Patient Centric Monitoring: Preventing And Learning From Mistakes
When validating the 1,000,000 data points that compose the typical Phase III trial, focusing on errors that don’t matter easily leads to wasted resources. The industry’s initial response — risk based monitoring — more efficiently detects errors and risks, but can still expend resources on errors irrelevant to a trial’s validity and miss opportunities to detect and solve critical quality issues.
At ICON, we’ve drawn upon other industries’ methodologies for risk mitigation to evolve RBM into a more useful approach for protecting your clinical data. One of those methodologies, Human Factor Analysis (HFA), is employed by NASA, Ford, the U.S. EPA, major airlines, and several countries’ militaries to identify and mitigate human error in large, variously trained work forces. HFA uses uniquely structured datasets to reveal underlying behaviours and factors that are otherwise difficult for humans to sense or reconstruct, but ultimately are the root cause of an incident.
For example, take an airline that experienced several mid-air collisions. An HFA investigation, driven by datasets in which behaviours have been classified by analysis, may reveal that in most cases crashes are caused by a subset of miscommunications and gaps in teamwork among pilots and air traffic control. With an accurate understanding of the root cause of these crashes, airlines could mitigate risk by deploying tactical decision-making training or SOP adaptations that address the human errors that ultimately increased collision risk.
ICON has incorporated human factor analysis and into its risk based monitoring approach, which we call Patient Centric Monitoring. ICON’s information platform, ICONIK, systematically classifies and analyses the causes of trial errors to help CRAs deploy the right risk-mitigation resources to the sites that need them most.
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