Ontological clarity and comprehension in health data models

Conceptual modeling forms an important part of systems analysis. If this is done incorrectly or incompletely, there can be serious implications for the resultant system, specifically in terms of rework and useability. One approach to improving the conceptual modelling process is to evaluate how well the model represents reality. Emergence of the Bunge-Wand-Weber (BWW) ontological model introduced a platform to classify and compare the grammar of conceptual modelling languages. This work applies the BWW theory to a real world example in the health arena. The general practice computing group data model was developed using the Barker Entity Relationship Modelling technique. We describe an experiment, grounded in ontological theory, which evaluates how well the GPCG data model is understood by domain experts. The results show that with the exception of the use of entities to represent events, the raw model is better understood by domain experts

Testing new alarms for medical electrical equipment

This paper reports the first of several tests of new auditory alarms originally proposed by Block et al. [1] and formalized in IEC 60601-1-8 for use in medical electrical equipment. We test whether participants who are supplied with the IEC-recommended mnemonics while learning label-alarm associations can more accurately identify the alarms after short periods of learning. Results for 18 participants strongly indicate that there is a mutual confusability between certain alarm pairs in both learning conditions, but that mnemonics may strengthen rather than diminish certain key confusions.