Motivated by the properties of spreading activation and conceptual distance, the authors propose a metric, called Distance, on the power set of nodes in a semantic net. Distance is the average minimum path length over all painvise combinations of nodes between two subsets of nodes. Distance can be successfully used to assess the conceptual distance between sets of concepts when used on a semantic net of hierarchical relations. When other kinds of relationships, like "cause," are used, Distance must be amended but then can again be effective. The judgments of Distance significantly correlate with the distance judgments that people make and help us determine whether semantic net SI is better or worse than semantic net S,. First a "conceptual distance" task is set, and people are asked to perform it. Then the same task is performed by Distance on SI and &. If Distance on SI performs more like people than Distance on S,, the conclusion is that SI is better than S,. Distance embedded in the methodology facilitates repeatable quantitative experiments.

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To assess and plan alterations in outpatient clinic structure, produces a computer simulation of an outpatient clinic based on detailed time and role measurements from the authors' clinic. The simulation which used an objectoriented design method is able to indicate the impact of changes in clinic structure using patient and doctor waiting times in clinic as endpoint measures. The effects of changes in clinic size, consultation time, patient mix, appointment scheduling and non-attendance were examined. We found that patient waiting time could be shortened considerably by using an optimizing appointment scheduler to determine appointment intervals. Clinic mix influences patient waiting time, which was shorter with a 1 in 4 ratio of new to followup patients. In mixed clinics, new patients appointments are optimally spread throughout the clinic to reduce patient waiting time. In all new or all follow-up clinics, waiting time is improved if the appointment interval reflects the consultation time. Computer modelling can help in optimizing clinic management so improving the delivery of care in outpatient services.

This article reports on exploratory experiments in evaluating and improving a thesaurus through studying its effect on retrieval. A formula called DISTANCE was developed to measure the conceptual distance between queries and documents encoded as sets of thesaurus terms. DISTANCE references MeSH (Medical Subject Headings) and assesses the degree of match between a MeSH-encoded query and document. The performance of DISTANCE on MeSH is compared to the performance of people in the assessment of conceptual distance between queries and documents, and is found to simulate with surprising accuracy the human performance. The power of the computer simulation stems both from the tendency of people to rely heavily on broader-than (BT) relations in making decisions about conceptual distance and from the thousands of accurate BT relations in MeSH. One source for discrepancy between the algorithms' measurement of closeness between query and document and people's measurement of closeness between query and document is occasional inconsistency in the BT relations. Our experiments with adding non-BT relations to MeSH showed how these non-BT non-BT relations to MeSH showed how these non-BT relations could improve document ranking, if DISTANCE were also appropriately revised to treat these relations differently from BT relations.