Data Management For An Anaesthesiology
Department Optimization
Author(s)
A. Rabasa, A. F. Compañ, A. J. Agulló, J. J. Rodríguez-Sala,
L. Santamaría & L. Noguera
Abstract
This paper is part of a Study in Progress which presents the Data Management
main guidelines for a live project designed to optimize the anaesthesiology
department of a public hospital in Spain. The main goal is the detection of
dispensable pre-surgery tests (e.g. chest radiography, blood test or
electrocardiogram), if there is a low correlation in the contrast between a
patient’s clinical history and incidences in the operating theatre. The paper’s
contribution consists of two parts. Part I shows the development of a data base
web application to facilitate data entry of both the anaesthesiology and the
operation data itself. The design of the web data base forms and the expert
(anaesthetist) specifications are shown. Part II shows how to choose and design
the classification rules as the most appropriate Data Mining method for this goal
and explains how such a method must be parameterized. Thus, from the
historical and clinical data of each new patient, the system could advise about the
suitability (or not) for certain expensive and invasive pre-surgery tests (as chest
radiography can be). By applying this software, the Hospital will be able to
improve the scheduling of critical resources that are often very expensive and
cause delays in operation waiting lists. Finally, as future research line, this
research aims to extend the proposed method to other medical departments in
order to achieve lower costs, avoid invasive tests and waiting lists, in conditions
where it is statistically possible.
Keywords: Data Mining, forecasting, optimization, medicine, anaesthesiology,
classification rules.
Keywords
Keywords: Data Mining, forecasting, optimization, medicine, anaesthesiology,
classification rules.
Related Book
Other papers in this volume
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