AEGIS:  Automated Early warning Generation Information System.  A Quality Improvement Project.
CCCF ePoster library. Rodger L. Oct 26, 2015; 117383; P46 Disclosure(s): No disclosures required.
Lisa Rodger
Lisa Rodger
Login now to access Regular content available to all registered users.

You may also access this content "anytime, anywhere" with the Free MULTILEARNING App for iOS and Android
Abstract
Rate & Comment (0)
P46


Topic: Quality Assurance/Quality Improvement Project


AEGIS:  Automated Early warning Generation Information System.  A Quality Improvement Project.



Lisa Rodger, L. Healy

Medicine, William Osler Health System, Toronto, Canada | Nursing, William Osler Health System, Toronto, Canada

Introduction:

Approximately 50% of ward patients admitted to the intensive care units at William Osler Health System (Toronto, Ontario) have not had a prior consultation by the critical care response team (CCRT). Delays in ICU or CCRT notification average 8 hours from onset of calling criteria. Overall, 25% of all patients admitted to the ICUs originate from the inpatient wards, and 80% of these patients have vital sign abnormalities that include 3 SIRS criteria. In response to these data, we designed a multivariable 'track and trigger' early warning system to enable detection of and response to patients at risk for deterioration on medical wards.



Objectives: To ensure the feasibility of an EMR-based early warning system that generates alerts that are sent to a wireless device carried by a frontline nurse leader.
To evaluate the impact of the early warning system on adverse outcomes occurring on medical wards, including: code blue, unanticipated admission to intensive care, and death.

Methods:

We conducted a quality improvement project on 6 inpatient medical wards at two community hospitals. A multi-parameter early warning system (named AEGIS) was designed to prioritize sensitivity. The hospital’s existing electronic medical record system was programmed to continuously search nurse-entered vital sign data and bloodwork values, generating alerts whenever a patient’s vital sign set included any of the following: 3 or more modified SIRS criteria (HR>89, Temperature >37.9 or < 35 degrees Celcius, RR>19, WBC< 4 or > 12), a shock index (HR/SBP) of >1.3, RR > 27, SBP>200 mm Hg, and HR>130 or < 40. Alerts were sent wirelessly to an iPOD® device carried by the charge nurse on each ward. We provided education to bedside nurses focusing on timely entry of vital sign data into the EMR. The charge nurses were instructed to follow a scalable clinical response algorithm once an alert was received. Outcomes of interest for this quality improvement project included the code blue rate, in-hospital mortality, and the rate of unplanned transfer to an intensive care unit, measured in the 12 months before and 12 months after the implementation of the early warning system on the 6 designated medical wards.



Results:

Despite an expected low positive predictive value of 15% for the outcomes of unplanned ICU admission, code blue, and death, the alert frequency was a manageable 3-6 per day per ward. Outcomes during the 12 months prior to AEGIS implementation and the 12 months post AEGIS implementation on the 6 inpatient medical wards were compared. We found that the average code blue rate for the 6 wards decreased from the baseline value by 35% (8.3 events/1000 ward admissions to 5.4 events/1000 ward admissions) in the 12 month period after AEGIS implementation. Unplanned ICU admissions decreased by an average of 17.5% (31.5 to 26 unplanned ICU admissions/1000 ward admissions) after AEGIS implementation. Finally, the average ward mortality rate fell slightly, from 38/1000 admissions to 36/1000 admissions, translating into 2-4 lives saved per month after AEGIS implementation.

No changes in the rate of CCRT activation or time to CCRT activation were observed. However, the charge nurses felt that the early warning system facilitated their communication with bedside and CCRT nurses as well as with attending physicians.



Conclusion:

A “track and trigger” early warning system that uses the electronic medical record to identify vital sign abnormalities and wirelessly alert an accountable frontline nurse leader is feasible and inexpensive to operate in a large community hospital. The observed decrease in code blue events, unplanned ICU admissions, and in- hospital deaths have prompted us to expand the program to include all inpatient wards at William Osler Health System.



References: None
    This eLearning portal is powered by:
    This eLearning portal is powered by MULTIEPORTAL
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.


Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.


Save Settings