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A cost simulation for mammography examinations taking into account equipment failures and resource utilization characteristics
Author(s) -
Coelli Fernando C.,
Almeida Renan M. V. R.,
Pereira Wagner C. A.
Publication year - 2010
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2009.01294.x
Subject(s) - mammography , attendance , medicine , unit (ring theory) , discrete event simulation , resource (disambiguation) , medical emergency , operations management , reliability engineering , computer science , simulation , engineering , psychology , computer network , mathematics education , cancer , breast cancer , economics , economic growth
Objective This work develops a cost analysis estimation for a mammography clinic, taking into account resource utilization and equipment failure rates. Materials and methods Two standard clinic models were simulated, the first with one mammography equipment, two technicians and one doctor, and the second (based on an actually functioning clinic) with two equipments, three technicians and one doctor. Cost data and model parameters were obtained by direct measurements, literature reviews and other hospital data. A discrete‐event simulation model was developed, in order to estimate the unit cost (total costs/number of examinations in a defined period) of mammography examinations at those clinics. The cost analysis considered simulated changes in resource utilization rates and in examination failure probabilities (failures on the image acquisition system). In addition, a sensitivity analysis was performed, taking into account changes in the probabilities of equipment failure types. Results For the two clinic configurations, the estimated mammography unit costs were, respectively, US$ 41.31 and US$ 53.46 in the absence of examination failures. As the examination failures increased up to 10% of total examinations, unit costs approached US$ 54.53 and US$ 53.95, respectively. The sensitivity analysis showed that type 3 (the most serious) failure increases had a very large impact on the patient attendance, up to the point of actually making attendance unfeasible. Conclusions Discrete‐event simulation allowed for the definition of the more efficient clinic, contingent on the expected prevalence of resource utilization and equipment failures.