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A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology
BACKGROUND: Hospital readmissions are associated with higher resource utilization and worse patient outcomes. Causes of unplanned readmission to the hospital are multiple with some being better targets for intervention than others. To understand risk factors for surgical readmission and their increm...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348767/ https://www.ncbi.nlm.nih.gov/pubmed/28288681 http://dx.doi.org/10.1186/s12913-017-2134-2 |
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author | Copeland, Laurel A. Graham, Laura A. Richman, Joshua S. Rosen, Amy K. Mull, Hillary J. Burns, Edith A. Whittle, Jeff Itani, Kamal M. F. Hawn, Mary T. |
author_facet | Copeland, Laurel A. Graham, Laura A. Richman, Joshua S. Rosen, Amy K. Mull, Hillary J. Burns, Edith A. Whittle, Jeff Itani, Kamal M. F. Hawn, Mary T. |
author_sort | Copeland, Laurel A. |
collection | PubMed |
description | BACKGROUND: Hospital readmissions are associated with higher resource utilization and worse patient outcomes. Causes of unplanned readmission to the hospital are multiple with some being better targets for intervention than others. To understand risk factors for surgical readmission and their incremental contribution to current Veterans Health Administration (VA) surgical quality assessment, the study, Improving Surgical Quality: Readmission (ISQ-R), is being conducted to develop a readmission risk prediction tool, explore predisposing and enabling factors, and identify and rank reasons for readmission in terms of salience and mutability. METHODS: Harnessing the rich VA enterprise data, predictive readmission models are being developed in data from patients who underwent surgical procedures within the VA 2007–2012. Prospective assessment of psychosocial determinants of readmission including patient self-efficacy, cognitive, affective and caregiver status are being obtained from a cohort having colorectal, thoracic or vascular procedures at four VA hospitals in 2015–2017. Using these two data sources, ISQ-R will develop readmission categories and validate the readmission risk prediction model. A modified Delphi process will convene surgeons, non-surgeon clinicians and quality improvement nurses to rank proposed readmission categories vis-à-vis potential preventability. DISCUSSION: ISQ-R will identify promising avenues for interventions to facilitate improvements in surgical quality, informing specifications for surgical workflow managers seeking to improve care and reduce cost. ISQ-R will work with Veterans Affairs Surgical Quality Improvement Program (VASQIP) to recommend potential new elements VASQIP might collect to monitor surgical complications and readmissions which might be preventable and ultimately improve surgical care. |
format | Online Article Text |
id | pubmed-5348767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53487672017-03-14 A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology Copeland, Laurel A. Graham, Laura A. Richman, Joshua S. Rosen, Amy K. Mull, Hillary J. Burns, Edith A. Whittle, Jeff Itani, Kamal M. F. Hawn, Mary T. BMC Health Serv Res Study Protocol BACKGROUND: Hospital readmissions are associated with higher resource utilization and worse patient outcomes. Causes of unplanned readmission to the hospital are multiple with some being better targets for intervention than others. To understand risk factors for surgical readmission and their incremental contribution to current Veterans Health Administration (VA) surgical quality assessment, the study, Improving Surgical Quality: Readmission (ISQ-R), is being conducted to develop a readmission risk prediction tool, explore predisposing and enabling factors, and identify and rank reasons for readmission in terms of salience and mutability. METHODS: Harnessing the rich VA enterprise data, predictive readmission models are being developed in data from patients who underwent surgical procedures within the VA 2007–2012. Prospective assessment of psychosocial determinants of readmission including patient self-efficacy, cognitive, affective and caregiver status are being obtained from a cohort having colorectal, thoracic or vascular procedures at four VA hospitals in 2015–2017. Using these two data sources, ISQ-R will develop readmission categories and validate the readmission risk prediction model. A modified Delphi process will convene surgeons, non-surgeon clinicians and quality improvement nurses to rank proposed readmission categories vis-à-vis potential preventability. DISCUSSION: ISQ-R will identify promising avenues for interventions to facilitate improvements in surgical quality, informing specifications for surgical workflow managers seeking to improve care and reduce cost. ISQ-R will work with Veterans Affairs Surgical Quality Improvement Program (VASQIP) to recommend potential new elements VASQIP might collect to monitor surgical complications and readmissions which might be preventable and ultimately improve surgical care. BioMed Central 2017-03-14 /pmc/articles/PMC5348767/ /pubmed/28288681 http://dx.doi.org/10.1186/s12913-017-2134-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Study Protocol Copeland, Laurel A. Graham, Laura A. Richman, Joshua S. Rosen, Amy K. Mull, Hillary J. Burns, Edith A. Whittle, Jeff Itani, Kamal M. F. Hawn, Mary T. A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title | A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title_full | A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title_fullStr | A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title_full_unstemmed | A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title_short | A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology |
title_sort | study to reduce readmissions after surgery in the veterans health administration: design and methodology |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348767/ https://www.ncbi.nlm.nih.gov/pubmed/28288681 http://dx.doi.org/10.1186/s12913-017-2134-2 |
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