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International Clinical Trials

Embrace Change

Risk-based monitoring (RBM) is primarily discussed by large life sciences organisations and CROs as they continue to implement this methodology. However, there is still a significant segment of the industry that has not yet transitioned to RBM – most notably, small to mid-sized companies. Some of the key reasons for this delay include the following perceptions and outlooks:
  • RBM is complex and difficult to execute
  • The model is nascent and continues to undergo changes
  • Internal expertise is unavailable to modify the necessary business processes
  • Fear of failure with a ‘wait and watch’ approach • Delayed technology buying decisions: ‘build’ versus ‘buy’
  • Change management challenges within organisations
For organisations wishing to progress towards RBM adoption, an important question is whether the RBM model followed by large businesses will be a good fit for everyone. Today’s RBM system has evolved from a base of traditional data management processes – which works reasonably well in full-service CROs, where RBM becomes an extension and modification of data management processes and repurposing of organisational staff to implement it. Smaller companies generally lack dedicated resources and expertise to develop and adopt RBM. In the absence of a nimble alternate model for executing RBM, this has led to the perception that its implementation is complex and difficult to introduce to smaller businesses. However, there is an alternative approach that is simple and implementable, with minimal change management barriers.

So, what does it actually take for smaller organisations to readily embrace RBM? Below is a step-by-step outline that can help lead to successful RBM adoption. This approach is divided into three segments: process, technology and people.

Process Aspects

The first step in working towards RBM adoption is to get the process right. It is crucial to consider the following aspects:

Risk Prioritisation and Developing a Risk Plan

The key objective of risk planning is to not only identify risk, but also its mitigation by effectively planning and objectively monitoring it without being elaborate and convoluted. There are two major aspects to consider during risk planning:
  • Pursue risks that matter and prioritise them. TransCelerate has formulated a risk assessment and categorisation guiding tool for the industry (1). The intent is to use this approach for continued risk planning in a clinical programme; however, all of this can translate into a very extensive risk outline, with multiple categories posing challenges for actionable planning
  • A lighter starting point for risk planning is to focus on processes linked to the successful scientific and ethical objectives of a study, and then propose risks that can affect the final outcome. A shorter, but critical list is then created from which those that really matter can be distilled into a risk plan and prioritised, based on probability and impact. This will help teams to focus on the highest need risks to be mitigated
  • Once prioritised risks have been listed, the top 10 risks in the order of priority (higher degree of probability and impact) need to be selected. These should be discussed within a team that is comprised of both scientific and medical personnel – including clinical operations, data management and statistical leads – which can assist in crystallising robust mitigation plans and dependencies between different functions to holistically monitor such risks in an aligned manner
In this way, risks can be added to the plan, prioritised and mitigated on an ongoing basis. It is acknowledged that risk planning as a process is relatively new to the clinical research industry, but external support can be utilised on an as-needed basis if such resources are not available within an organisation. The final output is a cross-functional derived integrated quality risk management plan (IQRMP) with proposed mitigation steps.

Transforming Risk Planning into a Data Monitoring Plan

The outcome of risk planning generally provides the following answers pertaining to risk:
  • What is the risk?
  • Why is it important?
  • When can it occur?
A data monitoring plan helps in clearly articulating and setting the foundation for monitoring risks using data points. It addresses the question of which data points will be reviewed, how exactly the risks will be monitored, how frequently they need to be observed, when an alert should be raised and the basis for escalation. The plan would also define data thresholds where applicable, such as when and if data outliers need to trigger alerts. Thus, a data monitoring plan links the risks into definitive data components and processes for manual review or automated signalling for systematic, consistent and scalable review. Finally, it is recommended that a data monitoring plan broadly cover site and subject level risks – along with other monitoring aspects as desired by the study – and be aligned with the site monitoring plan (see full PDF for Figure 1).

Monitoring Risk

The traditional monitoring model is not optimal, as the process is vulnerable to the subjective skill and experience of the site monitor. This is further compounded when ad hoc issues are discovered and managed during monitoring activities, which leads to more workload and less productive time on-site. This has been circumvented in the RBM approach by a combination of centralised data and on-site monitoring:

Clinical Intelligence Through Centralised Monitoring

The RBM approach lays focus on prioritising and reviewing subject data and processes that are linked to scientific and ethical outcomes in an objective and standardised manner. The central monitors can proactively identify issues or non-congruence in activities by reviewing subject and site data; this ensures that data quality is consistent, and decreases downstream surprises during audits or interim analysis. Site monitoring visits (SMVs) can also be rationalised better and objectively based on data. On an ongoing basis, there will be insights compiled by central monitors, which will be available to site monitors to review – bearing in mind they could vary from simple insights to pattern recognition in data.

On-Site Monitoring

In the RBM model, efforts required during an SMV will be dependent on the understanding and performance of a site from a quality and risk perspective. The modified process will be as follows:
  • Utilise the insights and reports provided from central monitoring
  • Perform source data verification (SDV) on subjects where data non-congruence or scientific queries have been noted in the central monitoring report. Adapt the level/percentage of SDV based on the output of central monitoring, as well as process checks during SMV at the site
The focus of monitoring will shift to deriving maximum value of being on site by evaluating study conduct processes at the site. This will include aspects like relationship management (with primary investigator and study staff), understanding the subject pathway followed for conducting study procedures, and assessing logical checks to what is being recorded in source documents. Hence, the expectation from a monitor – on an ongoing basis, and before a SMV – is a comprehensive understanding of safety risks or issues with trial subjects, as well as areas of focus for quality enhancement. All of these lead to the site monitor focusing on high-value activities.

Technology Enablement of Process

Technology needs to effectively enable RBM implementation. Small and mid-tier organisations in particular need nimble and easily configurable solutions to allow a smooth execution of the above processes (see full PDF for Figure 2).

Risk Planning

TransCelerate has created and shared the Microsoft Excel® based risk assessment and categorisation tool, which is derived from programmatic risk evaluation. Realistically, small and mid-size organisations tend to focus on a study-to-study basis, and every trial is considered by the team to be special in its own right. A dynamic, yet simple web tool – which can easily be configured to desired risk categories and enable documentation, assessment and planning for risk mitigation – is ideal. This helps in sharing information across company silos, creating a growing knowledge repository which can then be reused.

Smart Data Capture Systems that Enable RBM

Electronic case report forms (eCRFs) should offer an optimal way to dynamically manage field relationships. For example, if there are leading data points in an eCRF that need to be linked to an endpoint, the system should be able to readily link the field to the endpoint. The opposite is also applicable: if there are leading questions within a study that suggest a particular endpoint matter is unnecessary, the system should be able to hide or disable a field. This aids in decreasing data entry errors, while simultaneously lessening the risk of creating conflicting data. It represents an important step in accurate monitoring, as conflicting data may result in improper risk assessments.

The ability to create custom edit checks to flag endpoints based on specific data characteristics is also of great importance. This includes the capacity to identify data, such as out-of-range, high or low categories. Flags can then be associated with the characteristic that may be a cause for concern. Doing so aids in accurate patient safety risk analysis and drives appropriate actions.

Ongoing Data-Centred Monitoring

Critical data points linked to a central monitoring plan can be reviewed through standard data reports, such as operational data model XML and others. These insights help in manual review when the number of data points, volume of data and extent of correlations are small. However, in typical Phase 2, 3 and late phase studies, where the data and scope for review are more comprehensive, such an approach could mean manual, laborious and error-prone processes. This can be mitigated by using meaningful data visualisations. A system that can provide uncluttered and user-friendly visualisation for analysis, while at the same time helping to solve the end user question of ‘so what?’ works best for small and mid-tier organisations.

The personnel performing reviews should follow the central monitoring plan, which stipulates what needs to be evaluated. Additionally, the ability to define thresholds on certain parameters in a simplistic manner (data outliers more than 1.5 times of normal or desired range, for instance) is key. Advanced alerts can subsequently be configured at a later point in time.

Tracking Observation and Actions

Following data review, its findings and outcomes should be recorded. If there are issues for which action is required immediately, as a part of the routine telephonic call or during SMV, the same needs to be assigned and tracked until the problem is solved. Maintaining all of these in a simple and configurable system ensures that the audit trail for review of subjects, as well as action taken by respective team members, is recorded and available.

People Executing Process through Technology

Implementing RBM further requires the following two major skillsets from staff:

Risk Planning

The cornerstone for successfully adopting RBM begins with robust risk planning. Experience in risk planning and managing clinical trials, along with the acumen of forecasting risks, is critical – therefore, the study team leader, or equivalent, should possess this knowledge and ability. If such a skillset is not available within an organisation, seeking external help from specialised companies or consultants is beneficial. Such a resource should be able to review the protocol and suggest recommendations regarding study risks; they should also be able to work with different functional teams to develop a holistic mitigation plan and an effective IQRMP.

Centralised Clinical Data Review

This is a relatively new activity within the industry; the current standard approach is to repurpose staff (normally site monitors or data managers) to carry out this key role. These groups all have skills that allow them to understand the clinical and monitoring context that enables accurate data review. However, there are a few factors that merit consideration:
  • The reviewer needs to remain independent in their view of the subject’s progress and site performance, without being weighed by positive or negative bias of the clinical operations team or site
  • Analysing data through an in-depth review of correlations, trends and patterns requires an analytic skillset
If no suitable individuals can be recruited in-house, they can be found by either working with data management organisations equipped with central monitoring, or other niche providers who can offer centralised data monitoring on a pre-agreed scope. These partners should have procedures in place that seamlessly blend with clinical operations and data management of the organisation.

Bridging the Gap

To conclude, adopting RBM does not need to be complex and overbearing. It can be achieved by employing a combination of selective process upgrades, applying offthe- shelf nimble and effective technologies, and utilising people who can effectively plan and monitor for risks.

It is time to bring science back into the effective management and monitoring of clinical studies. Small and mid-sized organisations have the opportunity to move up the value chain and leverage their operating flexibility to bridge the RBM implementation divide within the clinical trials industry.


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Abby Abraham is Vice President of Clinical Solutions at Algorics, and has extensive experience within clinical research, including designing solutions for implementing RBM around organisational and study needs. He is responsible for intellectual property development and customer scientific engagement for Acuity, a clinical analytics solution.

Eric Jacobson has over 19 years’ experience in the pharma and medical device industry in a variety of roles, including clinical data management consulting and quality assurance. Currently, he serves as Implementation Consultant at MedNet Solutions, where he focuses on the adoption of the iMedNet eClinical platform for effective clinical trial management.

Abby Abraham
Eric Jacobson
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