<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04425694</url>
  </required_header>
  <id_info>
    <org_study_id>e-EWS 20200526</org_study_id>
    <nct_id>NCT04425694</nct_id>
  </id_info>
  <brief_title>Implementation and Evaluation of an Electronic Early Warning Score (e-EWS) System</brief_title>
  <official_title>Implementation and Evaluation of an Electronic Early Warning Score (e-EWS) System</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>The University of Hong Kong</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Lingnan University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Tuen Mun Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>The University of Hong Kong</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Early Warning Score (EWS) is a tool designed to help clinicians efficiently identify and&#xD;
      track patients who have or develop acute illness, and make timely clinical responses. The&#xD;
      calculation and charting of EWSs at Tuen Mun Hospital (TMH) is a manual process at present.&#xD;
      The purpose of this study is to automate the EWS calculation and charting process using an&#xD;
      electronic EWS (e-EWS) system. However, while the e-EWS system could potentially reduce ward&#xD;
      staff's workload and improve patient safety, its effectiveness can only be realized through&#xD;
      good human factors (HF) design that matches users' expectations, requirements and work&#xD;
      practices. Therefore, our aim is to carry out HF methods in order to inform design of the&#xD;
      e-EWS system before its implementation in a selected surgical ward in the hospital. After its&#xD;
      implementation, we will also conduct evaluation of the e-EWS system to assess its&#xD;
      effectiveness with respect to clinical outcomes.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Early Warning Score (EWS), also known as &quot;track-and-trigger&quot; system, is a tool designed to&#xD;
      help clinicians efficiently identify and track patients who have or develop acute illness,&#xD;
      and make timely clinical responses. An EWS is calculated based on values from a number of&#xD;
      physiological parameters (e.g. respiration rate, oxygen saturation, systolic blood pressure,&#xD;
      pulse rate, level of consciousness, and temperature) to obtain an aggregated score, which&#xD;
      indicates a patient's health status. It is mostly used by ward nurses to monitor their&#xD;
      patients; when a patient's EWS has exceeded a set threshold, the nurse should attend to the&#xD;
      patient more closely and consider for intervention e.g. call a doctor. The philosophy of EWS&#xD;
      is that it improves patient safety by enabling ward staff to detect patients' deterioration&#xD;
      early so that timely intervention can be administered within the &quot;golden period for&#xD;
      treatment&quot;.&#xD;
&#xD;
      In the U.K., an EWS system called the National Early Warning Score (NEWS) was first released&#xD;
      in 2012 (NEWS, 2012). The NEWS was the first system to standardize the calculation and&#xD;
      charting of acute-illness severity and has been widely adopted across the National Health&#xD;
      Service (NHS). Recently, in December 2017, NEWS2 has been released and it is an updated&#xD;
      version of the NEWS (NEWS2, 2017). NEWS2 can be readily computerized and has already been&#xD;
      integrated with some NHS hospitals' electronic health record systems. The Updated Report of a&#xD;
      Working Party of NEWS2 states that &quot;There are potential advantages of automated calculation&#xD;
      of the NEW score and automated alert systems.&quot; (p. 6). Therefore, the objective of this&#xD;
      proposed study is to implement and evaluate an electronic EWS (e-EWS) system in a selected&#xD;
      surgical ward at Tune Mun Hospital (TMH).&#xD;
&#xD;
      At TMH, the current practice of obtaining ward patients' EWS is a manual process: firstly, a&#xD;
      care giver measures a patient's specified physiological parameters by using the appropriate&#xD;
      monitoring instruments; secondly, the care giver writes down the values of the parameters on&#xD;
      a paper chart; thirdly, the paper chart is handed over to a nurse; and finally, the nurse&#xD;
      calculates the EWS. The process is performed on a designated regular time interval.&#xD;
&#xD;
      There are two main drawbacks of the manual EWS process: firstly, it is inefficient because&#xD;
      there are usually multiple patients in a ward and some physiological variables take time to&#xD;
      measure. Therefore, in a busy ward, missed physiological measures and irregular&#xD;
      measurement-taking intervals are often reported. These problems lead to staff ignoring EWS&#xD;
      calculations. Secondly, when EWS are calculated, nurses often do so for patients who already&#xD;
      show deteriorating conditions. This counters the original intent of EWS, which is to help&#xD;
      identify patients with early signs of deterioration. These drawbacks compromise clinicians'&#xD;
      ability to detect patient deterioration early, which could potentially compromise patient&#xD;
      safety.&#xD;
&#xD;
      A potential solution is to automate the manual EWS process. Some hospitals in Hong Kong, for&#xD;
      example, Tseung Kwan O Hospital has already adopted an e-EWS system in some of its wards. The&#xD;
      e-EWS system is connected to a physiological monitor, which takes various physiological&#xD;
      measurements. The system has an auto-charting module, which automatically captures patients'&#xD;
      physiological measurements in an electronic chart (e-chart) and calculates their EWS. All the&#xD;
      information in the auto-charting module is then wirelessly transferred to a central display&#xD;
      in the ward's nurses station. The central display shows patients' status in terms of EWS and&#xD;
      issues alerts when any EWS has exceeded a set threshold. The e-EWS system is not only capable&#xD;
      of auto-charting but also provides an alert mechanism to help nurses detect early&#xD;
      deterioration.&#xD;
&#xD;
      However, while the e-EWS system could potentially reduce ward staff's workload and improve&#xD;
      patient safety, its effectiveness can only be realized through good HF design that matches&#xD;
      users' expectations, requirements and work practices.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Active, not recruiting</last_known_status>
  <start_date type="Actual">November 1, 2019</start_date>
  <completion_date type="Anticipated">December 31, 2020</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Changes in the number of successful detection of deteriorating cases</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
  </primary_outcome>
  <primary_outcome>
    <measure>Changes in the number of cardiopulmonary resuscitations (CPRs)</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
  </primary_outcome>
  <primary_outcome>
    <measure>Changes in the number of ICU or high dependency unit transfer</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
  </primary_outcome>
  <primary_outcome>
    <measure>Changes in the number of assistance calls to doctors</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Changes in application-specific self-efficacy</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's perceived usefulness of the e-EWS system</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's perceived ease of use of the e-EWS system</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's perceived behavioral control</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's attitude towards the e-EWS system</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's intention to use</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's perceived work performance</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's perceived workload</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in the quality of patient care</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in the workflow</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in the accuracy of the technology</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Changes in ward staff's personal experiences in current clinical unit</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>This outcome will be measured by the 4-point Likert scale, with scores ranging from 1 (strongly disagree) to 4 (strongly agree).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Comments and suggestions of the system</measure>
    <time_frame>from the beginning of the study to the 6th month later</time_frame>
    <description>Ward staff's opinions will be collected by a semi-structured interview.</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">20</enrollment>
  <condition>Health Knowledge, Attitudes, Practice</condition>
  <arm_group>
    <arm_group_label>F3B ward staff</arm_group_label>
    <description>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.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>e-EWS system</intervention_name>
    <description>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.</description>
    <arm_group_label>F3B ward staff</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Participants will be selected from the runner nurses and the ward manager in a selected&#xD;
        surgical ward (F3B ward) at Tuen Mun Hospital (TMH).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All F3B ward nursing staff&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Staff not in F3B ward&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Tuen Mun Hospital</name>
      <address>
        <city>Hong Kong</city>
        <country>Hong Kong</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Hong Kong</country>
  </location_countries>
  <link>
    <url>http://www.nngroup.com/articles/how-many-test-users/</url>
    <description>Nielsen, J. (2012, June 4). How Many Test Users in a Usability Study?</description>
  </link>
  <reference>
    <citation>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.</citation>
  </reference>
  <reference>
    <citation>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.</citation>
  </reference>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 8, 2020</study_first_submitted>
  <study_first_submitted_qc>June 8, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 11, 2020</study_first_posted>
  <last_update_submitted>June 8, 2020</last_update_submitted>
  <last_update_submitted_qc>June 8, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 11, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>patient safety</keyword>
  <keyword>electronic Early Warning Score system</keyword>
  <keyword>human factors</keyword>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04425694
org study id: e-EWS 20200526
nct id: NCT04425694
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
observational model: Cohort
time perspective: Prospective
measure: Changes in the number of successful detection of deteriorating cases
time frame: from the beginning of the study to the 6th month later
measure: Changes in the number of cardiopulmonary resuscitations (CPRs)
time frame: from the beginning of the study to the 6th month later
measure: Changes in the number of ICU or high dependency unit transfer
time frame: from the beginning of the study to the 6th month later
measure: Changes in the number of assistance calls to doctors
time frame: from the beginning of the study to the 6th month later
measure: Changes in application-specific self-efficacy
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: 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
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: 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
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's perceived behavioral control
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's attitude towards the e-EWS system
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's intention to use
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's perceived work performance
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's perceived workload
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in the quality of patient care
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in the workflow
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in the accuracy of the technology
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 7-point Likert scale, with scores ranging from 1 (very strongly disagree) to 7 (very strongly agree).
measure: Changes in ward staff's personal experiences in current clinical unit
time frame: from the beginning of the study to the 6th month later
description: This outcome will be measured by the 4-point Likert scale, with scores ranging from 1 (strongly disagree) to 4 (strongly agree).
measure: Comments and suggestions of the system
time frame: from the beginning of the study to the 6th month later
description: Ward staff's opinions will be collected by a semi-structured interview.
arm group label: F3B ward staff
description: 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.
intervention type: Device
intervention name: e-EWS system
description: 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.
arm group label: F3B ward staff
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: Hong Kong
url: http://www.nngroup.com/articles/how-many-test-users/
description: Nielsen, J. (2012, June 4). How Many Test Users in a Usability Study?
citation: 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.
citation: 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.
responsible party type: Sponsor
sharing ipd: No