Evidence map›Paper›PMID 33094046›Full record

ArticleCureus2020

Emergency Department "Bounce-Back" Rates as a Function of Emergency Medicine Training Year.

Janine Curcio, Andrew Little, Chelsea Bolyard, Anand Gupta, Michelle Secic, Meenal Sharkey

Abstract read
In one paragraph

Article in Cureus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

6 authors.

Janine CurcioEmergency Medicine, OhioHealth Doctors Hospital, Columbus, USA.
Andrew LittleEmergency Medicine, OhioHealth Doctors Hospital, Columbus, USA.
Chelsea BolyardResearch, OhioHealth Research Institute, Columbus, USA.
Anand GuptaBiostatistics, OhioHealth Research Institute, Columbus, USA.
Michelle SecicBiostatistics, OhioHealth Research Institute, Columbus, USA.
Meenal SharkeyEmergency Medicine, OhioHealth Doctors Hospital, Columbus, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSince the 1990s, the emergency department (ED) unscheduled return visit (URV), or "bounce-back," has been used as a quality of care measurement. During that time, resident training was also scrutinized and uncovered a need for closer resident supervision, especially of second-year residents. Over the years, bounce-backs have continued to be analyzed with vigor, but research on residency training and supervision has lagged with few studies concurrently investigating residency supervision and bounce-backs. Other literature on resident supervision suggests that with adequate attending supervision, resident performance is equivalent to attending performance. With that in mind, it was hypothesized that resident bounce-back rates will be equivalent to attending bounce-back rates, and there will be no change among residency years. The primary objective of this study was to determine the rate at which patients are seen as a bounce-back visit within 72 hours of their initial visit to a community hospital ED during the study time frame. The secondary aims were to evaluate if the ED bounce-back rate is impacted by training level (residents or attending) and to describe bounce-back patient characteristics, including primary complaint/disease, age, comorbidities and issues with compliance.

methodsA retrospective chart review of 1000 charts was conducted from September 2015 to September 2017. Charts were randomly selected by the Quality & Patient Safety (QPS) team and, after applying inclusion/exclusion criteria, 732 charts were analysed. Inclusion criteria included age ≥ 18 years, patients treated by an Emergency Medicine (EM) resident during their initial visit and patients with a "discharge" disposition. Exclusion criteria included patients seen as a scheduled return visit (e.g., two-day return for blood pregnancy recheck, wound check, etc.). Demographics, initial visit variables, comorbidities and bounce-back data were collected based on electronic record query or chart review. Data was analysed using means, standard deviations, medians and ranges for continuous variables. Logistic regression modelling techniques were used to examine factors that affect whether the patient had a bounce-back visit.

resultsThe rate of URVs within 72 hours of the patient's initial visit was 4.65%. PGY1 and PGY2's bounce-back rate was 3.8% and 3.6%, respectively, and PGY3 and PGY4's bounce-back rate was 5.7% and 5.6%, respectively (p-value=.63). There was no statistically significant change among residency years. Most bounce-back characteristics analysed including primary complaint, age, and comorbidities demonstrated no statistical significance in the increased rate of bounce-back except for patients with a history of tobacco abuse, alcohol abuse and chronic pain. Current smokers were 6.5 times more likely to bounce back than former smokers (odds ratio=6.485, 95% confidence interval = 2.089 to 20.133, p-value=0.0012) and those with chronic pain were 2.5 times more likely to bounce back than those without chronic pain (odds ratio=2.518, 95% confidence interval =1.029 to 6.164, p=0.0431).

conclusionEM residency training year does not increase the frequency of bounce-backs in a community hospital ED. Finally, patients with substance abuse and chronic pain were more likely to bounce back.

Indexed as

bounce-backsemergency medicine trainingresidency training

Identifiers

PMID33094046
PMCPMC7571604

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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.