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A resident-driven quality improvement pilot study on the implementation of an Early Warning System score

By: T. Swami, A. Shams, M. Mittelstadt, C. Guenther, & R. Shahid

Introduction: The Early Warning System (EWS) communication tool has been successfully implemented in various institutions around the world. Using individual vital signs records, this simple bedside score triggers a communication algorithm and ultimately early intervention for deteriorating patients.

Our study aims to answer: Will the EWS score improve patient outcomes when implemented on a Clinical Teaching Unit?

Methods: This is a prospective pre-post quality improvement study conducted on the 6200 ward at Royal University Hospital. Nurses and residents were trained to utilize the EWS, comprised of 7 physiologic parameters including patients’ vital signs. The score ranges from 0-20, with ≥5 defined as a high score.

We identified patients six weeks pre- and six weeks post-EWS implementation on December 9 2019 with outcomes of death, transfer to the observation or critical care units, cardiac arrest, and septic work-up. We identified the EWS scores associated with each outcome and compliance to the EWS protocol.

Results: There were 15 patients pre- and 24 patients post-EWS implementation who experienced an outcome. Pre-EWS implementation, there were 28 events (5 septic work-ups, 14 transfers to observation units, 2 critical care transfers, 1 cardiac arrest, 6 unexpected deaths) and 41 events post-EWS implementation (10 septic work-ups, 14 transfers to the observation unit, 8 critical care transfers, 4 cardiac arrests, and 2 unexpected deaths).

The average EWS score for an event was 3.87. The algorithm was employed 41.4% of the time overall. Compliance rates were 46.2% for a low score and 60% for a high score.

Further data analysis is forthcoming.

Discussion: Preliminary results suggest the EWS may detect sepsis earlier and reduce the number of deaths; however, this pilot study is limited due to a small number of events and low compliance. Future directions will be aimed at increasing adherence through re-education of the EWS communication algorithm.

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