Evidence map›Paper›PMID 28288681›Full record

ArticleBMC health services research2017

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

Laurel A Copeland, Laura A Graham, Joshua S Richman, Amy K Rosen, Hillary J Mull, Edith A Burns, Jeff Whittle, Kamal M F Itani, Mary T Hawn

Abstract read
In one paragraph

Article in BMC health services research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Factors associated with a 30-day unplanned readmission after elective spine surgery: a retrospective cohort study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2021
    Article
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  5. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Laurel A CopelandVeterans Affairs: VA Central Western Massachusetts Healthcare System, Leeds, MA, USA. LaurelACopeland@gmail.com.
Laura A GrahamVeterans Affairs, Birmingham VAMC, Birmingham, AL, USA.
Joshua S RichmanVeterans Affairs, Birmingham VAMC, Birmingham, AL, USA.
Amy K RosenVeterans Affairs, Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.
Hillary J MullVeterans Affairs, Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.
Edith A BurnsVeterans Affairs, Milwaukee VAMC, Milwaukee, WI, USA.
Jeff WhittleVeterans Affairs, Milwaukee VAMC, Milwaukee, WI, USA.
Kamal M F ItaniDepartment of Surgery, Boston University School of Medicine, Boston, MA, USA.
Mary T HawnVeterans Affairs, Palo Alto VAMC, Palo Alto, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital 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.

methodsHarnessing 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.

Indexed as

VeteransComorbidityHumansPatient DischargePatient ReadmissionProspective StudiesQuality ImprovementQuality Indicators, Health CareRetrospective StudiesRisk FactorsSocioeconomic FactorsUnited StatesUnited States Department of Veterans AffairsComorbidityMethodsOperativeQuality of careSurgical proceduresVeterans

Identifiers

PMID28288681
PMCPMC5348767

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.