Risk-Based Monitoring in Clinical Trials

How can clinical trials guarantee both patient safety and data integrity without becoming prohibitively costly or complex? Risk-Based Monitoring (RBM) answers this by focusing oversight where it is needed most – streamlining trials while upholding rigorous standards. But with rising trial complexity and growing global demands, can we really afford to rely on outdated, one-size-fits-all monitoring approaches? This article aims to answer some important questions on the topic of RBM and why it is a necessity in modern-day clinical trials.

What is risk-based monitoring?

Risk-Based Monitoring (RBM) offers efficient observation based on real-time risk assessment. Instead of routinely checking all data, clinical monitoring teams apply a superior risk assessment framework. They monitor critical endpoints and protocol deviations using remote centralized monitoring techniques and precise prediction tools. 

Clinical trial monitoring once relied on in-person visits and exhaustive source data verification. While thorough, that traditional monitoring process proved inefficient, especially as trials grew in size and complexity. It was much like a nurse personally supervising every patient, every day – possible in a small clinic, but unsustainable in a large hospital. Today, with hundreds of patients enrolled across multiple sites, smarter tools are essential. With the rise of digital transformation in healthcare, RBM emerged as a smarter clinical monitoring model. It uses integrated central monitors and site analytics to track study activity in real time. [1,2]

Key principles of risk-based monitoring

Risk-Based Monitoring in clinical trials follows a simple rule: monitor where it matters. This principle guides the shift from traditional monitoring models to an innovative monitoring solution that adapts to real-time conditions and risk factors.

At its core, RBM uses continuous risk assessment to identify the most critical study elements. These include subject data review and factors that directly impact patient safety. Such factors may include high dropout rates or abnormal lab values. Central monitors, equipped with digital tools, detect trends and anomalies through remote centralized monitoring. These tools often involve data visualization dashboards and machine learning models designed to highlight site-level discrepancies or unexpected safety signals. This proactive method flags issues earlier, allowing clinical monitoring teams to respond before errors escalate.

A strong RBM operating model relies on a few foundational elements. First, an integrated central monitor uses a centralized monitoring technique to oversee data flow. This setup removes blind spots caused by sporadic site visits. Second, the system uses granular risk detection. It drills deep into subject-level data to uncover inconsistencies and protocol violations. The third core component is flexibility. Every clinical trial operates under a unique protocol and a specific patient population, following a local regulatory environment. RBM adjusts to these variables. This integrated approach results in better quality management and faster decision-making. [1,2]

The evolution of monitoring in clinical trials

Clinical trial monitoring has changed dramatically. What started as frequent on-site visits and complete source data verification has now shifted toward remote, risk-informed decision-making. 

Traditionally, monitors traveled to every trial site. They reviewed all data points manually. This approach guaranteed thoroughness but lacked agility. As trials expanded on a global scale, the traditional monitoring process was seen to become slower and slower. This method consumed the time and effort of medical staff, but often yielded no results. It frequently missed the chance to detect emerging risks early.

Risk-based monitoring in clinical trials solved that. Static monitoring schedules gave way to dynamic risk models. Centralized monitoring techniques began to dominate. Digital RBM platforms began tracking patient data remotely and continuously. This shift helped teams prioritize trial areas that truly needed attention. Instead of checking everything, they focused on signals that are risk indicators tied to compliance or data quality.

This shift didn’t just save time. It transformed clinical monitoring. The RBM solution fostered faster issue resolution and better patient protection. The industry started embracing this integrated workflow as a smarter alternative. [3]

Benefits of RBM over traditional monitoring approaches

Risk-Based Monitoring in clinical trials offers distinct advantages that traditional monitoring models cannot match. It transforms oversight from exhaustive to strategic. These are only some of the ways in which RBM is superior to conventional monitoring:

• Improves data accuracy 

Clinical trials using risk-based monitoring reported fewer errors in key outcome variables. Centralized monitors identified inconsistencies early, triggering site support only when needed. Instead of spreading attention across all data points, RBM redirects clinical monitoring toward critical signals such as adverse events and protocol deviations, improving both reliability and response time.

• Reduces monitoring costs without compromising safety

One of the most significant advantages of RBM lies in its ability to reduce costs. Studies have shown that replacing routine on-site visits with remote centralized monitoring reduces total trial monitoring costs significantly. Instead of visiting every site regularly, central monitors use site analytics and a comprehensive risk dashboard to decide when and if a visit is necessary. This makes site visit transportation more strategic and less frequent.

• Streamlines oversight using centralized monitoring techniques

RBM systems rely on digital transformation. Integrated central monitors work off real-time data pulled directly from electronic data capture platforms. This setup enables more accurate subject data review and supports proactive methods for risk management. Many tools now offer software-as-a-service (SaaS) models, allowing sponsors to customize the RBM operating model.

• Targets site-level issues before they escalate

The best-performing RBM tools in trials use risk indicators tied to site behavior (recruitment delays, inconsistent CRF entries, frequent protocol violations). These systems automatically flag irregularities to make the escalation faster. 

• Boosts oversight consistency across global sites

RBM enables central monitors to access live data from every trial location. The comprehensive risk dashboard reveals outliers and emerging issues, making it easier to apply consistent quality management across all regions.

• Accelerates decision-making using real-time risk indicators

With automated alerts based on risk scores, monitors no longer wait for site visits to act. They intervene early, which improves patient safety and maintains data flow.

Risk-based monitoring prioritizes action over routine and empowers teams to deliver better outcomes through digital transformation. [4,5]

Regulatory guidelines and compliance in RBM

Regulatory guidelines play a critical role in shaping how risk-based monitoring (RBM) unfolds in clinical trials today. Regulators worldwide recognize that an adaptive approach to clinical monitoring ensures patient safety and enhances data quality while responding to the increasing complexity of modern trials. 

The U.S. Food and Drug Administration (FDA) has provided clear direction on RBM through its 2013 guidance on Oversight of Clinical Investigations – A Risk-Based Approach to Monitoring. The FDA stresses the importance of source data verification and subject data review, focusing people’s attention on places where the risk factors tell us the issues are arising. This proactive method contrasts sharply with the traditional monitoring process, which relied heavily on frequent on-site visits and extensive source data verification at all sites regardless of their risk profile.

Similarly, the European Medicines Agency (EMA) supports the use of innovative monitoring solutions that integrate site analytics and centralized monitoring techniques. The EMA’s position encourages sponsors to develop a comprehensive risk dashboard that offers granular risk detection, enabling central monitors to oversee trial quality effectively. 

Compliance with these regulatory frameworks requires a sophisticated RBM operating model. Sponsors must incorporate continuous risk assessment and adapt monitoring intensity based on evolving clinical trial data. Digital tools that facilitate integrated workflows become indispensable in maintaining quality management and consistent support to site staff.  [6,7]

Implementation strategies for RBM in clinical trials

Deploying risk-based monitoring in clinical trials requires a cultural and structural shift. Successful implementation begins with a superior risk assessment that identifies the most critical variables early in the process. These variables shape a tailored monitoring model that supports the trial’s goals.

Sponsors must first conduct a structured risk assessment across all aspects of the protocol. This includes:

• subject data review workflows;
• investigational product handling;
• data management plans; 
• the local regulatory environment. 

A strong implementation plan also prioritizes the deployment of an integrated RBM platform that enables real-time clinical monitoring. Such a platform allows central monitors to track trends and deviations and offer site support promptly. 

Training plays a critical role here. Without proper instruction, even the most innovative monitoring solution can fail. Monitors need to understand the logic behind ML models that drive risk indicators. When teams align their understanding, they can respond quickly to potential issues using proactive methods. The full embrace of these tools means that the technology becomes part of the teams’ everyday workflow – a trusted partner that helps them spot and solve issues faster.

Organizations must also ensure their RBM solution integrates seamlessly with their existing quality management systems. If the monitoring tool remains isolated, data delays or conflicting metrics can undermine the entire trial. The system’s hand-in-hand work with humans guarantees the expertise of clinical staff. Integration promotes consistency and makes it easier to demonstrate compliance with regulators. 

Lastly, site staff need consistent support throughout the implementation process. Many sites struggle with the shift from full source data verification to a more focused model. By offering clear expectations and tools, sponsors create a collaborative environment where technology and people work together toward shared goals. [6]

Challenges and limitations of RBM adoption

Organizations may face multiple hurdles when they adopt risk-based monitoring. 

First, teams debate definitions. Some focus solely on reduced source data verification; others stress remote centralized monitoring or risk indicator triggers. This confusion stalls a unified RBM operating model and weakens integrated workflow design.

Next, technology gaps impede progress. Legacy systems often resist integration with comprehensive risk dashboards or ML model-driven risk assessment. Without real-time clinical monitoring tools, central monitors can’t detect risks precisely. RBM should be seen as a tool to help people do their jobs better, not just a tech system.

Cultural resistance appears when monitors trained in full on-site visits doubt adaptive approaches. They may fear audits if they shift to a proactive method. Sponsors must secure leadership buy-in and deliver training on interpreting risk indicators and leveraging integrated central monitor platforms. 

Process gaps also arise. SOPs often reflect old workflows without triggers for centralized monitoring or clear escalation based on superior risk assessment. Rewriting SOPs takes effort but ensures quality management across functions. 

Regulatory uncertainty magnifies hesitation: stakeholders worry that inspectors may question reduced SDV without a clear risk assessment rationale. ICH E6(R2) requires a justified monitoring plan that ties monitoring intensity to critical process and data identification.

Smaller sponsors and CROs face resource constraints. They need cost-effective RBM solutions that integrate site analytics and digital tools. Data integration challenges persist: disparate systems (EDC, CTMS, safety databases) often remain siloed. Overcoming this demands focused investment in digitalization and an integrated workflow.

Despite these challenges, organizations can gain the full benefits of RBM by remembering that clinical trials are about people – researchers, staff, and patients working together. [7]

Case studies: successful applications of RBM

The COVID-19 pandemic forced many teams to adopt remote-site monitoring. This shift acted as a natural experiment for RBM in clinical trials. Mid-pandemic data shows trial teams differentiated COVID-related deviations from other deviations. COVID-related deviations included:

• missed visits; 
• delayed sample collections;
• changes in protocol due to lockdowns or patient safety measures. 

Other deviations, such as data entry errors or protocol non-compliance unrelated to the pandemic, remained stable. They saw little change in non-COVID deviations despite lacking physical access to sites. These findings suggest that remote-site monitoring matches the performance of on-site monitoring seen before COVID.

Teams can draw lessons from pandemic-era trials. They can refine integrated RBM operating models using digital tools for remote centralized monitoring. They can build comprehensive risk dashboards that highlight risk indicators. They can train central monitors to interpret ML-driven signals and trigger targeted site support, and can update SOPs to codify when remote access makes sense. 

The FDA’s April 16, 2020, update urged sponsors to evaluate technologies carefully and apply risk-based methods to any unplanned remote source data collection. The guidance warns against interpreting flexibility as endorsement of 100% SDV long term. Trial leaders should document monitoring plan changes and rationale. They should engage regulators early when devising adaptive monitoring models. By treating the pandemic as a learning opportunity, sponsors can advance an adaptive approach to clinical monitoring. [9,10]

Future trends in RBM and adaptive monitoring

Risk-based monitoring will evolve as trials embrace digital transformation and integrated RBM. The growing use of eSource, wearable technologies, and EHR interoperability will shift data capture toward direct patient inputs and device feeds. 

Sponsors will apply predictive analytics and ML model outputs to drive granular risk detection and precise prediction of site performance. Centralized monitoring platforms will link site analytics with comprehensive risk dashboards. Teams will adopt an adaptive approach that targets critical risks rather than aiming for full SDR/SDV. 

Regulators will avoid one-size-fits-all mandates and focus on robust risk assessment rationale. Quality management will integrate proactive methods into workflows. Integrated central monitor tools will adapt thresholds as novel therapies and decentralized trials emerge. This future relies on consistent support for sites and superior risk assessment.

For patients, this means safer trials that respond quickly to emerging risks and fewer unnecessary procedures or visits. More accurate, real-time data capture from devices and direct inputs will make participation easier and more comfortable. Ultimately, patients benefit from faster, more reliable development of new treatments. [11]

Conclusion: The growing importance of RBM in clinical research

RBM operating models guide teams toward breakthrough medical treatment faster and protect every patient. The critical role of RBM will only grow, making each clinical monitoring effort more resilient and patient-centered. Do you want to be part of the healthcare that suits everyone perfectly? Contact BGO Software for the design of your clinical trial’s future.

References:

• [1] Jaguste V. S. (2019). Risk-based monitoring: Review of the current perceptions and toward effective implementation. Perspectives in clinical research, 10(2), 57–61. https://doi.org/10.4103/picr.PICR_18_18
• [2] Limaye, N., & Jaguste, V. (2019). Risk-Based Monitoring (RBM) Implementation: Challenges and Potential Solutions. Therapeutic innovation & regulatory science, 53(2), 183–189. https://doi.org/10.1177/2168479018769284
• [3] Velstra, I.-M., & Frotzler, A. (2020). All research needs to follow the rules set down by Good Clinical Practice. Spinal Cord, 58(9), 947–948. https://doi.org/10.1038/s41393-020-0509-4
• [4] Hurley, C., Shiely, F., Power, J., Clarke, M., Eustace, J. A., Flanagan, E., & Kearney, P. M. (2016). Risk based monitoring (RBM) tools for clinical trials: A systematic review. Contemporary Clinical Trials, 51, 15–27. https://doi.org/10.1016/j.cct.2016.09.003
• [5] Hurley, C., Shiely, F., Power, J., Clarke, M., Eustace, J. A., Flanagan, E., & Kearney, P. M. (2016). Risk based monitoring (RBM) tools for clinical trials: A systematic review. Contemporary Clinical Trials, 51, 15–27. https://doi.org/10.1016/j.cct.2016.09.003
• [6] U.S. Food and Drug Administration. (2013). Oversight of clinical investigations — A risk-based approach to monitoring. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/oversight-clinical-investigations-risk-based-approach-monitoring
• [7] European Medicines Agency. (2013). Reflection paper on risk-based quality management in clinical trials. https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-risk-based-quality-management-clinical-trials_en.pdf
• [8] Manasco, P. (2018, December 1). Risk-based monitoring: Barriers to adoption. Applied Clinical Trials, 27(12). https://www.appliedclinicaltrialsonline.com/view/risk-based-monitoring-barriers-adoption
• [9] Fneish, F., Schaarschmidt, F., & Fortwengel, G. (2021). Improving risk assessment in clinical trials: Toward a systematic risk-based monitoring approach. Current Therapeutic Research, 95, 100643. https://doi.org/10.1016/j.curtheres.2021.100643
• [10] Barnes, B., Stansbury, N., Brown, D., Garson, L., Gerard, G., Piccoli, N., Jendrasek, D., May, N., Castillo, V., Adelfio, A., Ramirez, N., McSweeney, A., Berlien, R., & Butler, P. J. (2021). Risk-Based Monitoring in Clinical Trials: Past, Present, and Future. Therapeutic innovation & regulatory science, 55(4), 899–906. https://doi.org/10.1007/s43441-021-00295-8
• [11] Vyas, N. R. (2020). Future of risk based monitoring in clinical trials. International Journal of Clinical Trials, 7(3), 221–228. https://doi.org/10.18203/2349-3259.ijct20203109

Dobrin Kolarov

Healthcare business analyst with expertise in marketing and business development, and holds an MPharm degree. He specialises in creating and executing communication strategies that make digital health solutions and pharmaceutical technologies clear, accessible, and resonation for their audiences.