Clinical trials can often take between six and seven years to complete, but that timeline isn’t always practical for the problems pharmaceutical companies are trying to solve. Additionally, six years is a long time for a project not to be earning revenue for the organization. As important as clinical trials are, they’re becoming increasingly complex, making them difficult to manage and very expensive, especially if organizations spend too long focused on the wrong data.

Many organizations are now turning to risk-based quality management (RBQM) to speed up their clinical trial timelines and improve the quality of the trials.

What is RBQM

Risk-based quality management, or RBQM, is the process by which pharmaceutical organizations monitor and manage quality throughout clinical trials. Organizations must identify critical risks early in the planning process to prevent them from wasting time and money on unnecessary data or processes during the trial. Additionally, they must clearly define each role’s responsibilities in data reviews to avoid duplication of effort.

RBM vs. RBQM

Risk-based monitoring (RBM) harnesses technology that provides reviews in real time to proactively identify and manage risks. While RBM used to be the norm, RBQM extends these principles by managing quality throughout all aspects of a clinical trial.

Why faster clinical trials are necessary

Clinical trials are very expensive to run, costing $1.3 billion on average. The high costs of trials also affect the costs of medication for patients. Part of this expense is due to the length and duration of clinical trials. Speeding trials up, so long as the quality remains high, can reduce operational costs and save money for both patients and the organization.

Additionally, clinical trials are too complex as they stand now, with only about 9% of studies resulting in approval by the FDA. Each of the three phases includes more subjects and data than the one before, meaning the organization needs more management capabilities. Adding more monitors without utilizing an RBQM approach also increases the likelihood of duplication of efforts – or that focus will be on the wrong data. In a worst-case scenario, this could force the organization to monitor for the correct data or restart the whole trial.

How AI and RBQM can improve clinical trials

RBQM takes advantage of artificial intelligence (AI) technology to identify critical risks faster and gain insights from the data that organizations collect. With standard and custom alerts, the algorithms can help researchers identify risks quickly and begin the issue management process. Additionally, AI can highlight trends across datasets, even providing predictive analytics, allowing the organization to react more quickly and implement necessary changes.

For example, when looking at site risk, AI can enable a composite of key risk indicators (KRIs), including adverse events, protocol deviations, query aging, and more. By comparing the composite KRI data against data points like enrollment, site exposure time, and subject visits, the algorithm can provide the risk rankings for all sites, a detailed analysis of each risk metric, and a holistic view of risk across each study. With these insights in hand, researchers can take the right actions, focusing the support for high-risk sites and making proactive revisions where necessary.

AI can also help organizations standardize assessments by creating a library of possible risks and cross-referencing the current data against that library – allowing companies to build better operational plans, improving subject safety, and reducing the startup time for a clinical trial.

When looking at labs, for example, AI analyzes vitals and results and compares those to other data from central or local labs. This allows the trial to identify outliers faster, so researchers can enact an action plan and mitigate the risk. Additionally, if they notice similar results in a multiple of the same sub-population, researchers can deploy preventative actions to improve subject safety.

Benefits of RBQM adoption

Speed is one of the biggest benefits organizations can see when they adopt RBQM for their clinical trials. Because RBQM standardizes and centralizes data, they know what the expectations are and can use AI to analyze the data faster. In some cases, they can reduce startup time from four to six months to as quickly as two weeks. Shortening clinical trials also naturally results in significant cost reductions.

Additionally, the quality of clinical trials where RBQM is implemented is simply better. A benefit of decentralized trials using RBQM is subject centricity, making it easier for subjects to access trial sites and provide electronic assessments. With better access, pharmaceutical companies can enroll more trial subjects and improve the quality of trial data.

Adopting RBQM for your clinical trial

RBQM adoption can seem like a daunting task, but by implementing it in a stepwise fashion and with expert support, processes have become easier. Thanks to technological advances, many companies already have some level of RBM processes in place that they can extend to meet RBQM standards. Additionally, many systems have already implemented parts of RBQM on the learning side, leveraging basic statistical models and automating that learning to identify certain risks.

If you’re working with a technology group to build and implement RBQM technology, ensure they have RBQM subject matter experts who have real world experience dealing with risk in clinical trials. The experts should also have knowledge of the regulatory space to help you navigate potential pitfalls, including audit findings. Many organizations, however, don’t have the resources available to build their own systems and research external technology to fit their needs. In that case, ensure the RBQM technology you select includes an intuitive and system agnostic user interface, automation, intelligent workflows, and flexible monitoring options.

Implementing RBQM technology is one of the best ways to begin the adoption process. You’ll get automation to reduce operational costs, keep the trials from being too resource-heavy, as well as better insights into your data. As you build your processes out, you’ll eventually have a full RBQM system in place, speeding up clinical trials, reducing costs, and improving subject safety and trial quality.

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