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Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies

BACKGROUND: Accurate calculation of hospital length of stay (LOS) from the English Hospital Episode Statistics (HES) is important for a wide range of audit and research purposes. The two methodologies which are commonly used to achieve this differ in their accuracy and complexity. We compare these m...

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Autores principales: Busby, John, Purdy, Sarah, Hollingworth, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427566/
https://www.ncbi.nlm.nih.gov/pubmed/28499377
http://dx.doi.org/10.1186/s12913-017-2295-z
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author Busby, John
Purdy, Sarah
Hollingworth, William
author_facet Busby, John
Purdy, Sarah
Hollingworth, William
author_sort Busby, John
collection PubMed
description BACKGROUND: Accurate calculation of hospital length of stay (LOS) from the English Hospital Episode Statistics (HES) is important for a wide range of audit and research purposes. The two methodologies which are commonly used to achieve this differ in their accuracy and complexity. We compare these methods and make recommendations on when each is most appropriate. METHODS: We calculated LOS using continuous inpatient spells (CIPS), which link care spanning across multiple hospitals, and spells, which do not, for six conditions with short (dyspepsia or other stomach function, ENT infection), medium (dehydration and gastroenteritis, perforated or bleeding ulcer), and long (stroke, fractured proximal femur) average LOS. We examined how inter-area comparisons (i.e. benchmarking) and temporal trends differed. We defined a classification system for spells and explored the causes of differences. RESULTS: Stroke LOS was 16.5 days using CIPS but 24% (95% CI: 23, 24) lower, at 12.6 days, using spells. Smaller differences existed for shorter-LOS conditions including dehydration and gastroenteritis (4.5 vs. 4.2 days) and ENT infection (0.9 vs. 0.8 days). Typical patient pathways differed markedly between areas and have evolved over time. One area had the third shortest stroke LOS (out of 151) using spells but the fourth longest using CIPS. These issues were most profound for stroke and fractured proximal femur, as patients were frequently transferred to a separate hospital for rehabilitation, however important disparities also existed for conditions with simpler secondary care pathways (e.g. ENT infections, dehydration and gastroenteritis). CONCLUSIONS: Spell-based LOS is widely used by researchers and national reporting organisations, including the Health and Social Care Information Centre, however it can substantially underestimate the time patients spend in hospital. A widespread shift to a CIPS methodology is required to improve the quality of LOS estimates and the robustness of research and benchmarking findings. This is vital when investigating clinical areas with typically long, complex patient pathways. Researchers should ensure that their LOS calculation methodology is fully described and explicitly acknowledge weaknesses when appropriate.
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spelling pubmed-54275662017-05-15 Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies Busby, John Purdy, Sarah Hollingworth, William BMC Health Serv Res Research Article BACKGROUND: Accurate calculation of hospital length of stay (LOS) from the English Hospital Episode Statistics (HES) is important for a wide range of audit and research purposes. The two methodologies which are commonly used to achieve this differ in their accuracy and complexity. We compare these methods and make recommendations on when each is most appropriate. METHODS: We calculated LOS using continuous inpatient spells (CIPS), which link care spanning across multiple hospitals, and spells, which do not, for six conditions with short (dyspepsia or other stomach function, ENT infection), medium (dehydration and gastroenteritis, perforated or bleeding ulcer), and long (stroke, fractured proximal femur) average LOS. We examined how inter-area comparisons (i.e. benchmarking) and temporal trends differed. We defined a classification system for spells and explored the causes of differences. RESULTS: Stroke LOS was 16.5 days using CIPS but 24% (95% CI: 23, 24) lower, at 12.6 days, using spells. Smaller differences existed for shorter-LOS conditions including dehydration and gastroenteritis (4.5 vs. 4.2 days) and ENT infection (0.9 vs. 0.8 days). Typical patient pathways differed markedly between areas and have evolved over time. One area had the third shortest stroke LOS (out of 151) using spells but the fourth longest using CIPS. These issues were most profound for stroke and fractured proximal femur, as patients were frequently transferred to a separate hospital for rehabilitation, however important disparities also existed for conditions with simpler secondary care pathways (e.g. ENT infections, dehydration and gastroenteritis). CONCLUSIONS: Spell-based LOS is widely used by researchers and national reporting organisations, including the Health and Social Care Information Centre, however it can substantially underestimate the time patients spend in hospital. A widespread shift to a CIPS methodology is required to improve the quality of LOS estimates and the robustness of research and benchmarking findings. This is vital when investigating clinical areas with typically long, complex patient pathways. Researchers should ensure that their LOS calculation methodology is fully described and explicitly acknowledge weaknesses when appropriate. BioMed Central 2017-05-12 /pmc/articles/PMC5427566/ /pubmed/28499377 http://dx.doi.org/10.1186/s12913-017-2295-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Busby, John
Purdy, Sarah
Hollingworth, William
Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title_full Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title_fullStr Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title_full_unstemmed Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title_short Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies
title_sort calculating hospital length of stay using the hospital episode statistics; a comparison of methodologies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427566/
https://www.ncbi.nlm.nih.gov/pubmed/28499377
http://dx.doi.org/10.1186/s12913-017-2295-z
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