No abstract

The goal of this project was to develop a Pediatric Decision Support system (PDS) that allows a resident physician to define a patient case based on symptoms (diagnostic signs and test results) and generates a list of possible diagnoses based on the World Health Organization's International Classification of Diseases (ICD10). The intent is to improve the diagnostic approach taken by resident physicians and eventually become a training tool in medical education programs.

This paper describes how psychological research can contribute to the requirements engineering, the design and usefulness of a Diagnostic Decision Support System (DDSS) intended to support pediatric residents' diagnostic decisions. Research on cognitive biases in Bayesian decision tasks is discussed. The design of the DDSS is briefly outlined, and a formative usefulness test is reported. Under the assumption that a particular cognitive bias could be overcome by showing it to participants, pediatric residents were given a set of Bayesian decision tasks. One half was given an opportunity to interact with NeoPeDDS and the other half was not. Results showed that NeoPedDDS usage improved the accuracy of the diagnostic decisions, but that formal training in Bayesian statistics appears to be necessary for residents to evaluate ambiguous information in a normatively correct manner.

No abstract