Prehospital provider prediction of emergency department disposition: implications for selective diversion.
OBJECTIVE
To determine whether emergency medical services (EMS) personnel can use selective diversion and accurately predict those patients being transported who are unlikely to need a critical care bed and those patients unlikely to require admission to the hospital.


METHODS
This was a prospective study of patients being transported by the local EMS service. The EMS providers were asked to predict disposition of the patient. Emergency department (ED) personnel were asked to indicate on the study sheet the actual disposition of the patient.


RESULTS
A total of 411 patient transports were entered into the study. The EMS providers predicted that 246 (59.9%) would be discharged to home, 96 (23.3%) would be admitted to a floor bed, and 69 (16.8%) would be admitted to a critical care bed (CCB). The actual dispositions of the patients were: 253 (61.6%) discharged to home, 99 (24.1%) admitted to a floor bed, and 59 (9.9%) admitted to a CCB. The EMS providers performed well at predicting those patients who would not need a CCB: negative predictive value 96.2% (95% confidence interval [CI]) (93.4-97.9). They also correctly identified most patients who were discharged to home: 209 of 253, 85% (95% CI is equal to 79.7-89.1%).


CONCLUSIONS
EMS providers appear to be capable of using selective diversion categories. EMS providers correctly identified most patients who will not require a critical care bed. The EMS providers also correctly identified most patients who will be discharged from the ED after treatment.
Price
Timothy G
TG
Department of Emergency Medicine, School of Medicine, University of Louisville, Kentucky 40202, USA. drtprice@bellsouth.net
Hooker
Edmond A
EA
Neubauer
Joshua
J
D018672:Community Health Planning; D016638:Critical Illness / Q000145:classification; D003441:Crowding; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D007629:Kentucky; D009111:Local Government; D010343:Patient Admission / Q000706:statistics & numerical data*; D010360:Patient Transfer / Q000706:statistics & numerical data*; D011379:Prognosis; D011446:Prospective Studies; D011635:Public Health Administration; D014218:Triage / Q000379:methods*