Features of electronic Early Warning systems which impact clinical decision making

Paper-based Modified Early Warning Scorecards (MEWS) have been developed to help nursing staff detect hospital in-patient deterioration at an early stage. MEWS is based on patient vital signs where these values are transformed into a MEWS score. An electronic Modified Early Warning Scorecard (eMEWS) prototype has been designed and developed to fulfill the role of a computerized Clinical Decision Support System (CDSS) and to assist healthcare professionals in their decision making activities. A review of the existing electronic Early Warning Scorecards (eEWS) revealed they lack certain features that assist in capturing a holistic view of the patient health status for example color codes and vital sign trends. The proposed eMEWS prototype employs these features with the aim of assisting healthcare professionals to obtain a clear understanding of the patient status. A survey was conducted to evaluate the impact of paper-based MEWS and eMEWS as part of the decision making process. The advantages and disadvantages of eMEWS over the paper-based MEWS are presented.

[1]  John O'Donoghue,et al.  Modified Early Warning Scorecard: The Role of Data/Information Quality within the Decision Making Process , 2011 .

[2]  U. Kyriacos,et al.  Monitoring vital signs using early warning scoring systems: a review of the literature. , 2011, Journal of nursing management.

[3]  Richard Y. Wang,et al.  Data quality assessment , 2002, CACM.

[4]  I. Mitchell,et al.  A prospective controlled trial of the effect of a multi-faceted intervention on early recognition and intervention in deteriorating hospital patients. , 2010, Resuscitation.

[5]  Gary B. Smith,et al.  Calculating early warning scores--a classroom comparison of pen and paper and hand-held computer methods. , 2006, Resuscitation.

[6]  Gordon A. Harrison,et al.  Signs of critical conditions and emergency responses (SOCCER): a model for predicting adverse events in the inpatient setting. , 2006, Resuscitation.

[7]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[8]  D. Shanks.  IMPLICIT LEARNING AND TACIT KNOWLEDGE - AN ESSAY ON THE COGNITIVE UNCONSCIOUS - REBER,A , 1995 .

[9]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[10]  Michael Buist,et al.  Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. , 2004, Resuscitation.

[11]  D. Prytherch,et al.  Improving accuracy and efficiency of early warning scores in acute care. , 2009, British journal of nursing.