Length of stay as a performance indicator: a robust statistical methodology

Kulinskaya, Elena, Kornbrot, Diana and Gao, Haiyan (2005) Length of stay as a performance indicator: a robust statistical methodology. IMA Journal of Management Mathematics, 16 (4). pp. 369-381. ISSN 1471-678X

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Abstract

Length of stay (LOS) is an important performance indicator for costing and hospital management and a key measure of efficiency of NHS. However, LOS is difficult to analyse because its statistical distribution is non-normal and LOS data habitually have many outliers. Furthermore, the usefulness of LOS for improving NHS performance is undermined because no adjustments are made for some key factors. This paper addresses both these problems. Health episodes statistics data from the UK NHS for 1997/98, and 1998/99 are analysed to investigate the effects of five key variables: admission method, discharge destination, provider (hospital) type, speciality and NHS region. All are found to influence LOS. The effects of some factors are substantial, and were not previously known, and so are not included in planned future NHS performance measures, e.g. LOS is at least 25% longer for patients transferred from other hospitals rather than admitted as an emergency; and LOS for patients discharged to private institutions is more than twice that for patients discharged to NHS institutions or their own home. The problem of finding the most appropriate statistical analysis for data of the LOS type is addressed by comparing standard general linear model methods with an advanced robust method called truncated maximum likelihood (TML). The TML methods are shown to have several advantages over standard methods, in terms of model fit and accuracy of parameter estimation. Implications of these findings for future use of LOS are considered.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Vishal Gautam
Date Deposited:	20 Jul 2011 13:02
Last Modified:	17 Apr 2023 23:39
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23608
DOI:	10.1093/imaman/dpi015

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