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Open Access Highly Accessed Research article

Predicting length of stay from an electronic patient record system: a primary total knee replacement example

Evelene M Carter1* and Henry WW Potts2

Author Affiliations

1 Information Services Department, Oxford University Hospitals NHS Trust, Oxford, UK

2 Institute of Epidemiology and Health Care, University College London, London, UK

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BMC Medical Informatics and Decision Making 2014, 14:26  doi:10.1186/1472-6947-14-26

Published: 4 April 2014

Abstract

Background

To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay.

Methods

Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n = 2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman’s correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques.

Results

Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4–6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data).

Conclusions

Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

Keywords:
Length of stay; Regression analysis; Models, statistical; negative binomial; Total knee replacement; Computerized Medical Records; Hospital planning