To Tara via Holyhead: The Emergence of Irish Catholic Ethinicity in Nineteenth-Century Christchurch, New Zealand
In: Journal of social history, Band 36, Heft 2, S. 431-458
ISSN: 1527-1897
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In: Journal of social history, Band 36, Heft 2, S. 431-458
ISSN: 1527-1897
In: The Economic Journal, Band 49, Heft 195, S. 544
In: Economica, Band 6, Heft 21, S. 67
In: The Economic Journal, Band 48, Heft 190, S. 196
In: The Economic Journal, Band 54, Heft 214, S. 229
In: The Economic Journal, Band 49, Heft 194, S. 317
In: The Economic Journal, Band 48, Heft 189, S. 87
In: Economica, Band 4, Heft 16, S. 469
In: The Economic Journal, Band 45, Heft 178, S. 311
In: The Economic Journal, Band 41, Heft 164, S. 635
This is a conference paper. Made available by kind permission of the publisher. ; As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis.
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In: The Economic Journal, Band 49, Heft 196, S. 741
In: The Economic Journal, Band 48, Heft 189, S. 89