Industry Trends: Cycle Time to Resolve Risk Signals

Published on: 
Applied Clinical Trials, Applied Clinical Trials-06-01-2023, Volume 32, Issue 6

Metric derived from CluePoints central monitoring platform assesses average total cycle time from risk signal creation until closure.

Central monitoring aims to detect emerging quality-related risks proactively during a clinical trial, resulting in study team intervention to address any confirmed issues and thereby drive optimal quality outcomes. A variety of tools may be applied to support central monitoring, but statistical data monitoring (SDM) and key risk indicators (KRIs) are the most commonly used. When central monitoring reviews detect risks (e.g., atypical data patterns or KRI thresholds exceeded), a risk signal is created which initiates the process of study team review and follow-up to address the risk.

In this article, we present a metric assessing the average total cycle time from risk signal creation until closure (e.g., resolution). This metric was derived using risk signals documented on the CluePoints central monitoring platform over the past three years, which covers 534 studies across 49 different research organizations.

Research organizations take 35 days, on average, to process and close each risk signal, as presented in Figure 1 below. There is a relatively broad distribution around this average, with 25% of the research organizations taking less than 16 days on average (Industry P25) and 25% taking more than 74 days (Industry P75). Interestingly, as presented in Figure 2, the average cycle time is very similar for risk signals detected by KRIs vs. those detected by SDM.


The broad distribution observed in risk signal processing cycle times might reflect a number of contributing factors, including the relative level of experience a given organization has with central monitoring and/or the effectiveness of training and engagement with all cross-functional stakeholders. It may also simply reflect differences in approach to risk signal processing. For example, some organizations may prefer to close risk signals as soon as remedial actions have been taken, while others prefer to keep the signals open until they have confirmed that the relevant data has improved.