A robust clinical trial risk management program requires more than a checklist and good intentions. To adequately protect patients and ensure the reliability of the clinical trial data, it’s vital to infuse the process with “critical thinking,” says Leslie Sam, BA, LSS BB, CIQ, Principal Consultant and Risk Management and Issue Management Practice Lead for Wool Consulting Group.
Successful risk management begins with gathering key players together early in the process, Sam says. “Bring together the study team—for example, the biostatistician, data manager, medical monitor, the study manager, and vendors—and make it a true cross-functional exercise, to identify risks and determine strategies for mitigating the risks,” she explains.
The study team should examine existing processes and look for weaknesses or areas where something could go wrong during the trial, Sam advises. “Lean on their experience, because they’ve seen a lot in previous studies” and their knowledge can help you identify and/or mitigate quality shortcomings, she says.
In addition to obvious areas where patient safety might be at risk, Sam suggests burrowing down deeply with a focus on nuance. For example, data entry delay might sound like a project management operational issue, but it can impact quality, too, she notes. “What if that data entry delay prevents you from making an important course correction earlier in the trial?” she asks. “The slow data entry may potentially impact the overall quality of the clinical trial by limiting the most proactive and early response to a problem,” she adds.
Sam is also a big proponent of “baking” risk management activities into existing systems and processes. Whether it’s protocol development or another aspect of the clinical trial, “it’s vital for the study team to understand existing processes and identify ways to enhance them with effective risk management practices,” she says.
“Look at what could go wrong in the study and assess its impact on patient safety and data integrity,” Sam advises. Biostatisticians can be especially helpful when working to determine when a realized risk (for example, a protocol deviation or issue) is a relatively isolated incident and not threatening to overall trial quality, or perhaps indicative of a bigger problem meriting closer examination (for example, a full-blown root cause analysis and corrective and preventive action steps), she adds. “Biostatisticians can tell you when a deviation is a show-stopper” or a simpler case of something that calls for course correction, she says.
Author: Michael Causey
