Cargando…

Potential of community-based risk estimates for improving hospital performance measures and discharge planning

BACKGROUND: Risk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factor...

Descripción completa

Detalles Bibliográficos
Autores principales: Reid, Michael, Kephart, George, Andreou, Pantelis, Robinson, Alysia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8094366/
https://www.ncbi.nlm.nih.gov/pubmed/33926991
http://dx.doi.org/10.1136/bmjoq-2020-001230
_version_ 1783687966992891904
author Reid, Michael
Kephart, George
Andreou, Pantelis
Robinson, Alysia
author_facet Reid, Michael
Kephart, George
Andreou, Pantelis
Robinson, Alysia
author_sort Reid, Michael
collection PubMed
description BACKGROUND: Risk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates. METHODS: Using hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence. RESULTS: Community of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management. CONCLUSION: Contextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.
format Online
Article
Text
id pubmed-8094366
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-80943662021-05-18 Potential of community-based risk estimates for improving hospital performance measures and discharge planning Reid, Michael Kephart, George Andreou, Pantelis Robinson, Alysia BMJ Open Qual Original Research BACKGROUND: Risk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates. METHODS: Using hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence. RESULTS: Community of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management. CONCLUSION: Contextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve. BMJ Publishing Group 2021-04-29 /pmc/articles/PMC8094366/ /pubmed/33926991 http://dx.doi.org/10.1136/bmjoq-2020-001230 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://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/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Reid, Michael
Kephart, George
Andreou, Pantelis
Robinson, Alysia
Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title_full Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title_fullStr Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title_full_unstemmed Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title_short Potential of community-based risk estimates for improving hospital performance measures and discharge planning
title_sort potential of community-based risk estimates for improving hospital performance measures and discharge planning
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8094366/
https://www.ncbi.nlm.nih.gov/pubmed/33926991
http://dx.doi.org/10.1136/bmjoq-2020-001230
work_keys_str_mv AT reidmichael potentialofcommunitybasedriskestimatesforimprovinghospitalperformancemeasuresanddischargeplanning
AT kephartgeorge potentialofcommunitybasedriskestimatesforimprovinghospitalperformancemeasuresanddischargeplanning
AT andreoupantelis potentialofcommunitybasedriskestimatesforimprovinghospitalperformancemeasuresanddischargeplanning
AT robinsonalysia potentialofcommunitybasedriskestimatesforimprovinghospitalperformancemeasuresanddischargeplanning