Optimal Scheduling Using Length-of-Stay Data for Diverse Routine Procedures

Currie, Christine S. M.; Lu, Lanting

doi:10.1007/978-3-642-00179-6_12

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…It has already been used successfully to group machines based on their breakdown duration data (Lu et al, 2007) and in a healthcare setting to group procedures based on patients' length-of-stay in hospital (Currie and Lu, 2009), as described in Sec. The implementation is relatively simple and should have a wide range of applications.…”

Section: Resultsmentioning

confidence: 99%

“…This example comes from (Currie and Lu, 2009), where the classification was used to preprocess length-of-stay data for hospital procedures to use in Gallivan and Utley's linear programming approach for setting up optimal schedules (Gallivan and Utley, 2005). We have length-of-stay data for 10,929 different episodes recorded over a period of 7 months coming from 655 different procedures.…”

Section: Classification Of Hospital Length Of Stay Datamentioning

confidence: 99%

“…The results of the ACM, using a p-value threshold of 0.10, suggest that there should be 48 groups, which are listed in (Currie and Lu, 2009). The largest group contains 8 procedures and there are four groups of 7 procedures; 14 groups contain only one procedure.…”

Section: Classification Of Hospital Length Of Stay Datamentioning

confidence: 99%

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Evaluation of the Arrows Method for Classification of Data

Currie

2010

Asia Pac. J. Oper. Res.

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We evaluate the Arrows Classification Method (ACM) for grouping objects based on the similarity of their data. This is a new method, which aims to achieve a balance between the conflicting objectives of maximizing internal cohesion and external isolation in the output groups. The method is widely applicable, especially in simulation input and output modelling, and has previously been used for grouping machines on an assembly line, based on data on time-to-repair; and hospital procedures, based on length-of-stay data. The similarity of the data from a pair of objects is measured using the two-sample Cramér-von-Mises goodness of fit statistic, with bootstrapping employed to find the significance or p-value of the calculated statistic. The p-values coming from the paired comparisons serve as inputs to the ACM, and allow the objects to be classified such that no pair of objects that are grouped together have significantly different data. In this article, we give the technical details of the method and evaluate its use through testing with specially generated samples. We will also demonstrate its practical application with two real examples.

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Section: Resultsmentioning

confidence: 99%

Section: Classification Of Hospital Length Of Stay Datamentioning

confidence: 99%