Author ORCID Identifier

0000-0001-5652-8769

Date Available

4-16-2023

Year of Publication

2023

Degree Name

Doctor of Nursing Practice

Advisor

Dr. Sheila Melander

Committee Member

Dr. Jacob Higgins

Committee Member

Dr. Lacey Buckler

Co-Director of Graduate Studies

Dr. Ashley Montgomery-Yatres

Abstract

Abstract

Background: The increased workload bedside nurses face today requires new tools to assist with the identification of deteriorating patients during hospitalization. The Modified Early Warning Score (MEWS) tool has formed the background of early warning tools. Newer, more complex tools, like Epic’s Deterioration Index (EDI), have been developed to identify patient deterioration earlier. There is lack of evidence in the literature comparing different early warning tools, implementation, and patient outcomes.

Objective: The purpose of the study was to examine models for EWS notification for RRT and patient outcomes between the use of the MEWS and EDI in an adult, acute care in-patient setting.

Methods: This study was a retrospective analysis of admitted adult patients hospitalized during two different 3-month intervals. This study compared the 3-tier alert trigger (RN: 45, Provider: 55. Rapid Response Team: 65) for the EDI to the MEWS’ one alert trigger (MEWS >6). The study endpoints examined were Rapid Response notifications, in-hospital mortality rate, hospital length of stay (LOS), code blue activations, unexpected transfers to the intensive care unit (ICU), mechanical ventilation after a rapid response activation, and the use of supplemental oxygen after rapid response activation. Data analysis was performed using descriptive and correlational statistics.

Results: A total of 12,210 patients were examined (n = 6,602 in MEWS cohort and n = 5,608 patients in the EDI cohort). Significant differences were found in Rapid Response notifications (MEWS: 370, EDI: 251, p=0.005), LOS (median: MEWS 1.99, EDI 1.79, p=0.012), unintended ICU transfers (MEWS: 243, EDI 145, p =

Conclusions: The EDI in tandem with a proactive model of monitoring for deteriorations demonstrated to have better patient outcomes as compared to the MEWS’ reactive model.

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