Date Published: May 8, 2019
Publisher: Public Library of Science
Author(s): Eveline Mestrom, Ashley De Bie, Melissa van de Steeg, Merel Driessen, Louis Atallah, Rick Bezemer, R. Arthur Bouwman, Erik Korsten, Raffaele Serra.
Early warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients.
This study conducted retrospective before-and-after comparative analysis of non-automated and automated MEWS for patients admitted to the surgical high-dependency unit in a tertiary hospital. Operational outcomes included number of recorded assessments of the individual MEWS elements, number of complete MEWS assessments, as well as adherence rate to related protocols. Clinical outcomes included hospital length of stay, in-hospital and 28-day mortality, and ICU readmission rate.
Recordings in the electronic medical record from the control period contained 7929 assessments of MEWS elements and were performed in 320 patients. Recordings from the intervention period contained 8781 assessments of MEWS elements in 273 patients, of which 3418 were performed with the automated EWS system. During the control period, 199 (2.5%) complete MEWS were recorded versus 3991 (45.5%) during intervention period. With the automated MEWS systems, the percentage of missing assessments and the time until the next assessment for patients with a MEWS of ≥2 decreased significantly. The protocol adherence improved from 1.1% during the control period to 25.4% when the automated MEWS system was involved. There were no significant differences in clinical outcomes.
Implementation of an automated EWS system on a surgical high dependency unit improves the number of complete MEWS assessments, registered vital signs, and adherence to the EWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability.
Automated early warning score (EWS) systems are increasingly embedded in clinical practice to improve registration and awareness of vital signs and enhance rapid response teams (RRT) notifications. The impact on clinical outcomes of these systems remains uncertain in various populations, for example in the high-risk surgical population, which is the focus of this study.
A total of 594 patients were included for analysis, 320 patients for the control group and 274 patients for the intervention group. Both groups were comparable in terms of age, gender, and APACHE II and -IV scores at the time of first ICU admittance postoperatively (Table 3). The control group consisted of significantly less patients undergoing oncologic abdominal surgery (69.7 versus 80.3%; p = 0.01) and more patients undergoing aortic surgery (20.0 versus 12.4%; p 0.01).
The results of this retrospective study in a surgical high-dependency unit show that the use of an automated MEWS system improves the recording separate MEWS elements and complete MEWS assessments, as well as the resulting adherence to the MEWS hospital protocol. The use of this system improved the MEWS hospital protocol adherence for MEWS assessments using conventional methods during the intervention period. After implementing the automated MEWS system, 39% of the assessments were performed during the intervention period. Although there was a trend towards improved clinical outcomes in this period, this study did not show significant differences in mortality, length of stay, ICU readmission rate, or severity of illness at ICU readmission.
Implementation of an automated MEWS on a surgical high dependency unit improves the number of complete MEWS, registered vital signs, and adherence to the local MEWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability to provide actionable insights to the right person at the right time.