Clinical paperCalculating early warning scores—A classroom comparison of pen and paper and hand-held computer methods☆
Introduction
In recent years, the focus in managing critically ill patients has been on the prompt recognition of clinical deterioration and early treatment outside critical care units.1, 2, 3, 4, 5, 6 To assist in the early detection of critical illness, many hospitals now use a “track and trigger” system that allocates points to routine vital signs measurements on the basis of their derangement from an arbitrarily agreed “normal” range.7, 8, 9, 10 These points are summed to provide an early warning score (EWS). The weighted value of one or more individual vital signs measurements or, more usually, the EWS is often used to suggest an alteration in the frequency of vital signs monitoring to nurses, or to call ward doctors or critical care outreach teams to the patient.
Little is known about the accuracy with which EWS are calculated and charted, although individual physiological variables, such as heart rate, blood pressure and temperature are often measured accurately using regularly calibrated, electronic devices. Over-scoring may lead to the unnecessary calling of medical staff such that the use of an EWS system may fall into disrepute. Underscoring may lead to a delay in the detection of patient deterioration.
The process by which an EWS is obtained requires several complex activities. It involves the accurate collection of raw vital signs data, the correct ascription of a weighted value according to the degree of physiological derangement and the arithmetic addition of weighted values to form an EWS. Each of these stages can introduce error, which may influence the EWS. Errors may also occur in the transcription of raw or derived data on to paper charts.
Our hospital has developed a system for direct input of vital signs data into handheld personal digital assistants (PDA), linked via wi-fi to a central computer. The system is in use in the Medical Assessment Unit of the hospital and is being introduced to other clinical areas. It permits the rapid calculation of an EWS from raw physiological data, without the need for healthcare staff to know or consult EWS weightings. All raw physiological data, weighted values and EWS are stored on the central computer, with the PDA acting as a data input device. An up-to-date vital signs chart for any patient can be viewed on the PDA at any time.
In this classroom study we compared the speed and accuracy of charting the weighted value attributed to each vital sign, and of calculating an EWS, using the traditional pen and paper method with that using the PDA (VitalPAC™). We also assessed nurses’ preference for each system.
Section snippets
Method
Twenty-one nurses working in the Medical Assessment Unit of Queen Alexandra Hospital, Portsmouth agreed to participate in the study. All were familiar with the EWS used in the study, as this has been employed routinely in the hospital for over 3 years. The study involved the entry and charting of five different, fictitious, physiological vital signs datasets, each of which included measurements of heart rate, blood pressure, temperature, respiratory rate, urine output and neurological status.
Results
After removal of the first vital signs dataset for each technique a total of 168 (84 VitalPAC™; 84 pen/paper) dataset entries were available for analysis. As each dataset contained six individual physiological variables, participants were expected to process 504 (84 × 6) values and derive 84 EWS using each method.
Discussion
Government and Royal College sponsored publications 5, 6, 11, 12, 13, 14 in England and Wales strongly advocate the use of an EWS system as an adjunct to identifying and managing acutely ill patients. However, there is no published evidence about the accuracy with which clinical staff calculate and record EWS, or the time taken in so doing. The issue of accuracy is particularly important as EWS systems have been widely adopted with little validation, clinical responses have been linked to
Conflicts of interest
VitalPAC™ is a collaborative development of the Learning Clinic and Portsmouth Hospitals NHS Trust.
Acknowledgements
The authors wish to acknowledge the assistance of the MAU staff at Portsmouth Hospitals Trust for participating in the study and for the Department of Information & Communication Technologies for their support.
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A Spanish translated version of the summary of this article appears as Appendix in the online version at doi:10.1016/j.resuscitation.2005.12.002