from being able to partake in the studies with potentially less travel to sites, which typically enables stronger assessment compliance and enables more subjects to participate.
Fortunately, the International Council for Harmonization (ICH), which sets ethical and scientific quality standards, acknowledges the value of RBQM. In April 2022, RBQM became a regulatory requirement that is in full effect as part of ICH’s guidance ICH E6(R2). The guidance states that it is the sponsors’ responsibility to implement a system to manage quality throughout all stages of the trial process. It is therefore important that trial sites, sponsors, and CROs adjust quickly to the new regulations. Since ICH guidelines are primarily around trial design, it is crucial to have a deep understanding of the new guidelines to create trial protocols.
However, the ICH guidance can be overwhelming and confusing to understand, which is why many are turning to RBQM applications that integrate overall risk assessment and the regulations to reduce the risk and add value during their clinical trials. Additionally, RBQM applications leverage AI algorithms and statistical models for more informed decision-making and risk mitigation. RBQM technology enables researchers to address issues off-site, improving subject safety, data flow, and data quality. It also identifies high-risk sites for early intervention with predictive analytics, while simultaneously enabling near real-time data reviews to identify ongoing risks. This enables a proactive approach, allowing for greater adherence to the protocol and regulatory requirements, while also reducing the chances that risk occurs as the trial progresses.
Approach to managing critical and non-critical risks
Despite monitoring for different types of risks, organisations have to approach critical and non-critical risks differently to ensure they don’t derail the trial. RBQM supports this process.
Non-critical risks
When a non-critical risk is identified, the action taken will likely be less involved and is unrelated to the essential data needed to assess the endpoint analysis. The action will be influenced by the risk plan and likely addressed by more ancillary project team roles.
Critical risks
When a critical risk is identified, immediate pre-defined actions enable quick mitigations to be conducted. The actions likely include a central monitor to contact and notify the site of the issue and seek resolution. The risk may need to be escalated beyond the central or remote monitoring role, based on the criticality of the issue – which would be pre-defined, as well.
When escalated to another role (e.g., CRA, Medical Monitor), the communication includes the status of the risk, the analysis performed, and historically or tangentially related information that can help the role understand the risk at hand and the actual root cause. Pre-defined actions may also include trigger and ad hoc CRA monitoring visits to resolve the issue on-site.
The resulting actions that were conducted or are pending completion are documented as part of the identified risk. The project team, when meeting at a regular cadence, tracks the issues/risks, resulting actions, and if they resurface. Higher risk sites may require more frequent onsite visits to ensure compliance compared to sites at low risk.
Why siloed risk monitoring is problematic
When organisations monitor risks in a siloed fashion, it is challenging to get a holistic view of the entire study. It is difficult to spot trends, risks, and outliers immediately, posing challenges to ensure quality data and subject safety. This creates the conditions for continual issues at the trial, site, and subject-level to be identified. A small event that could potentially be mitigated very early, can continue to cascade to other subjects or other sites. Unfortunately, this often results in poor data quality, redundancy, mismanagement, and wasted financial resources.
Putting strategy into place - Focus on what matters
When it comes to risk assessment, early detection and intervention can prevent significant, and possibly permanent, damage to a trial. Technology tools that use AI/ML should be designed to provide research teams with the power to make sense of the mammoth amount of data collected through clinical research. Integrated monitoring is a combination of central, remote, and on-site monitoring across multiple project roles that leverages quality system management, including advanced AI and ML techniques with data analytics and automated workflows. The combination shifts the focus to reliable and critical data, as compared to unreliable and non-critical data, reducing the time and effort it takes to monitor a trial.
The analytics also need to be flexible and generate true insights for monitors to leverage metrics, enabling a true risk-based approach to quality management. In sites with the highest risks, quality issues or even potential quality issues are identified through technology and intelligence, leading to more frequent and near real-time issue resolutions. Additionally, automation should be leveraged for the overall improvement of efficiency to ensure that the issues are surfaced and addressed in a timely manner.
Leveraging technologies like AI/ML to generate insights and predictions can enable monitors to focus attention on riskier sites, sites predicted to have risks, and mitigate risks as the trial progresses. But insights are only half the battle. It is just as important to focus on change management and training for all project roles who monitor, to prepare them to understand these insights and what actions to take based on them.
RBQM won’t happen overnight
As clinical trials become more and more decentralised, technology and experience can support a cross-functional project team to identify potential issues. This will reduce the likelihood of missing risks. Planning, through an intelligent technology application, is part of the solution and a key factor in driving risk detection. Risk detection is initiated through the risk planning process by defining the risks and thresholds. Project teams should be able to easily and flexibly modify thresholds based on outcomes monitored in real-time or what they've seen historically to be the most effective in the past. The process to adapt risks and thresholds should be standardised, flexible, and require next to no manual programming. Additionally, decentralised clinical trials will likely increase in numbers, as long as they are successful and get better subject and site engagement. They will also require RBQM processes in place in order to review the remote data captured.
Transitioning to RBQM can take time and can feel like an enormous task to implement, but it doesn’t have to happen all at once, nor does it have to be difficult. Organisations can adopt RBQM processes in a stepwise manner versus a full-blown, risk-based quality management approach from the onset. Implementing site risk identifiers or Key Risk Indicators (KRIs) is a sensible introduction into RBQM and you can scale up from there. For those organisations that already have risk-based monitoring systems in place, they can add automation and alerts to push them toward RBQM if the system allows or change to a solution that allows them to implement the full RBQM approach.
Research organisations need to access centralised quality management to keep from duplicating work, wasting money, and focusing on the wrong issues. Instituting a full end-to-end RBQM process complete with intuitive, intelligent, interoperable technology will be the key to future clinical trial success.
About the authors
Gayle Hamilton, director of RBQM for IQVIA’s Digital Trial Management Suite, is an experienced Risk-Based Monitoring project advisor and project lead, with a strong background in clinical operations and project management. She has supported study trial teams and IT development in RBQM implementation across all phases and therapeutic areas, driving the development of processes, tools, and systems. Hamilton is also experienced in assuring high-quality business performance of clinical operations within global projects.
Adrian Kizewski, associate director of RBQM for IQVIA’s Digital Trial Management Suite, brings expertise spanning R&D and clinical life sciences, business analysis, process design and improvement, and product implementation. He is currently a lead for IQVIA’s Risk-Based Quality Management SaaS solution. Kizewski holds an MBA from the McDonough School of Business at Georgetown University, in addition to an MSc in Pharmacology from The John Hopkins University School of Medicine and a BSc in Biochemistry from Temple University.
