Evaluation of a decision support system to predict preoperative investigations

Background. We have developed the 'Optimising Surgical Care and Assessment Record' (OSCAR), a clinical decision support system, to help nurses in predicting necessary preoperative investigations before surgery. OSCAR applies the hospital's protocols, which are based on the National Institute for Health and Clinical Excellence guidelines, to the patient's medical history and surgical details before recommending required investigations. Methods. We selected case notes of 50 patients randomly from the OSCAR system that were recorded between October 2006 and January 2007. To form a reference standard, these case histories were anonymized and then sent to 10 consultant anaesthetists across the country. They were asked to study the case history and choose which tests they would carry out and which they would not. Then we have evaluated OSCAR's ability to predict the necessary investigations and the nurses' judgement, in comparison with the reference standard. Results. OSCAR's ability to identify which investigations should be carried out, that is, its sensitivity, was 91.5% and its ability to identify which investigations not to carry out, that is, its specificity, was 82.7%. OSCAR was consistent in predicting investigations for differing severities of surgery, for ASA grade and gender. We were unable to demonstrate any overall difference between OSCAR and the nurse's ability to predict preoperative investigations. When combining the nurse's predictions with OSCAR's recommendations, an even greater sensitivity of 98.2% could be achieved. Conclusions. OSCAR's prediction algorithm cannot replace the nurse's judgement, but it can be used as a supplementary decision aid to promote consistency and improve accuracy.