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Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling
OBJECTIVES: Accreditation in France relies on a mandatory 4-year cycle of self-assessment and a peer review of 82 standards, among which 14 focus priority standards (FPS). Hospitals are also required to measure yearly quality indicators (QIs—5 in 2010). On advice given by the accreditation committee...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758968/ https://www.ncbi.nlm.nih.gov/pubmed/23996820 http://dx.doi.org/10.1136/bmjopen-2013-003289 |
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author | Guérin, Sophie Le Pogam, Marie-Annick Robillard, Benjamin Le Vaillant, Marc Lucet, Bruno Gardel, Christine Grenier, Catherine Loirat, Philippe |
author_facet | Guérin, Sophie Le Pogam, Marie-Annick Robillard, Benjamin Le Vaillant, Marc Lucet, Bruno Gardel, Christine Grenier, Catherine Loirat, Philippe |
author_sort | Guérin, Sophie |
collection | PubMed |
description | OBJECTIVES: Accreditation in France relies on a mandatory 4-year cycle of self-assessment and a peer review of 82 standards, among which 14 focus priority standards (FPS). Hospitals are also required to measure yearly quality indicators (QIs—5 in 2010). On advice given by the accreditation committee of HAS (Haute Autorité en Santé), based on surveyors proposals and relying mostly on compliance to standards, accreditation decisions are taken by the board of HAS. Accreditation is still perceived by hospitals as a burdensome process and a simplification would be welcomed. The hypothesis was that a more limited number of criteria might give sufficient amount of information on hospitals overall quality level, appraised today by accreditation decisions. DESIGN: The accuracy of predictions of accreditation decisions given by a model, Partial Least Square-2 Discriminant Analysis (PLS2-DA), using only the results of FPS and QIs was measured. Accreditation decisions (full accreditation (A), recommendations or reservation (B), remit decision or non-accreditation (C)), results of FPS and QIs were considered qualitative variables. Stability was assessed by leave one out cross validation (LOOCV). SETTING AND PARTICIPANTS: All French 489 acute care organisations (ACO) accredited between June 2010 and January 2012 were considered, 304 of them having a rehabilitation care sector (RCS). RESULTS: Accuracy of prediction of accreditation decisions was good (89% of ACOs and 91% of ACO-RCS well classified). Stability of results appeared satisfactory when using LOOCV (87% of ACOs and 89% of ACO-RCS well classified). Identification of worse hospitals was correct (90% of ACOs and 97% of ACO-RCS predicted C were actually C). CONCLUSIONS: Using PLS2-DA with a limited number of criteria (QIs and FPS) provides an accurate prediction of accreditation decisions, especially for underperforming hospitals. This could support accreditation committees which give advices on accreditation decisions, and allow fast-track handling of ‘safe’ reports. |
format | Online Article Text |
id | pubmed-3758968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-37589682013-09-03 Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling Guérin, Sophie Le Pogam, Marie-Annick Robillard, Benjamin Le Vaillant, Marc Lucet, Bruno Gardel, Christine Grenier, Catherine Loirat, Philippe BMJ Open Health Services Research OBJECTIVES: Accreditation in France relies on a mandatory 4-year cycle of self-assessment and a peer review of 82 standards, among which 14 focus priority standards (FPS). Hospitals are also required to measure yearly quality indicators (QIs—5 in 2010). On advice given by the accreditation committee of HAS (Haute Autorité en Santé), based on surveyors proposals and relying mostly on compliance to standards, accreditation decisions are taken by the board of HAS. Accreditation is still perceived by hospitals as a burdensome process and a simplification would be welcomed. The hypothesis was that a more limited number of criteria might give sufficient amount of information on hospitals overall quality level, appraised today by accreditation decisions. DESIGN: The accuracy of predictions of accreditation decisions given by a model, Partial Least Square-2 Discriminant Analysis (PLS2-DA), using only the results of FPS and QIs was measured. Accreditation decisions (full accreditation (A), recommendations or reservation (B), remit decision or non-accreditation (C)), results of FPS and QIs were considered qualitative variables. Stability was assessed by leave one out cross validation (LOOCV). SETTING AND PARTICIPANTS: All French 489 acute care organisations (ACO) accredited between June 2010 and January 2012 were considered, 304 of them having a rehabilitation care sector (RCS). RESULTS: Accuracy of prediction of accreditation decisions was good (89% of ACOs and 91% of ACO-RCS well classified). Stability of results appeared satisfactory when using LOOCV (87% of ACOs and 89% of ACO-RCS well classified). Identification of worse hospitals was correct (90% of ACOs and 97% of ACO-RCS predicted C were actually C). CONCLUSIONS: Using PLS2-DA with a limited number of criteria (QIs and FPS) provides an accurate prediction of accreditation decisions, especially for underperforming hospitals. This could support accreditation committees which give advices on accreditation decisions, and allow fast-track handling of ‘safe’ reports. BMJ Publishing Group 2013-08-29 /pmc/articles/PMC3758968/ /pubmed/23996820 http://dx.doi.org/10.1136/bmjopen-2013-003289 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Health Services Research Guérin, Sophie Le Pogam, Marie-Annick Robillard, Benjamin Le Vaillant, Marc Lucet, Bruno Gardel, Christine Grenier, Catherine Loirat, Philippe Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title | Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title_full | Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title_fullStr | Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title_full_unstemmed | Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title_short | Can we simplify the hospital accreditation process? Predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
title_sort | can we simplify the hospital accreditation process? predicting accreditation decisions from a reduced dataset of focus priority standards and quality indicators: results of predictive modelling |
topic | Health Services Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758968/ https://www.ncbi.nlm.nih.gov/pubmed/23996820 http://dx.doi.org/10.1136/bmjopen-2013-003289 |
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