Validation of a diagnostic reminder system in emergency medicine: a multi-centre study.
BACKGROUND
Diagnostic error is a significant problem in emergency medicine, where initial clinical assessment and decision making is often based on incomplete clinical information. Traditional computerised diagnostic systems have been of limited use in the acute setting, mainly due to the need for lengthy system consultation. We evaluated a novel web-based reminder system, which provides rapid diagnostic advice to users based on free text search terms.


METHODS
Clinical data collected from patients presenting to three emergency departments with acute medical problems were entered into the diagnostic system. The displayed results were assessed against the final discharge diagnoses for patients who were admitted to hospital (diagnostic accuracy) and against a set of "appropriate" diagnoses for each case provided by an expert panel (potential utility).


RESULTS
Data were collected from 594 patients (53.4% of screened attendances). Mean age was 49.4 years (95% CI 47.7 to 51.1) and the majority had significant past illnesses. Most were assessed first by junior doctors (70%) and 266/594 (44.6%) were admitted to hospital. Overall, the diagnostic system displayed the final discharge diagnosis in 95% of inpatients and 90% of "must-not-miss" diagnoses suggested by the expert panel. The discharge diagnosis appeared within the first 10 suggestions in 78% of cases.


CONCLUSIONS
The Isabel diagnostic aid has been shown to be of potential use in reminding junior doctors of key diagnoses in the emergency department. The effects of its widespread use on decision making and diagnostic error can be clarified by evaluating its impact on routine clinical decision making.
Ramnarayan
Padmanabhan
P
Children's Acute Transport Service, London WC1R 4LL, UK. ram@isabelhealthcare.com
Cronje
Natalie
N
Brown
Ruth
R
Negus
Rupert
R
Coode
Bill
B
Moss
Philip
P
Hassan
Taj
T
Hamer
Wayne
W
Britto
Joseph
J
D000704:Analysis of Variance; D016009:Chi-Square Distribution; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D003951:Diagnostic Errors / Q000517:prevention & control*; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D005260:Female; D006801:Humans; D008131:London; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D017010:Reminder Systems; D014584:User-Computer Interface
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