ArticleJournal of the Intensive Care Society2022
Animation supported communication on intensive care; a service improvement initiative.
Article in Journal of the Intensive Care Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Video clips for patient comprehension of atrial fibrillation and deep vein thrombosis in emergency care. A randomised clinical trial.NPJ digital medicine · 2024Article
- Strategies Used by Healthcare Systems to Communicate with Hospitalized Patients and Families with Limited English Proficiency During the COVID-19 Pandemic: A Narrative Review.Journal of immigrant and minority health · 2023Review
- Animation Supported Consent Before Elective Laparoscopic Cholecystectomy.World journal of surgery · 2022Article
- Content Quality, Reliability, and Factors Associated With Likes of Venous Thromboembolism-Related Short Videos on TikTok and Bilibili: A Cross-Platform Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: COVID-19 has created unique challenges for families of patients admitted to intensive care units. Restricted visiting, language barriers and time constraints have limited communication, resulting in a lack of understanding and anxiety. We introduced digital animations to support communication and assessed the impact on families of patients admitted to intensive care. Methods: Multi-language animations explaining mechanical ventilation, (www.explainmyprocedure.com/icu) were introduced at two London intensive care units during the COVID-19 pandemic. Web-links were sent by email. Reported understanding of the treatment, its benefits, risks and alternatives was assessed among family contacts of 71 consecutive patients admitted to intensive care; 39 before the animations were introduced (no animation group) and 32 afterwards (animation group). Reported understanding in the two groups was assessed by telephone questionnaire and compared. Results: Following introduction, all relatives reported they had watched the animation. The proportions who reported complete understanding of mechanical ventilation, its benefits, risks and alternatives, in the no animation group (n = 39) were, respectively, 15%, 28%, 0% and 3% and in the animation group (n = 32), 94%, 97%, 84% and 66% (p < 0.0001 for all comparisons). Conclusion: Family use of online multi-language animations explaining mechanical ventilation is feasible, acceptable and associated with substantial improvement in understanding. The approach is not limited to mechanical ventilation, or to use in a pandemic, and has the potential to be applied to a wide range of treatment and recovery pathways on intensive care.
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Registered trials
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.