Health Knowledge, Attitudes, PracticeNCT04425694
Implementation and Evaluation of an Electronic Early Warning Score (e-EWS) System
An observational study of Health Knowledge, Attitudes, Practice, run by The University of Hong Kong at 1 site in Hong Kong.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Health Knowledge, Attitudes, Practice
- What is being tested?
- e-EWS system
- Type of treatment
- Medical device
- How many people
- 20 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Nov 2019to 31 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Participants will be selected from the runner nurses and the ward manager in a selected surgical ward (F3B ward) at Tuen Mun Hospital (TMH).
Who may be able to join
- All F3B ward nursing staff
Who cannot join
- Staff not in F3B ward
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed description
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.
- Conditions
- Health Knowledge, Attitudes, Practice
- Being tested
- e-EWS system
- Keywords
- patient safety, electronic Early Warning Score system, human factors
- Medical subject headings
- Behavior
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
e-EWS systemDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| F3B ward staff | The e-EWS system will be implemented in a selected surgical ward (F3B ward) in Tuen Mun Hospital. All F3B ward staff will use the system and evaluate its effectiveness. Receives: Device: e-EWS system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Changes in the number of successful detection of deteriorating cases
Measured: from the beginning of the study to the 6th month later
Changes in the number of cardiopulmonary resuscitations (CPRs)
Measured: from the beginning of the study to the 6th month later
Changes in the number of ICU or high dependency unit transfer
Measured: from the beginning of the study to the 6th month later
Changes in the number of assistance calls to doctors
Measured: from the beginning of the study to the 6th month later
Secondary outcomes (13)
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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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.
Measured: from the beginning of the study to the 6th month later
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2020
- Study start1 Nov 2019
- Primary completion31 Dec 2020 (estimated)
- Study completion31 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Health Knowledge, Attitudes, PracticeStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 20 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Lingnan University, Tuen Mun Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04425694
- Sponsor's protocol ID
- e-EWS 20200526
- Status last verified
- Jun 2020
- First submitted
- 8 Jun 2020
- Last updated
- 11 Jun 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Royal College of Physicians. National Early Warning Score (NEWS): Standardising the assessment of acute illness severity in the NHS. Report of a working party. London: RCP, 2012.
Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. London: RCP, 2017.
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Implementation and Evaluation of an Electronic Early Warning Score (e-EWS) System
- ClinicalTrials.gov ID
- NCT04425694
- Acronym / sponsor ID
- e-EWS 20200526
- Conditions
- Health Knowledge, Attitudes, Practice
- Keywords
- patient safety, electronic Early Warning Score system, human factors
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 20 participants (estimated)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Collaborators
- Lingnan University, Tuen Mun Hospital
- Study start
- 1 Nov 2019 (actual)
- Primary completion
- 31 Dec 2020 (estimated)
- Study completion
- 31 Dec 2020 (estimated)
- First posted
- 11 Jun 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| F3B ward staff | – | Device: e-EWS systemThe e-EWS system will be implemented in a selected surgical ward (F3B ward) in Tuen Mun Hospital. All F3B ward staff will use the system and evaluate its effectiveness. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be selected from the runner nurses and the ward manager in a selected surgical ward (F3B ward) at Tuen Mun Hospital (TMH).
Key inclusion criteria (1 listed)
- All F3B ward nursing staff
Key exclusion criteria (1 listed)
- Staff not in F3B ward
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| e-EWS systemThe 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. | Device | F3B ward staff |
Primary outcome
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
13 secondary outcomes
Changes in application-specific self-efficacy
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
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
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived behavioral control
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's attitude towards the e-EWS system
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's intention to use
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived work performance
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's perceived workload
Time frame: from the beginning of the study to the 6th month later
Changes in the quality of patient care
Time frame: from the beginning of the study to the 6th month later
Changes in the workflow
Time frame: from the beginning of the study to the 6th month later
Changes in the accuracy of the technology
Time frame: from the beginning of the study to the 6th month later
Changes in ward staff's personal experiences in current clinical unit
Time frame: from the beginning of the study to the 6th month later
Comments and suggestions of the system
Time frame: from the beginning of the study to the 6th month later
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jun 2020.
- BackgroundRoyal College of Physicians. National Early Warning Score (NEWS): Standardising the assessment of acute illness severity in the NHS. Report of a working party. London: RCP, 2012.
- BackgroundRoyal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. London: RCP, 2017.
- Nielsen, J. (2012, June 4). How Many Test Users in a Usability Study?Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Health Knowledge, Attitudes, Practice, run by The University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 11 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in the number of successful detection of deteriorating cases (time frame: from the beginning of the study to the 6th month later). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04425694, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Jun 2020.