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Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study

OBJECTIVES: To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system. DESIGN: Observational study. SETTING: 21 hospitals operated by Kai...

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Autores principales: Marafino, Ben J, Escobar, Gabriel J, Baiocchi, Michael T, Liu, Vincent X, Plimier, Colleen C, Schuler, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356037/
https://www.ncbi.nlm.nih.gov/pubmed/34380667
http://dx.doi.org/10.1136/bmj.n1747
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author Marafino, Ben J
Escobar, Gabriel J
Baiocchi, Michael T
Liu, Vincent X
Plimier, Colleen C
Schuler, Alejandro
author_facet Marafino, Ben J
Escobar, Gabriel J
Baiocchi, Michael T
Liu, Vincent X
Plimier, Colleen C
Schuler, Alejandro
author_sort Marafino, Ben J
collection PubMed
description OBJECTIVES: To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system. DESIGN: Observational study. SETTING: 21 hospitals operated by Kaiser Permanente Northern California. PARTICIPANTS: 1 539 285 eligible index hospital admissions corresponding to 739 040 unique patients from June 2010 to December 2018. 411 507 patients were discharged post-implementation of the Transitions Program; 80 424 (19.5%) of these patients were at medium or high predicted risk and were assigned to receive the intervention after discharge. INTERVENTION: Patients admitted to hospital were automatically assigned to be followed by the Transitions Program in the 30 days post-discharge if their predicted risk of 30 day readmission or mortality was greater than 25% on the basis of electronic health record data. MAIN OUTCOME MEASURES: Non-elective hospital readmissions and all cause mortality in the 30 days after hospital discharge. RESULTS: Difference-in-differences estimates indicated that the intervention was associated with significantly reduced odds of 30 day non-elective readmission (adjusted odds ratio 0.91, 95% confidence interval 0.89 to 0.93; absolute risk reduction 95% confidence interval −2.5%, −3.1% to −2.0%) but not with the odds of 30 day post-discharge mortality (1.00, 0.95 to 1.04). Based on the regression discontinuity estimate, the association with readmission was of similar magnitude (absolute risk reduction −2.7%, −3.2% to −2.2%) among patients at medium risk near the risk threshold used for enrollment. However, the regression discontinuity estimate of the association with post-discharge mortality (−0.7% −1.4% to −0.0%) was significant and suggested benefit in this subgroup of patients. CONCLUSIONS: In an integrated health system, the implementation of a comprehensive readmissions prevention intervention was associated with a reduction in 30 day readmission rates. Moreover, there was no association with 30 day post-discharge mortality, except among medium risk patients, where some evidence for benefit was found. Altogether, the study provides evidence to suggest the effectiveness of readmission prevention interventions in community settings, but further research might be required to confirm the findings beyond this setting.
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spelling pubmed-83560372021-08-24 Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study Marafino, Ben J Escobar, Gabriel J Baiocchi, Michael T Liu, Vincent X Plimier, Colleen C Schuler, Alejandro BMJ Research OBJECTIVES: To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system. DESIGN: Observational study. SETTING: 21 hospitals operated by Kaiser Permanente Northern California. PARTICIPANTS: 1 539 285 eligible index hospital admissions corresponding to 739 040 unique patients from June 2010 to December 2018. 411 507 patients were discharged post-implementation of the Transitions Program; 80 424 (19.5%) of these patients were at medium or high predicted risk and were assigned to receive the intervention after discharge. INTERVENTION: Patients admitted to hospital were automatically assigned to be followed by the Transitions Program in the 30 days post-discharge if their predicted risk of 30 day readmission or mortality was greater than 25% on the basis of electronic health record data. MAIN OUTCOME MEASURES: Non-elective hospital readmissions and all cause mortality in the 30 days after hospital discharge. RESULTS: Difference-in-differences estimates indicated that the intervention was associated with significantly reduced odds of 30 day non-elective readmission (adjusted odds ratio 0.91, 95% confidence interval 0.89 to 0.93; absolute risk reduction 95% confidence interval −2.5%, −3.1% to −2.0%) but not with the odds of 30 day post-discharge mortality (1.00, 0.95 to 1.04). Based on the regression discontinuity estimate, the association with readmission was of similar magnitude (absolute risk reduction −2.7%, −3.2% to −2.2%) among patients at medium risk near the risk threshold used for enrollment. However, the regression discontinuity estimate of the association with post-discharge mortality (−0.7% −1.4% to −0.0%) was significant and suggested benefit in this subgroup of patients. CONCLUSIONS: In an integrated health system, the implementation of a comprehensive readmissions prevention intervention was associated with a reduction in 30 day readmission rates. Moreover, there was no association with 30 day post-discharge mortality, except among medium risk patients, where some evidence for benefit was found. Altogether, the study provides evidence to suggest the effectiveness of readmission prevention interventions in community settings, but further research might be required to confirm the findings beyond this setting. BMJ Publishing Group Ltd. 2021-08-11 /pmc/articles/PMC8356037/ /pubmed/34380667 http://dx.doi.org/10.1136/bmj.n1747 Text en © Author(s) (or their employer(s)) 2019. 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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Research
Marafino, Ben J
Escobar, Gabriel J
Baiocchi, Michael T
Liu, Vincent X
Plimier, Colleen C
Schuler, Alejandro
Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title_full Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title_fullStr Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title_full_unstemmed Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title_short Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
title_sort evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356037/
https://www.ncbi.nlm.nih.gov/pubmed/34380667
http://dx.doi.org/10.1136/bmj.n1747
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