Evidence map›Paper›PMID 32574181›Full record

ArticlePloS one2020

It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates.

Adi Berliner Senderey, Tamar Kornitzer, Gabriella Lawrence, Hilla Zysman, Yael Hallak, Dan Ariely, Ran Balicer

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06915480 (Reducing Appointment NoShow Through Targeted Pre-appointment Messaging), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% of its field
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

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.

NCT06915480 phase3recruitingnot on this mapstarted 2025, after this paper: background citation

Reducing Appointment NoShow Through Targeted Pre-appointment Messaging

TypeinterventionalSponsorHugo W. Moser Research Institute at Kennedy Krieger, Inc.Ran2025 to 2027Enrolled5,000ConditionsNeurodevelopmental DisordersArmsReminder message and patient navigation
3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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  15. Automated patient self-scheduling: case study.Journal of the American Medical Informatics Association : JAMIA · 2022
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  18. [Adherence to anti-VEGF treatment-Considerations and practical recommendations].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2021
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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

7 authors at 4 institutions in 2 countries.

Adi Berliner SendereyClalit Research Institute, Clalit Health Services, Tel Aviv, Israel.ORCID 0000-0002-6235-8177
Tamar KornitzerKayma Labs, kayma, Tel Aviv, Israel.ORCID 0000-0003-3866-3873
Gabriella LawrenceClalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
Hilla ZysmanKayma Labs, kayma, Tel Aviv, Israel.
Yael HallakFuqua School of Business, Duke University, Durham, North Carolina, United States of America.
Dan ArielyKayma Labs, kayma, Tel Aviv, Israel.
Ran BalicerClalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
Duke University · USBen-Gurion University of the Negev · ILHadassah Medical Center · ILTechnion – Israel Institute of Technology · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Reminder SystemsText MessagingFemaleHospitalsHumansMaleMiddle AgedPatient ComplianceQuality Assurance, Health Care

Identifiers

PMID32574181
PMCPMC7310733
OpenAlexW3037279738

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.