LONG DISTANCE TRIAGE: ASSESSING PATIENT RISK FOR INTER-HOSPITAL TRANSFER PATIENTS

Purpose:       Inter-hospital transfer (IHT) patients tend to be among the highest acuity patients in the University of Maryland Medical Center internal medicine department.   While there are decision support tools to assist assigning the correct triage level to emergency department patients in person, there are no tools for conducting over-the-phone triage prior to patient transfer.

Method: In this paper, we develop a method for assessing the risk of mortality or the requirement an ICU bed for incoming IHT patients, using both logistic regression and decision trees.  Using blood pressure, hemoglobin count, white blood cell count, and pulse data from 1158 IHT patients at UMMC, we develop a tool, called HALT (Hypertension, Anemia, Leukocytosis, Tachycardia), that classifies patients as low- or high-risk.

Result: Eight percent of the 1158 patients either required an ICU bed within 48 hours of transfer or died.  HALT achieves 41% sensitivity (percent of positive cases correctly identified) and 88% specificity (percent of negative cases correctly identified) using only binary cutoffs for each variable.  When the model is extended to use logistic regression instead of simply comparing each variable to a cutoff value, the model’s sensitivity increases to 52%, while specificity remains at 88%.  A combination method that reports a patient as high-risk if either HALT or logistic regression classifies the patient as high-risk achieves 59% sensitivity and 85% specificity.  When patient age is added into the logistic regression model, sensitivity increases to 54% while specificity remains at 88%.

Conclusion:  Our combination tool helps clinicians assess the risk of incoming IHT patients and provides help in deciding where to place incoming patients.