Application of a statistics-based expert system to provide automated second opinions prior to elective surgery

Second opinions now are mandated by many funding agencies prior to elective surgery to provide an opportunity to evaluate the indications for surgery and allow an evaluation of the risks of surgery. One difficulty with second opinion programs is the observation that 80% to 95% of all surgical recommendations confirm the need for surgery and the number of patients requiring second opinions represents a significant health care cost. The interactive statistical package (ISP) is a statistics-based decision support system currently used to teach residents medical decision processes. Training an ISP is relatively simple because experts need only define the patient parameters and the allowed values of each parameter. Each expert then sets up a decision tree to represent his or her decision process. The system will then present a series of randomly generated cases to the expert and observe the expert's action. Through Bayesian statistics the system identifies the parameter values that significantly affect the expert's judgment and the direction that they exert on the decision. A decision support system used to evaluate patients with prostate cancer reproduces an expert's treatment selection with an accuracy of 94% on a log rank test when selecting among six treatment options including three different surgical procedures, two different radiation regimens, and palliative therapy. The entire system was trained and validated over 3 weeks, and the accuracy of the system is comparable to the consistency of the expert (90%). Because several experts can review the same patient material and make decisions independently, multiple systems can be created and validated, and operated in parallel.(ABSTRACT TRUNCATED AT 250 WORDS)