ABSTRACT
This study addresses the problems in evaluating nursing diagnostic artificial intelligence (AI) expert systems. Two separate experiments (N = 49) were conducted using a computer expert system. The first experiment, the "white box" experiment (n = 9), compared the diagnostic techniques applied by experience RNs against the programmed techniques used by the expert system. The second experiment, the "black box" experiment (n = 40), compared diagnostic results of beginning nurses against the computer expert systems results. In some cases the computer outperformed the nurses and vice versa. The evaluation techniques, as applied in both experiments, enhance the ability of nurses to evaluate and select AI expert systems to be used in computer-assisted diagnosis of nursing problems.
Subject(s)
Expert Systems , Nursing Diagnosis/standards , Software Validation , Clinical Competence , Humans , Models, Nursing , Nursing Evaluation ResearchABSTRACT
A major bottleneck in the construction of expert systems has traditionally been the solicitation and formalization of expertise from the "human expert." As a means of reducing this bottleneck, the authors propose the use of "text" as a source of knowledge. Acquisition of knowledge from text has not been successful thus far, primarily because most text is not presented in a format (rules, frames, or logic) that can be directly used to load a knowledge base. The authors propose techniques to overcome these inherent problems. This article introduces a model for building an expert system that relies on "text" as the source of knowledge. The model is introduced via a case study involving the building of a nurse expert system designed to replicate the medical diagnostic activities of professional nurses.
