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Factors influencing hospital high length of stay outliers
BACKGROUND: The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. METHODS: We used hospital administrative data from inpatient episodes in public acute car...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3470984/ https://www.ncbi.nlm.nih.gov/pubmed/22906386 http://dx.doi.org/10.1186/1472-6963-12-265 |
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author | Freitas, Alberto Silva-Costa, Tiago Lopes, Fernando Garcia-Lema, Isabel Teixeira-Pinto, Armando Brazdil, Pavel Costa-Pereira, Altamiro |
author_facet | Freitas, Alberto Silva-Costa, Tiago Lopes, Fernando Garcia-Lema, Isabel Teixeira-Pinto, Armando Brazdil, Pavel Costa-Pereira, Altamiro |
author_sort | Freitas, Alberto |
collection | PubMed |
description | BACKGROUND: The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. METHODS: We used hospital administrative data from inpatient episodes in public acute care hospitals in the Portuguese National Health Service (NHS), with discharges between years 2000 and 2009, together with some hospital characteristics. The dependent variable, LOS outliers, was calculated for each diagnosis related group (DRG) using a trim point defined for each year by the geometric mean plus two standard deviations. Hospitals were classified on the basis of administrative, economic and teaching characteristics. We also studied the influence of comorbidities and readmissions. Logistic regression models, including a multivariable logistic regression, were used in the analysis. All the logistic regressions were fitted using generalized estimating equations (GEE). RESULTS: In near nine million inpatient episodes analysed we found a proportion of 3.9% high LOS outliers, accounting for 19.2% of total inpatient days. The number of hospital patient discharges increased between years 2000 and 2005 and slightly decreased after that. The proportion of outliers ranged between the lowest value of 3.6% (in years 2001 and 2002) and the highest value of 4.3% in 2009. Teaching hospitals with over 1,000 beds have significantly more outliers than other hospitals, even after adjustment to readmissions and several patient characteristics. CONCLUSIONS: In the last years both average LOS and high LOS outliers are increasing in Portuguese NHS hospitals. As high LOS outliers represent an important proportion in the total inpatient days, this should be seen as an important alert for the management of hospitals and for national health policies. As expected, age, type of admission, and hospital type were significantly associated with high LOS outliers. The proportion of high outliers does not seem to be related to their financial coverage; they should be studied in order to highlight areas for further investigation. The increasing complexity of both hospitals and patients may be the single most important determinant of high LOS outliers and must therefore be taken into account by health managers when considering hospital costs. |
format | Online Article Text |
id | pubmed-3470984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34709842012-10-16 Factors influencing hospital high length of stay outliers Freitas, Alberto Silva-Costa, Tiago Lopes, Fernando Garcia-Lema, Isabel Teixeira-Pinto, Armando Brazdil, Pavel Costa-Pereira, Altamiro BMC Health Serv Res Research Article BACKGROUND: The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. METHODS: We used hospital administrative data from inpatient episodes in public acute care hospitals in the Portuguese National Health Service (NHS), with discharges between years 2000 and 2009, together with some hospital characteristics. The dependent variable, LOS outliers, was calculated for each diagnosis related group (DRG) using a trim point defined for each year by the geometric mean plus two standard deviations. Hospitals were classified on the basis of administrative, economic and teaching characteristics. We also studied the influence of comorbidities and readmissions. Logistic regression models, including a multivariable logistic regression, were used in the analysis. All the logistic regressions were fitted using generalized estimating equations (GEE). RESULTS: In near nine million inpatient episodes analysed we found a proportion of 3.9% high LOS outliers, accounting for 19.2% of total inpatient days. The number of hospital patient discharges increased between years 2000 and 2005 and slightly decreased after that. The proportion of outliers ranged between the lowest value of 3.6% (in years 2001 and 2002) and the highest value of 4.3% in 2009. Teaching hospitals with over 1,000 beds have significantly more outliers than other hospitals, even after adjustment to readmissions and several patient characteristics. CONCLUSIONS: In the last years both average LOS and high LOS outliers are increasing in Portuguese NHS hospitals. As high LOS outliers represent an important proportion in the total inpatient days, this should be seen as an important alert for the management of hospitals and for national health policies. As expected, age, type of admission, and hospital type were significantly associated with high LOS outliers. The proportion of high outliers does not seem to be related to their financial coverage; they should be studied in order to highlight areas for further investigation. The increasing complexity of both hospitals and patients may be the single most important determinant of high LOS outliers and must therefore be taken into account by health managers when considering hospital costs. BioMed Central 2012-08-20 /pmc/articles/PMC3470984/ /pubmed/22906386 http://dx.doi.org/10.1186/1472-6963-12-265 Text en Copyright ©2012 Freitas et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Freitas, Alberto Silva-Costa, Tiago Lopes, Fernando Garcia-Lema, Isabel Teixeira-Pinto, Armando Brazdil, Pavel Costa-Pereira, Altamiro Factors influencing hospital high length of stay outliers |
title | Factors influencing hospital high length of stay outliers |
title_full | Factors influencing hospital high length of stay outliers |
title_fullStr | Factors influencing hospital high length of stay outliers |
title_full_unstemmed | Factors influencing hospital high length of stay outliers |
title_short | Factors influencing hospital high length of stay outliers |
title_sort | factors influencing hospital high length of stay outliers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3470984/ https://www.ncbi.nlm.nih.gov/pubmed/22906386 http://dx.doi.org/10.1186/1472-6963-12-265 |
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