Evidence map›Paper›PMID 35419132›Full record

ArticleThe Neurohospitalist2022

A Multi-Component Transition of Care Improvement Project to Reduce Hospital Readmissions Following Ischemic Stroke.

Ann M Leonhardt-Caprio, Craig R Sellers, Elizabeth Palermo, Thomas V Caprio, Robert G Holloway

Abstract read
In one paragraph

Article in The Neurohospitalist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

5 authors.

Ann M Leonhardt-CaprioUniversity of Rochester Medical Center, Rochester, NY, USA.ORCID https://orcid.org/0000-0001-8170-8577
Craig R SellersUniversity of Rochester Medical Center, Rochester, NY, USA.
Elizabeth PalermoUniversity of Rochester Medical Center, Rochester, NY, USA.
Thomas V CaprioUniversity of Rochester School of Nursing, Rochester, NY, USA.
Robert G HollowayUniversity of Rochester School of Medicine and Dentistry, Rochester, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ischemic stroke (IS) is a common cause of hospitalization which carries a significant economic burden and leads to high rates of death and disability. Readmission in the first 30 days after hospitalization is associated with increased healthcare costs and higher risk of death and disability. Efforts to decrease the number of patients returning to the hospital after IS may improve quality and cost of care. Methods: Improving care transitions to reduce readmissions is amenable to quality improvement (QI) initiatives. A multi-component QI intervention directed at IS patients being discharged to home from a stroke unit at an academic comprehensive stroke center using IS diagnosis-driven home care referrals, improved post-discharge telephone calls, and timely completion of discharge summaries was developed. The improvement project was implemented on July 1, 2019, and evaluated for the 6 months following initiation in comparison to the same 6-month period pre-intervention in 2018. Results: Following implementation, a statistically significant decrease in 30-day all-cause same-hospital readmission rates from 7.4% to 2.8% ( Conclusions: An evidence-based bundled process improvement intervention for IS patients discharged to home was associated with decreased hospital readmission rates following IS.

Indexed as

care transitionsischemic strokereadmissionreadmission reductionstroketransition of care

Identifiers

PMID35419132
PMCPMC8995625

What OpenQuestion holds

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Registered trials

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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.