A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology

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A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology

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Title: A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology
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.

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Citation: Copeland, Laurel A., Laura A. Graham, Joshua S. Richman, Amy K. Rosen, Hillary J. Mull, Edith A. Burns, Jeff Whittle, Kamal M. F. Itani, and Mary T. Hawn. 2017. “A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology.” BMC Health Services Research 17 (1): 198. doi:10.1186/s12913-017-2134-2. http://dx.doi.org/10.1186/s12913-017-2134-2.
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Abstract: 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.
Published Version: doi:10.1186/s12913-017-2134-2
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348767/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:32071951
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