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It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates
Failure to attend hospital appointments has a detrimental impact on care quality. Documented efforts to address this challenge have only modestly decreased no-show rates. Behavioral economics theory has suggested that more effective messages may lead to increased responsiveness. In complex, real-wor...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310733/ https://www.ncbi.nlm.nih.gov/pubmed/32574181 http://dx.doi.org/10.1371/journal.pone.0234817 |
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author | Berliner Senderey, Adi Kornitzer, Tamar Lawrence, Gabriella Zysman, Hilla Hallak, Yael Ariely, Dan Balicer, Ran |
author_facet | Berliner Senderey, Adi Kornitzer, Tamar Lawrence, Gabriella Zysman, Hilla Hallak, Yael Ariely, Dan Balicer, Ran |
author_sort | Berliner Senderey, Adi |
collection | PubMed |
description | Failure to attend hospital appointments has a detrimental impact on care quality. Documented efforts to address this challenge have only modestly decreased no-show rates. Behavioral economics theory has suggested that more effective messages may lead to increased responsiveness. In complex, real-world settings, it has proven difficult to predict the optimal message composition. In this study, we aimed to systematically compare the effects of several pre-appointment message formats on no-show rates. We randomly assigned members from Clalit Health Services (CHS), the largest payer-provider healthcare organization in Israel, who had scheduled outpatient clinic appointments in 14 CHS hospitals, to one of nine groups. Each individual received a pre-appointment SMS text reminder five days before the appointment, which differed by group. No-show and advanced cancellation rates were compared between the eight alternative messages, with the previously used generic message serving as the control. There were 161,587 CHS members who received pre-appointment reminder messages who were included in this study. Five message frames significantly differed from the control group. Members who received a reminder designed to evoke emotional guilt had a no-show rates of 14.2%, compared with 21.1% in the control group (odds ratio [OR]: 0.69, 95% confidence interval [CI]: 0.67, 0.76), and an advanced cancellation rate of 26.3% compared with 17.2% in the control group (OR: 1.2, 95% CI: 1.19, 1.21). Four additional reminder formats demonstrated significantly improved impact on no-show rates, compared to the control, though not as effective as the best performing message format. Carefully selecting the narrative of pre-appointment SMS reminders can lead to a marked decrease in no-show rates. The process of a/b testing, selecting, and adopting optimal messages is a practical example of implementing the learning healthcare system paradigm, which could prevent up to one-third of the 352,000 annually unattended appointments in Israel. |
format | Online Article Text |
id | pubmed-7310733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73107332020-06-26 It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates Berliner Senderey, Adi Kornitzer, Tamar Lawrence, Gabriella Zysman, Hilla Hallak, Yael Ariely, Dan Balicer, Ran PLoS One Research Article Failure to attend hospital appointments has a detrimental impact on care quality. Documented efforts to address this challenge have only modestly decreased no-show rates. Behavioral economics theory has suggested that more effective messages may lead to increased responsiveness. In complex, real-world settings, it has proven difficult to predict the optimal message composition. In this study, we aimed to systematically compare the effects of several pre-appointment message formats on no-show rates. We randomly assigned members from Clalit Health Services (CHS), the largest payer-provider healthcare organization in Israel, who had scheduled outpatient clinic appointments in 14 CHS hospitals, to one of nine groups. Each individual received a pre-appointment SMS text reminder five days before the appointment, which differed by group. No-show and advanced cancellation rates were compared between the eight alternative messages, with the previously used generic message serving as the control. There were 161,587 CHS members who received pre-appointment reminder messages who were included in this study. Five message frames significantly differed from the control group. Members who received a reminder designed to evoke emotional guilt had a no-show rates of 14.2%, compared with 21.1% in the control group (odds ratio [OR]: 0.69, 95% confidence interval [CI]: 0.67, 0.76), and an advanced cancellation rate of 26.3% compared with 17.2% in the control group (OR: 1.2, 95% CI: 1.19, 1.21). Four additional reminder formats demonstrated significantly improved impact on no-show rates, compared to the control, though not as effective as the best performing message format. Carefully selecting the narrative of pre-appointment SMS reminders can lead to a marked decrease in no-show rates. The process of a/b testing, selecting, and adopting optimal messages is a practical example of implementing the learning healthcare system paradigm, which could prevent up to one-third of the 352,000 annually unattended appointments in Israel. Public Library of Science 2020-06-23 /pmc/articles/PMC7310733/ /pubmed/32574181 http://dx.doi.org/10.1371/journal.pone.0234817 Text en © 2020 Berliner Senderey et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Berliner Senderey, Adi Kornitzer, Tamar Lawrence, Gabriella Zysman, Hilla Hallak, Yael Ariely, Dan Balicer, Ran It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title | It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title_full | It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title_fullStr | It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title_full_unstemmed | It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title_short | It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates |
title_sort | it’s how you say it: systematic a/b testing of digital messaging cut hospital no-show rates |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310733/ https://www.ncbi.nlm.nih.gov/pubmed/32574181 http://dx.doi.org/10.1371/journal.pone.0234817 |
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