Improving the Efficacy of the Emergency Severity Index
Disciplines
Health and Medical Administration
Abstract (300 words maximum)
When you are checked into an emergency room, you are categorized by a widely used triage system known as the emergency severity index (ESI). The index rates the patients’ symptoms on a scale of 1 to 5 with 1 meaning immediate lifesaving care is required and 5 being non urgent minor illnesses. This tool has helped promote clinical urgency by allowing the triage nurses and doctors to know exactly which patient need to be seen first. While there has been great success with the utilization of the ESI as is, the challenges lie when multiple patients fall under the same index number. Currently there is no algorithm that could further break down the whole number index to one that includes decimals places to further discriminate between diagnosis. Using orbit regression models this research hopes to determine whether it is possible to add an additional algorithm to differentiate the severity of patient symptoms that fall within the same general index number. Our long-term goal is to improve overall health and decrease waiting times and uncertainty in the emergency department. The research endeavors to revolutionize emergency care systems, bringing advantages to patients, healthcare practitioners, and society in general.
Academic department under which the project should be listed
SPCEET - Industrial and Systems Engineering
Primary Investigator (PI) Name
Luisa Valentina Nino de Valladares
Improving the Efficacy of the Emergency Severity Index
When you are checked into an emergency room, you are categorized by a widely used triage system known as the emergency severity index (ESI). The index rates the patients’ symptoms on a scale of 1 to 5 with 1 meaning immediate lifesaving care is required and 5 being non urgent minor illnesses. This tool has helped promote clinical urgency by allowing the triage nurses and doctors to know exactly which patient need to be seen first. While there has been great success with the utilization of the ESI as is, the challenges lie when multiple patients fall under the same index number. Currently there is no algorithm that could further break down the whole number index to one that includes decimals places to further discriminate between diagnosis. Using orbit regression models this research hopes to determine whether it is possible to add an additional algorithm to differentiate the severity of patient symptoms that fall within the same general index number. Our long-term goal is to improve overall health and decrease waiting times and uncertainty in the emergency department. The research endeavors to revolutionize emergency care systems, bringing advantages to patients, healthcare practitioners, and society in general.