Medication errors: hospital pharmacist perspective.

In recent years medication error has justly received considerable attention, as it causes substantial mortality, morbidity and additional healthcare costs. Risk assessment models, adapted from commercial aviation and the oil and gas industries, are currently being developed for use in clinical pharmacy. The hospital pharmacist is best placed to oversee the quality of the entire drug distribution chain, from prescribing, drug choice, dispensing and preparation to the administration of drugs, and can fulfil a vital role in improving medication safety. Most elements of the drug distribution chain can be optimised; however, because comparative intervention studies are scarce, there is little scientific evidence available demonstrating improvements in medication safety through such interventions. Possible interventions aimed at reducing medication errors, such as developing methods for detection of patients with increased risk of adverse drug events, performing risk assessment in clinical pharmacy and optimising the drug distribution chain are discussed. Moreover, the specific role of the clinical pharmacist in improving medication safety is highlighted, both at an organisational level and in individual patient care.

SCAN: A Scalable Model of Attentional Selection.

This paper describes the SCAN (Signal Channelling Attentional Network) model, a scalable neural network model for attentional scanning. The building block of SCAN is a gating lattice, a sparsely-connected neural network defined as a special case of the Ising lattice from statistical mechanics. The process of spatial selection through covert attention is interpreted as a biological solution to the problem of translation-invariant pattern processing. In SCAN, a sequence of pattern translations combines active selection with translation-invariant processing. Selected patterns are channelled through a gating network, formed by a hierarchical fractal structure of gating lattices, and mapped onto an output window. We show how the incorporation of an expectation-generating classifier network (e.g. Carpenter and Grossberg's ART network) into SCAN allows attentional selection to be driven by expectation. Simulation studies show the SCAN model to be capable of attending and identifying object patterns that are part of a realistically sized natural image. Copyright 1997 Elsevier Science Ltd.