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Eur J Emerg Med ; 19(5): 292-6, 2012 Oct.
Article in English | MEDLINE | ID: mdl-21971293

ABSTRACT

OBJECTIVE: A correct prehospital diagnosis of emergency patients is crucial as it determines initial treatment, admitting specialty, and subsequent treatment. We evaluated the diagnostic accuracy of emergency physicians. METHODS: All patients seen by six emergency physicians staffing the local emergency ambulance and rescue helicopter services during an 8-month period were studied. The ambulance and helicopter physicians had 3 and 4 years, respectively, training in anesthesia and intensive care medicine. The admission diagnoses were compared with the discharge diagnoses for agreement. Time of day of the emergency call, patients' age, and sex, living conditions, and presenting symptoms were evaluated as contributing factors. RESULTS: Three hundred and fifty-five ambulance and 241 helicopter deployment protocols were analyzed. The overall degree of agreement between initial and discharge diagnoses was 90.1% with no difference attributable to years of experience. The lowest agreement rate was seen in neurological disorders (81.5%), with a postictal state after an unobserved seizure often being diagnosed as a cerebrovascular accident. Inability to obtain a complete medical history (e.g. elderly patients, patients in nursing homes, neurological impairment) was associated with a lower agreement rate between initial and discharge diagnoses (P<0.05). CONCLUSION: Medical history, physical examination, ECG, and blood glucose enabled a correct diagnosis in most cases, but some were impossible to resolve without further technical and laboratory investigations. Only a few were definitively incorrect. A detailed medical history is essential. Neurological disorders can present with misleading symptoms and when the diagnosis is not clear it is better to assume the worst case.


Subject(s)
Clinical Competence , Diagnostic Errors , Emergency Medical Services/methods , Patient Discharge , Practice Patterns, Physicians' , Adolescent , Adult , Aged , Aged, 80 and over , Chi-Square Distribution , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Young Adult
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