Improving escalation of deteriorating patients through cognitive task analysis: Understanding differences between...

Area of Science:

• Healthcare delivery
• Patient safety
• Clinical workflow optimization

Background:

• Effective care escalation is crucial for detecting and communicating in-hospital patient deterioration.
• Despite existing protocols, patient deaths still occur due to failures in escalating care.
• Understanding real-world healthcare professional practices (work-as-done) is key to addressing escalation challenges.

Purpose of the Study:

• To develop a model of care escalation processes.
• To identify performance variability impacting escalation.
• To examine interdependencies within the escalation process.

Main Methods:

• Applied Cognitive Task Analysis interviews with 30 clinical experts from various roles (nurses, physiotherapists, doctors, etc.).
• Analysis using Framework Analysis to identify task-related elements of care escalation.
• Representation of findings using a Functional Resonance Analysis Model.

Main Results:

• Identified 32 tasks in work-as-done, expanding on the 8 tasks in work-as-prescribed (NEWS2 escalation).
• 28% (9/32) of these tasks were identified as cognitively challenging with high performance variability.
• Three interdependent tasks ('synthesizing data', 'critical escalation decision', 'identifying interim actions') represent potential failure points.
• Poor usability of clinical information systems hinders data assimilation and increases cognitive load.

Conclusions:

• Clinical judgment is essential and requires flexibility in escalation protocols and audits.
• Improved information systems are needed to synthesize patient data, reduce cognitive load, and support decision-making.
• Reducing cognitive load in data assimilation enables healthcare professionals to provide better, more creative care.