Combining data mining tools with health care models for improved understanding of health processes and resource utilisation.
Variability and uncertainty are inherent characteristics of most health care processes. Patient pathways and dwelling times even within the same process typically vary from patient to patient, such as the flow of patients through a particular health care provider or patient progression through the natural history of a given disease. The challenge for the OR modeller is to adequately handle and capture the stochastic features within developed models. This paper will discuss the benefits of combining patient classification tools (data mining techniques) with developed OR models, such as simulation tools, to more accurately capture patient outcomes, risks and resource needs. Illustrative applications will demonstrate the approach.
Harper
Paul
P
School of Mathematics, University of Southampton, Highfield, UK. p.r.harper@maths.soton.ac.uk
D003660:Decision Support Systems, Management; D006295:Health Resources / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients / Q000145:classification*; D007902:Length of Stay; D017146:Models, Organizational; D015233:Models, Statistical; D020380:Needs Assessment; D011787:Quality of Health Care; D018570:Risk Assessment