Authors (including presenting author) :
Tsang WF(1), Reed FD(2)
Affiliation :
(1) Intensive Care Unit, Queen Elizabeth Hospital, (2) Department of Medicine, Queen Elizabeth Hospital
Keyword 1: :
ventilator management
Keyword 2: :
information-based electronic platform
Keyword 3: :
electronic platform
Introduction :
At present, the application of evidence-based practice among healthcare systems with information technology in Hong Kong is increasing. However, there is a significant knowledge gap in clinical areas to support frontline physicians and nurses in handling different situations, especially for critically ill patients with mechanical ventilation, such as ventilator troubleshooting and alarm setup. Frontline staff may not be competent enough to handle ventilator troubleshooting due to thin healthcare resources, varying levels of training and experience. This knowledge gap can lead to serious complications such as delays in patient care, increased risks of complications, and, finally, poor patient outcomes. To address this issue, the introduction of information technology to assist frontline nursing staff in tackling these problems systematically was essential.
Objectives :
The study primarily aimed to develop and test an information-based electronic platform for ventilator troubleshooting to enhance nurses’ competencies in general wards of Hong Kong public hospitals. Primary objectives Assess nurses’ satisfaction with the electronic platform for handling troubleshooting of invasive mechanical ventilation using the System Usability Scale. Evaluate nurses’ comfort level and confidence in independently troubleshooting invasive mechanical ventilation after introduction of the platform. Specific objectives Enhance nurses’ knowledge and skills in ventilator troubleshooting, alarm setting, and adherence to hospital ventilator protocols. Increase comfort in managing life-threatening ventilator complications via clear flowcharts and safe practice environment. Promote ongoing education, knowledge exchange, and evidence-based practice through accessible digital resources.
Methodology :
A cross-sectional study design was used in this study. This pilot study was conducted on nurses working in Queen Elizabeth Hospital in HKSAR, China, in 2025. To perform the study, a ventilator training platform was first developed. The contents and functional requirements of the electronic platform included mechanical ventilation guidelines, ventilator parameters and settings, ventilator modes, ventilator alarms and handling of patient-ventilator dyssynchrony. The study was conducted with a population of 40 registered nurses in general wards, all of whom consented to participate. Questionnaires were gathered prior to the implementation of the electronic platform. A face-to-face training session was conducted for the recruited nurses on the proper utilization of the electronic platform. Questionnaires were gathered subsequent to the implementation of the electronic platform.
Result & Outcome :
The goal of this study is: 1. The satisfaction level of nurses towards the electronic platform for handling troubleshooting of invasive mechanical ventilation increase, and 2. The comfort level of nurses for handling troubleshooting in invasive mechanical ventilation after the introduction of the electronic platform improve The study found moderate usability of the ventilator electronic platform but very high satisfaction and large gains in nurses’ confidence and comfort with ventilator troubleshooting. Key quantitative results Mean System Usability Scale score was 63.5 (grade D overall), with 88.5% agreeing with positive items; 92.5% wanted to use the system more often, but 57.5% felt there was a steep learning curve. Overall satisfaction with the system was 97.5%; about 92% rated the training items as agree/strongly agree. Confidence and competency outcomes Proportion reporting high confidence across 13 ventilator competencies rose from 13.65% pre-training to 52.88% post-training; 90% improved their total confidence score. Total confidence score increased from 23.53 to 32.55 (max 39), with all 13 items improving significantly (p ≤ 0.01), especially alarm troubleshooting and ventilator setting adjustments.
