Evidence map›Paper›PMID 35616007›Full record

ArticleJournal of applied clinical medical physics2022

The impact of COVID-19 on a high-volume incident learning system: A retrospective analysis.

Dustin J Jacqmin, Jennie S M Crosby

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2022. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Dustin J JacqminDepartment of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Jennie S M CrosbyDepartment of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Justine Yang Bruce · 1985 to 2026
$142.6M
NCI NIH HHS P30 CA014520NIH HHS Cancer Center Support Grant: P30 CA014520
6 · The paper itself

Abstract

purposeThe purpose of this work was to assess how the coronavirus disease 2019 (COVID-19) pandemic impacted our incident learning system data and communicate the impact of a major exogenous event on radiation oncology clinical practice.

methodsTrends in our electronic incident reporting system were analyzed to ascertain the impact of the COVID-19 pandemic, including any direct clinical changes. Incident reports submitted in the 18 months prior to the pandemic (September 14, 2018 to March 13, 2020) and reports submitted during the first 18 months of the pandemic (March 14, 2020 to September 13, 2021) were compared. The incident reports include several data elements that were evaluated for trends between the two time periods, and statistical analysis was performed to compare the proportions of reports.

resultsIn the 18 months prior to COVID-19, 192 reports were submitted per 1000 planning tasks (n = 832 total). In the first 18 months of the pandemic, 147 reports per 1000 planning tasks were submitted (n = 601 total), a decrease of 23.4%. Statistical analysis revealed that there were no significant changes among the data elements between the pre- and during COVID-19 time periods. An analysis of the free-text narratives in the reports found that phrases related to pretreatment imaging were common before COVID-19 but not during. Conversely, phrases related to intravenous contrast, consent for computed tomography, and adaptive radiotherapy became common during COVID-19.

conclusionsThe data elements captured by our incident learning system were stable after the onset of the COVID-19 pandemic, with no statistically significant findings after correction for multiple comparisons. A trend toward fewer reports submitted for low-risk issues was observed. The methods used in the work can be generalized to events with a large-scale impact on the clinic or to monitor an incident learning system to drive future improvement activities.

Indexed as

COVID-19HumansPandemicsRetrospective StudiesRisk ManagementTomography, X-Ray ComputedCOVID-19Incident learningRadiotherapy

Identifiers

PMID35616007
PMCPMC9278685

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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.