Emergency departments (EDs) have been shown to be high risk clinical areas for the occurrence of medical adverse events.1,2
In contrast to the inpatient setting where diagnostic delays and missed diagnoses account for only 10–15% of adverse events, diagnostic errors are more frequent and assume greater significance in primary care, emergency medicine and critical care, resulting in a large number of successful negligence claims.3,4,5,6
Various reasons may account for this difference. In EDs, acute clinical presentations are often characterised by incomplete and poor quality information at initial assessment leading to considerable diagnostic uncertainty. Systematic factors such as frequent shift changes, overwork, time pressure and high patient throughput may further contribute to diagnostic errors.7
Cognitive biases inherent within diagnostic reasoning have also been shown to play a crucial role in perpetuating patient safety breaches during diagnostic assessment.8,9
Despite easier access to health information for the public through NHS Direct and NHS Online, the demand for emergency care is steadily rising,10
and it is possible that greater pressure on emergency staff to cut waiting times and increase efficiency with limited resources will lead to a higher incidence of patient safety incidents during diagnostic assessment.
The use of computerised diagnostic decision support systems (DDSS) has been proposed as a technological solution to minimise diagnostic error.11
A number of DDSS have been developed over the past few decades, but most have not shown promise in EDs, either because they focussed on a narrow clinical problem (eg, chest pain) or because computerised aids intended for general use were used only for consultation in rare and complex diagnostic dilemmas. In order to enter complete clinical data into these systems using system‐specific medical terminology, considerable user motivation and time was required (often 20–40 min of data entry time).12
) is a novel DDSS which was primarily developed for acute paediatrics. Isabel users enter their search terms in natural language free text, and are shown a list of diagnostic suggestions (up to a maximum of 30, displayed on three consecutive pages, 10 diagnoses per page), which are arranged by body system (eg, cardiovascular, gastrointestinal) rather than by clinical probability.13,14
These suggestions are intended only as reminders to prompt clinicians to consider them in their diagnostic investigation, not as “correct” choices or “likely” diagnoses. The system uses statistical natural language processing techniques to search an underlying knowledge base containing textual descriptions of >10
000 diseases. In clinical trials performed in acute paediatrics, mean Isabel usage time was <3 min, and the system reminded junior doctors to consider clinically significant diagnoses in 12.5% of cases.15
The DDSS was extended to cover adult disease conditions in January 2005.16
Although the extended Isabel system was closely modelled on the paediatric version, a large scale validation of the newly developed system was felt necessary to establish its diagnostic accuracy and potential utility for a range of clinical scenarios among adult patients in EDs. This was especially important since acute presentations in adults differ significantly from those in children. Adults may have multiple pre‐morbid conditions that confound their acute illness. Further, the spectrum of diseases encountered is quite distinct, and the relative importance of diagnoses during initial assessment may be different from that in paediatric cases. It was also postulated that the greater amount of clinical detail available on adults presenting to EDs may prolong data entry time. In addition, since the Isabel system relies on extracting key concepts from its knowledge base (textbooks and journal articles), variations in natural language textual patterns in the medical sources used for the adult system may significantly influence its diagnostic suggestions.