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

Evelene M Carter,Henry Ww Potts

doi:10.1186/1472-6947-14-26

Abstract

BackgroundTo 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.MethodsData 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.ResultsFactors 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).ConclusionsValuable 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.

Highlights

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
There were no significant seasonal effects on Length of stay (LoS) for primary total knee replacements (PTKs), there has been a significant reduction in the average LoS in 2011 from 5 to 4 days
Patients admitted on a Tuesday and Wednesday stayed for 1 day longer (6 days) than those admitted on a Monday or Friday

Summary

Introduction

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. Length of stay (LoS) is an important metric for assessing the quality of care and planning capacity within a hospital It is a key performance indicator for the Department of Health (DoH) in England, used to monitor hospital quality and manage patient expectation. Hospitals are constantly adapting to clinical and financial pressures driven by policy changes, including recent attention towards reducing LoS where differences between hospitals are shown to vary widely [2]. This continuous pressure for improvement requires hospitals to review their processes to become more cost efficient and more standardised to improve patient expectation. Gaining a better understanding of LoS provides an opportunity to reduce the time patients stay in hospital [3]

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Conclusion