Understanding Clinical Decision Support Failures in Pediatric Intensive Care Units via Applied Systems Safety Engineering and Human Factors Problem Analysis: Insights From the DISCOVER Learning Lab.

Abstract

Objectives: Children receiving care in pediatric intensive care units (PICUs) are vulnerable to decompensation and diagnostic error due to the complex and dynamic nature of pediatric critical illness. In the PICU, the few clinical decision support (CDS) tools that have been implemented to support diagnostic accuracy (i.e., the ability to detect the presence of a condition) have not led to an increase in clinician adoption of desired practices nor demonstrated clear clinical benefit.

Methods: The DISCOVER Learning Lab analyzed workflow and failure modes in diagnosing and managing clinical decompensation in the PICU, using systems safety engineering and human factors to examine intersections with established CDS. Methods employed included qualitative interviews, workflow mapping, immersive virtual reality (VR) systems testing via a digital twin environment, and a failure modes effect analysis.

Results: Workflow mapping and qualitative interviews revealed barriers to communication, workflow inefficiencies, and limited access to up-to-date clinical information during critical events in the PICU. The immersive VR systems testing elucidated how PICU staff members currently interact with CDS tools and how various tools could better integrate into or influence clinical workflows. Critical failure modes were identified with corresponding opportunity areas for intervention.

Conclusions: The application of a systems safety engineering and human factors approach to problem analysis, partnered with novel use of immersive VR and digital twin technology, led to valuable insights into common failure modes and potential opportunity areas to improve diagnostic accuracy and care delivery in a quaternary referral center PICU.