TY - JOUR
T1 - Physiological and descriptive variables as predictors for the Emergency Severity Index
AU - Claudio, David
AU - Ricondo, Luciano
AU - Freivalds, Andris
AU - Okudan Kremer, Gül E.
N1 - Funding Information:
This research was funded in part by the National Action Council for Minorities in Engineering (NACME), the Sloan Foundation, the NASA Space Grant Fellowship, the Pennsylvania Assistive Technology Commercialization Initiative (PATCI), the NSF Graduate Fellowship Research Program, and the Hal and Inge Marcus Center for Service Enterprise Engineering.
PY - 2012/4
Y1 - 2012/4
N2 - Many hospital emergency departments (EDs) in the United States have implemented the use of the five-level Emergency Severity Index (ESI) as their clinical decision support method to enhance clinical decision making in the triage process. The ESI designates the most acutely ill patients as level 1 or 2 and those who do not meet these criteria are assigned to levels 3-5 based on estimated resource utilization. Although the number of resources is the primary decision rule to determine levels 3-5, physiological and descriptive variables can also be used to predict the ESI level. This study uses several physiological and descriptive variables as predictors to determine the ESI value. The physiological variables include heart rate, blood pressure, temperature, respiration rate, and oxygen level, whereas the descriptive variables include age, gender, pain level, and patient complaint. An ordered probit model was developed for ESI prediction. In addition, a linear regression model was also developed to demonstrate the necessity of having a decision making tool that allows for non-integer values. The results of this research can be used to enhance the precision of the ESI and the nurse's ability to prioritize treatment based on triage acuity. The decision making tool can also be used to stratify patients who are classified in the same priority group and may eliminate the necessity of grouping patients into different categories.
AB - Many hospital emergency departments (EDs) in the United States have implemented the use of the five-level Emergency Severity Index (ESI) as their clinical decision support method to enhance clinical decision making in the triage process. The ESI designates the most acutely ill patients as level 1 or 2 and those who do not meet these criteria are assigned to levels 3-5 based on estimated resource utilization. Although the number of resources is the primary decision rule to determine levels 3-5, physiological and descriptive variables can also be used to predict the ESI level. This study uses several physiological and descriptive variables as predictors to determine the ESI value. The physiological variables include heart rate, blood pressure, temperature, respiration rate, and oxygen level, whereas the descriptive variables include age, gender, pain level, and patient complaint. An ordered probit model was developed for ESI prediction. In addition, a linear regression model was also developed to demonstrate the necessity of having a decision making tool that allows for non-integer values. The results of this research can be used to enhance the precision of the ESI and the nurse's ability to prioritize treatment based on triage acuity. The decision making tool can also be used to stratify patients who are classified in the same priority group and may eliminate the necessity of grouping patients into different categories.
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U2 - 10.1080/19488300.2012.680572
DO - 10.1080/19488300.2012.680572
M3 - Article
AN - SCOPUS:84981229224
SN - 1948-8300
VL - 2
SP - 131
EP - 141
JO - IIE Transactions on Healthcare Systems Engineering
JF - IIE Transactions on Healthcare Systems Engineering
IS - 2
ER -