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Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan

BACKGROUND: Few studies have examined whether risk adjustment is evenly applicable to hospitals with various characteristics and case-mix. In this study, we applied a generic prediction model to nationwide discharge data from hospitals with various characteristics. METHOD: We used standardized data...

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Autores principales: Miyata, Hiroaki, Hashimoto, Hideki, Horiguchi, Hiromasa, Fushimi, Kiyohide, Matsuda, Shinya
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882385/
https://www.ncbi.nlm.nih.gov/pubmed/20482816
http://dx.doi.org/10.1186/1472-6963-10-130
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author Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Fushimi, Kiyohide
Matsuda, Shinya
author_facet Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Fushimi, Kiyohide
Matsuda, Shinya
author_sort Miyata, Hiroaki
collection PubMed
description BACKGROUND: Few studies have examined whether risk adjustment is evenly applicable to hospitals with various characteristics and case-mix. In this study, we applied a generic prediction model to nationwide discharge data from hospitals with various characteristics. METHOD: We used standardized data of 1,878,767 discharged patients provided by 469 hospitals from July 1 to October 31, 2006. We generated and validated a case-mix in-hospital mortality prediction model using 50/50 split sample validation. We classified hospitals into two groups based on c-index value (hospitals with c-index ≥ 0.8; hospitals with c-index < 0.8) and examined differences in their characteristics. RESULTS: The model demonstrated excellent discrimination as indicated by the high average c-index and small standard deviation (c-index = 0.88 ± 0.04). Expected mortality rate of each hospital was highly correlated with observed mortality rate (r = 0.693, p < 0.001). Among the studied hospitals, 446 (95%) had a c-index of ≥0.8 and were classified as the higher c-index group. A significantly higher proportion of hospitals in the lower c-index group were specialized hospitals and hospitals with convalescent wards. CONCLUSION: The model fits well to a group of hospitals with a wide variety of acute care events, though model fit is less satisfactory for specialized hospitals and those with convalescent wards. Further sophistication of the generic prediction model would be recommended to obtain optimal indices to region specific conditions.
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spelling pubmed-28823852010-06-09 Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan Miyata, Hiroaki Hashimoto, Hideki Horiguchi, Hiromasa Fushimi, Kiyohide Matsuda, Shinya BMC Health Serv Res Research article BACKGROUND: Few studies have examined whether risk adjustment is evenly applicable to hospitals with various characteristics and case-mix. In this study, we applied a generic prediction model to nationwide discharge data from hospitals with various characteristics. METHOD: We used standardized data of 1,878,767 discharged patients provided by 469 hospitals from July 1 to October 31, 2006. We generated and validated a case-mix in-hospital mortality prediction model using 50/50 split sample validation. We classified hospitals into two groups based on c-index value (hospitals with c-index ≥ 0.8; hospitals with c-index < 0.8) and examined differences in their characteristics. RESULTS: The model demonstrated excellent discrimination as indicated by the high average c-index and small standard deviation (c-index = 0.88 ± 0.04). Expected mortality rate of each hospital was highly correlated with observed mortality rate (r = 0.693, p < 0.001). Among the studied hospitals, 446 (95%) had a c-index of ≥0.8 and were classified as the higher c-index group. A significantly higher proportion of hospitals in the lower c-index group were specialized hospitals and hospitals with convalescent wards. CONCLUSION: The model fits well to a group of hospitals with a wide variety of acute care events, though model fit is less satisfactory for specialized hospitals and those with convalescent wards. Further sophistication of the generic prediction model would be recommended to obtain optimal indices to region specific conditions. BioMed Central 2010-05-19 /pmc/articles/PMC2882385/ /pubmed/20482816 http://dx.doi.org/10.1186/1472-6963-10-130 Text en Copyright ©2010 Miyata 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
Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Fushimi, Kiyohide
Matsuda, Shinya
Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title_full Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title_fullStr Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title_full_unstemmed Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title_short Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
title_sort assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in japan
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882385/
https://www.ncbi.nlm.nih.gov/pubmed/20482816
http://dx.doi.org/10.1186/1472-6963-10-130
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