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How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK
OBJECTIVES: The relationship between patient feedback in the General Practice Patient Survey (GPPS) and Care Quality Commission (CQC) inspections of practices was investigated to understand whether there is an association between patient views and regulator ratings of quality. The specific aims were...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692819/ https://www.ncbi.nlm.nih.gov/pubmed/33243813 http://dx.doi.org/10.1136/bmjopen-2020-041709 |
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author | Tallett, Amy Poots, Alan J Graham, Chris Peters, Michele Corbett, Rory Sizmur, Steve Forder, Julien |
author_facet | Tallett, Amy Poots, Alan J Graham, Chris Peters, Michele Corbett, Rory Sizmur, Steve Forder, Julien |
author_sort | Tallett, Amy |
collection | PubMed |
description | OBJECTIVES: The relationship between patient feedback in the General Practice Patient Survey (GPPS) and Care Quality Commission (CQC) inspections of practices was investigated to understand whether there is an association between patient views and regulator ratings of quality. The specific aims were to understand whether patients’ self-reported experiences of primary care can predict CQC inspection ratings of GP practices by: (i) Measuring the association between GPPS results and CQC inspection ratings of GP practices; (ii) Building a predictive model of GP practice quality ratings that use GPPS results; and (iii) Evaluating the predictive model for risk stratification. DESIGN: Retrospective analysis of routinely collected data using decision tree modelling. SETTING: Primary care: GP practices in England. PRIMARY AND SECONDARY OUTCOME MEASURES: GPPS scores and GP practice CQC inspection ratings during 2018. RESULTS: Most GP practices (72%, 974/1350) were rated as ‘Good’ overall by CQC. Simply assuming that all practices will be rated as ‘Good’ results in a correct prediction 72% of the time, and it was not possible to improve on this overall level of predictive accuracy using decision tree modelling (correct in 73% of cases). However, a set of GPPS questions were found to have value in identifying practices at elevated risk of a poor inspection rating. CONCLUSIONS: Although there were some associations between GPPS data and CQC inspection ratings, there were limitations to the use of GPPS data for predictive analysis. This is a likely result of the majority of CQC inspections of GPs resulting in a ‘Good’ or ‘Outstanding’ rating. However, some GPPS questions were found to have value in identifying practices at higher risk of an ‘Inadequate’ or ‘Requires Improvement’ rating, and this may be valuable for surveillance purposes. For example, the CQC could use key questions from the survey to target inspection planning. |
format | Online Article Text |
id | pubmed-7692819 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-76928192020-12-09 How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK Tallett, Amy Poots, Alan J Graham, Chris Peters, Michele Corbett, Rory Sizmur, Steve Forder, Julien BMJ Open Health Services Research OBJECTIVES: The relationship between patient feedback in the General Practice Patient Survey (GPPS) and Care Quality Commission (CQC) inspections of practices was investigated to understand whether there is an association between patient views and regulator ratings of quality. The specific aims were to understand whether patients’ self-reported experiences of primary care can predict CQC inspection ratings of GP practices by: (i) Measuring the association between GPPS results and CQC inspection ratings of GP practices; (ii) Building a predictive model of GP practice quality ratings that use GPPS results; and (iii) Evaluating the predictive model for risk stratification. DESIGN: Retrospective analysis of routinely collected data using decision tree modelling. SETTING: Primary care: GP practices in England. PRIMARY AND SECONDARY OUTCOME MEASURES: GPPS scores and GP practice CQC inspection ratings during 2018. RESULTS: Most GP practices (72%, 974/1350) were rated as ‘Good’ overall by CQC. Simply assuming that all practices will be rated as ‘Good’ results in a correct prediction 72% of the time, and it was not possible to improve on this overall level of predictive accuracy using decision tree modelling (correct in 73% of cases). However, a set of GPPS questions were found to have value in identifying practices at elevated risk of a poor inspection rating. CONCLUSIONS: Although there were some associations between GPPS data and CQC inspection ratings, there were limitations to the use of GPPS data for predictive analysis. This is a likely result of the majority of CQC inspections of GPs resulting in a ‘Good’ or ‘Outstanding’ rating. However, some GPPS questions were found to have value in identifying practices at higher risk of an ‘Inadequate’ or ‘Requires Improvement’ rating, and this may be valuable for surveillance purposes. For example, the CQC could use key questions from the survey to target inspection planning. BMJ Publishing Group 2020-11-26 /pmc/articles/PMC7692819/ /pubmed/33243813 http://dx.doi.org/10.1136/bmjopen-2020-041709 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) 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, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Health Services Research Tallett, Amy Poots, Alan J Graham, Chris Peters, Michele Corbett, Rory Sizmur, Steve Forder, Julien How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title | How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title_full | How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title_fullStr | How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title_full_unstemmed | How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title_short | How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK |
title_sort | how can patient experience scores be used to predict quality inspection ratings? a retrospective cross-sectional study of national primary care datasets in the uk |
topic | Health Services Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692819/ https://www.ncbi.nlm.nih.gov/pubmed/33243813 http://dx.doi.org/10.1136/bmjopen-2020-041709 |
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