Early Warning Score (EWS) is a tool designed to help clinicians efficiently identify and track patients who have or develop acute illness, and make timely clinical responses. The calculation and charting of EWSs at Tuen Mun Hospital (TMH) is a manual process at present. The purpose of this study is to automate the EWS calculation and charting process using an electronic EWS (e-EWS) system. However, while the e-EWS system could potentially reduce ward staff's workload and improve patient safety, its effectiveness can only be realized through good human factors (HF) design that matches users' expectations, requirements and work practices. Therefore, our aim is to carry out HF methods in order to inform design of the e-EWS system before its implementation in a selected surgical ward in the hospital. After its implementation, we will also conduct evaluation of the e-EWS system to assess its effectiveness with respect to clinical outcomes.
Early Warning Score (EWS), also known as "track-and-trigger" system, is a tool designed to help clinicians efficiently identify and track patients who have or develop acute illness, and make timely clinical responses. An EWS is calculated based on values from a number of physiological parameters (e.g. respiration rate, oxygen saturation, systolic blood pressure, pulse rate, level of consciousness, and temperature) to obtain an aggregated score, which indicates a patient's health status. It is mostly used by ward nurses to monitor their patients; when a patient's EWS has exceeded a set threshold, the nurse should attend to the patient more closely and consider for intervention e.g. call a doctor. The philosophy of EWS is that it improves patient safety by enabling ward staff to detect patients' deterioration early so that timely intervention can be administered within the "golden period for treatment". In the U.K., an EWS system called the National Early Warning Score (NEWS) was first released in 2012 (NEWS, 2012). The NEWS was the first system to standardize the calculation and charting of acute-illness severity and has been widely adopted across the National Health Service (NHS). Recently, in December 2017, NEWS2 has been released and it is an updated version of the NEWS (NEWS2, 2017). NEWS2 can be readily computerized and has already been integrated with some NHS hospitals' electronic health record systems. The Updated Report of a Working Party of NEWS2 states that "There are potential advantages of automated calculation of the NEW score and automated alert systems." (p. 6). Therefore, the objective of this proposed study is to implement and evaluate an electronic EWS (e-EWS) system in a selected surgical ward at Tune Mun Hospital (TMH). At TMH, the current practice of obtaining ward patients' EWS is a manual process: firstly, a care giver measures a patient's specified physiological parameters by using the appropriate monitoring instruments; secondly, the care giver writes down the values of the parameters on a paper chart; thirdly, the paper chart is handed over to a nurse; and finally, the nurse calculates the EWS. The process is performed on a designated regular time interval. There are two main drawbacks of the manual EWS process: firstly, it is inefficient because there are usually multiple patients in a ward and some physiological variables take time to measure. Therefore, in a busy ward, missed physiological measures and irregular measurement-taking intervals are often reported. These problems lead to staff ignoring EWS calculations. Secondly, when EWS are calculated, nurses often do so for patients who already show deteriorating conditions. This counters the original intent of EWS, which is to help identify patients with early signs of deterioration. These drawbacks compromise clinicians' ability to detect patient deterioration early, which could potentially compromise patient safety. A potential solution is to automate the manual EWS process. Some hospitals in Hong Kong, for example, Tseung Kwan O Hospital has already adopted an e-EWS system in some of its wards. The e-EWS system is connected to a physiological monitor, which takes various physiological measurements. The system has an auto-charting module, which automatically captures patients' physiological measurements in an electronic chart (e-chart) and calculates their EWS. All the information in the auto-charting module is then wirelessly transferred to a central display in the ward's nurses station. The central display shows patients' status in terms of EWS and issues alerts when any EWS has exceeded a set threshold. The e-EWS system is not only capable of auto-charting but also provides an alert mechanism to help nurses detect early deterioration. However, while the e-EWS system could potentially reduce ward staff's workload and improve patient safety, its effectiveness can only be realized through good HF design that matches users' expectations, requirements and work practices.
Study Type
OBSERVATIONAL
Enrollment
20
The e-EWS system is connected to a physiological monitor, which takes various physiological measurements. The system has an auto-charting module, which automatically captures patients' physiological measurements in an electronic chart (e-chart) and calculates their EWS. All the information in the auto-charting module is then wirelessly transferred to a central display in the ward's nurses station. The central display shows patients' status in terms of EWS and issues alerts when any EWS has exceeded a set threshold. The e-EWS system is not only capable of auto-charting but also provides an alert mechanism to help nurses detect early deterioration.
Tuen Mun Hospital
Hong Kong, Hong Kong
Changes in the number of successful detection of deteriorating cases
Time frame: from the beginning of the study to the 6th month later
Changes in the number of cardiopulmonary resuscitations (CPRs)
Time frame: from the beginning of the study to the 6th month later
Changes in the number of ICU or high dependency unit transfer
Time frame: from the beginning of the study to the 6th month later
Changes in the number of assistance calls to doctors
Time frame: from the beginning of the study to the 6th month later
Changes in application-specific self-efficacy
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived usefulness of the e-EWS system
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived ease of use of the e-EWS system
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived behavioral control
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's attitude towards the e-EWS system
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's intention to use
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived work performance
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived workload
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in the quality of patient care
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in the workflow
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in the accuracy of the technology
This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's personal experiences in current clinical unit
This outcome will be measured by the 4-point Likert scale, with scores ranging from 1 (strongly disagree) to 4 (strongly agree).
Time frame: from the beginning of the study to the 6th month later
Comments and suggestions of the system
Ward staff's opinions will be collected by a semi-structured interview.
Time frame: from the beginning of the study to the 6th month later
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