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Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations
Background: Hospitalizations of nursing home residents are associated with various health risks. Previous research indicates that, to some extent, hospitalizations of this vulnerable population may be inappropriate and even avoidable. This study aimed to develop a consensus list of hospital discharg...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021670/ https://www.ncbi.nlm.nih.gov/pubmed/35464174 http://dx.doi.org/10.12688/f1000research.73875.2 |
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author | Bohnet-Joschko, Sabine Valk-Draad, Maria Paula Schulte, Timo Groene, Oliver |
author_facet | Bohnet-Joschko, Sabine Valk-Draad, Maria Paula Schulte, Timo Groene, Oliver |
author_sort | Bohnet-Joschko, Sabine |
collection | PubMed |
description | Background: Hospitalizations of nursing home residents are associated with various health risks. Previous research indicates that, to some extent, hospitalizations of this vulnerable population may be inappropriate and even avoidable. This study aimed to develop a consensus list of hospital discharge diagnoses considered to be nursing home-sensitive, i.e., avoidable. Methods: The study combined analyses of routine data from six statutory health insurance companies in Germany and a two-stage Delphi panel, enhanced by expert workshop discussions, to identify and corroborate relevant diagnoses. Experts from four different disciplines estimated the proportion of hospitalizations that could potentially have been prevented under optimal conditions. Results: We analyzed frequencies and costs of data for hospital admissions from 242,236 nursing home residents provided by statutory health insurance companies. We identified 117 hospital discharge diagnoses, which had a frequency of at least 0.1%. We recruited experts (primary care physicians, hospital specialists, nursing home professionals and researchers) to estimate the proportion of potentially avoidable hospitalizations for the 117 diagnoses deemed avoidable in two Delphi rounds (n=107 in Delphi Round 1 and n=96 in Delphi Round 2, effective response rate=91%). A total of 35 diagnoses with high and consistent estimates of the proportion of potentially avoidable hospitalizations were identified as nursing home-sensitive. In an expert workshop (n=16), a further 25 diagnoses were discussed that had not reached the criteria, of which another 23 were consented to be nursing home-sensitive conditions. Extrapolating the frequency and mean costs of these 58 diagnoses to the national German context yielded total potentially avoidable care costs of €768,304,547, associated with 219,955 nursing home-sensitive hospital admissions. Conclusion: A total of 58 nursing home-relevant diagnoses (ICD-10-GM three-digit level) were classified as nursing home-sensitive using an adapted Delphi procedure. Interventions should be developed to avoid hospital admission from nursing homes for these diagnoses. |
format | Online Article Text |
id | pubmed-9021670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-90216702022-04-21 Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations Bohnet-Joschko, Sabine Valk-Draad, Maria Paula Schulte, Timo Groene, Oliver F1000Res Research Article Background: Hospitalizations of nursing home residents are associated with various health risks. Previous research indicates that, to some extent, hospitalizations of this vulnerable population may be inappropriate and even avoidable. This study aimed to develop a consensus list of hospital discharge diagnoses considered to be nursing home-sensitive, i.e., avoidable. Methods: The study combined analyses of routine data from six statutory health insurance companies in Germany and a two-stage Delphi panel, enhanced by expert workshop discussions, to identify and corroborate relevant diagnoses. Experts from four different disciplines estimated the proportion of hospitalizations that could potentially have been prevented under optimal conditions. Results: We analyzed frequencies and costs of data for hospital admissions from 242,236 nursing home residents provided by statutory health insurance companies. We identified 117 hospital discharge diagnoses, which had a frequency of at least 0.1%. We recruited experts (primary care physicians, hospital specialists, nursing home professionals and researchers) to estimate the proportion of potentially avoidable hospitalizations for the 117 diagnoses deemed avoidable in two Delphi rounds (n=107 in Delphi Round 1 and n=96 in Delphi Round 2, effective response rate=91%). A total of 35 diagnoses with high and consistent estimates of the proportion of potentially avoidable hospitalizations were identified as nursing home-sensitive. In an expert workshop (n=16), a further 25 diagnoses were discussed that had not reached the criteria, of which another 23 were consented to be nursing home-sensitive conditions. Extrapolating the frequency and mean costs of these 58 diagnoses to the national German context yielded total potentially avoidable care costs of €768,304,547, associated with 219,955 nursing home-sensitive hospital admissions. Conclusion: A total of 58 nursing home-relevant diagnoses (ICD-10-GM three-digit level) were classified as nursing home-sensitive using an adapted Delphi procedure. Interventions should be developed to avoid hospital admission from nursing homes for these diagnoses. F1000 Research Limited 2022-04-06 /pmc/articles/PMC9021670/ /pubmed/35464174 http://dx.doi.org/10.12688/f1000research.73875.2 Text en Copyright: © 2022 Bohnet-Joschko S et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Bohnet-Joschko, Sabine Valk-Draad, Maria Paula Schulte, Timo Groene, Oliver Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title | Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title_full | Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title_fullStr | Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title_full_unstemmed | Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title_short | Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations |
title_sort | nursing home-sensitive conditions: analysis of routine health insurance data and modified delphi analysis of potentially avoidable hospitalizations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021670/ https://www.ncbi.nlm.nih.gov/pubmed/35464174 http://dx.doi.org/10.12688/f1000research.73875.2 |
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