Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Tallett, Amy, Poots, Alan J, Graham, Chris, Peters, Michele, Corbett, Rory, Sizmur, Steve, Forder, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
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
_version_ 1783614600978104320
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
work_keys_str_mv AT tallettamy howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT pootsalanj howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT grahamchris howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT petersmichele howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT corbettrory howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT sizmursteve howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk
AT forderjulien howcanpatientexperiencescoresbeusedtopredictqualityinspectionratingsaretrospectivecrosssectionalstudyofnationalprimarycaredatasetsintheuk