In Situ Simulation as a Tool to Longitudinally Identify and Track Latent Safety Threats in a Structured Quality Improvement Initiative for SARS-CoV-2 Airway Management

A Single-Center Study

Abstract

Background 

In situ simulation has emerged as a powerful tool for identifying latent safety threats (LSTs). After the first wave of the SARS-CoV-2 pandemic, an urban community emergency department (ED) identified opportunities for improvement surrounding acute airway management and particularly focused on infection control precautions, equipment availability, and interprofessional communication during acute resuscitation. Using the Model for Improvement, a hybrid in situ/quality improvement initiative was implemented using Plan-Do-Study-Act (PDSA) cycles to enhance systems for intubating patients with SARS-CoV-2.

Methods 

Three PDSA cycles consisting of 10 simulations each were conducted from June 2020 through February 2021. Latent safety threats (LST) were identified through an in situ simulation scenario involving a patient with SARS-CoV-2 in acute respiratory failure. LSTs were collected through structured debriefs focused on (1) infection control, (2) equipment availability, and (3) communication. The SAFER-Matrix was used to score LSTs according to frequency and likelihood of harm by members of the ED QI team (SAFER score). The research team worked with the same QI leaders to implement action plans based on scored threats using cause-and-effect and driver diagrams. The Donabedian model was used to conceptually evaluate the quality of interventions upon conclusion of the third PDSA cycle.

Results 

The median SAFER score decreased from 10.94 in PDSA cycle 1 to 6.77 in PDSA cycle 2 to 4.71 in PDSA cycle 3. Across all identified LSTs, the SAFER score decreased by 3.114 for every additional PDSA cycle (P = 0.0167). When evaluating for threats identified as being primarily structure based, there was a decrease in SAFER score of 1.28 per every additional PDSA cycle (P = 0.001). There was a decrease in total count of LST of 0.20 per additional simulation run (P = 0.02) after controlling for shift type, census, perceived workload, team size, and prior attendance in simulations across all PDSA cycles.

Conclusions 

This study presents a blueprint for the utilization of in situ simulation through multiple waves of the SARS-CoV-2 pandemic to identify LSTs and use the SAFER score as a surrogate marker to monitor the impact of interventions for a safer environment for both medical staff and patients.