Evidence map›Paper›PMID 31317241›Full record

ArticlePediatric radiology2019

Review of learning opportunity rates: correlation with radiologist assignment, patient type and exam priority.

Marla B K Sammer, Marcus D Sammer, Lane F Donnelly

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Article in Pediatric radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Marla B K SammerDepartment of Radiology, Texas Children's Hospital, 6107 Fannin St., Suite 470, Houston, TX, 77030, USA. mbsammer@texaschildrens.org.ORCID 0000-0002-0026-0326
Marcus D SammerDepartment of Radiology, Baylor College of Medicine, Houston, TX, USA.
Lane F DonnellyCenter for Pediatric and Maternal Value, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCommon cause analysis of learning opportunities identified in a peer collaborative improvement process can gauge the potential risk to patients and opportunities to improve.

objectiveTo study rates of learning opportunities based on radiologist assignment, patient type and exam priority at an academic children's hospital with 24/7 in-house attending coverage. MATERIALS AND

methodsActively submitted peer collaborative improvement learning opportunities from July 2, 2016, to July 31, 2018, were identified. Learning opportunity rates (number of learning opportunities divided by number of exams in each category) were calculated based on the following variables: radiologist assignment at the time of dictation (daytime weekday, daytime weekend and holiday, evening, and night) patient type (inpatient, outpatient or emergency center) and exam priority (stat, urgent or routine). A statistical analysis of rate differences was performed using a chi-square test. Pairwise comparisons were made with Bonferroni method adjusted P-values.

resultsThere were 1,370 learning opportunities submitted on 559,584 studies (overall rate: 0.25%). The differences in rates by assignment were statistically significant (P<0.0001), with the highest rates on exams dictated in the evenings (0.31%) and lowest on those on nights (0.19%). Weekend and holiday daytime (0.26%) and weekday daytime (0.24%) rates fell in between. There were significantly higher rates on inpatients (0.33%) than on outpatients (0.22%, P<0.0001) or emergency center patients (0.16%, P<0.0001). There were no significant differences based on exam priority (stat 0.24%, urgent 0.26% and routine 0.24%, P=0.55).

conclusionIn this study, the highest learning opportunity rates occurred on the evening rotation and inpatient studies, which could indicate an increased risk for patient harm and potential opportunities for improvement.

Indexed as

Clinical CompetenceHumansMaleQuality Assurance, Health CareRadiologistsRadiologyShift Work ScheduleDiagnostic errorFatigueFeedbackPediatric radiologyPeer learningQuality improvementSafety

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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.