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NEONATE is a prototype of an expert system for the Newborn Intensive Care Unit developed at the Primary Children's Medical Center in Salt Lake City, Utah. A pilot study was undertaken to see if the addition of radiological frames to the NEONATE software could aid attending Neonatologists to interpret chest X-ray films. A set of radiological frames was created from rules generated by a radiologist. The performance of these radiological frames was compared to the performance of other radiologists using the Kappa statistic to measure agreement. There is a good agreement between the computer's decisions and the radiologists' decisions. The radiological frames were also tested to see if they help physicians who are not trained in radiology. A system that compares the residents' interpretation and the computer's interpretation to a gold standard interpretation was developed. It shows that the computer helps the first and second year residents, but not the third year residents. This article suggests that NEONATE's interpretation of chest X-ray findings are close to the radiologists' interpretations. While NEONATE's radiological frames help novice physicians in reaching better chest X-ray interpretation, the current study suggests that they are not likely to help a Neonatologist.

Iliad is a computerized, expert system for internal medical diagnosis. The system is designed to teach diagnostic skills by means of simulated patient case presentations. We report the results of a controlled trial in which junior students were randomly assigned to received Iliad training on one of two different simulated case mixes. Each group was subsequently tested in both their "trained" and "untrained" case domain. The testing consisted of computerized, simulated patient cases for which no training feedback was provided. Outcome variables were designed to measure the students' performance on these test cases. The results indicate that students made fewer diagnostic errors and more conclusively confirmed their diagnostic hypotheses when they were tested in their trained domain. We conclude that expert systems such as Iliad can effectively teach diagnostic skills by supplementing trainees' actual case experience with computerized simulations.

NEONATE is a prototype of an expert application for the HELP Hospital Information System. Its goal is to improve documentation in the Newborn Intensive Care Unit. The decision module of NEONATE is designed to produce an admission problem list. In this paper, the admission problem list that NEONATE generates was compared to the admission problem list of the current CETUS system for 30 patients. These were compared to a retrospectively constructed gold standard problem list. Of 101 problems in the gold standard list, 56 were on the current admission reports; 82 were found by NEONATE. NEONATE found 31 problems missed on the current admission reports; the current admission reports contained 5 problems missed by NEONATE. The current admission reports contained 9 false positives; whereas NEONATE's reports contained 27. Of the 27, 16 were caused by a single rule in NEONATE. We conclude that an expert system has great potential for improving the documentation of the patient problem list.