Literature DB >> 32574181

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

Adi Berliner Senderey1,2, Tamar Kornitzer3, Gabriella Lawrence1,4, Hilla Zysman3, Yael Hallak5, Dan Ariely3,5, Ran Balicer1,6.   

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.

Entities:  

Year:  2020        PMID: 32574181     DOI: 10.1371/journal.pone.0234817

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  5 in total

1.  Automated patient self-scheduling: case study.

Authors:  Elizabeth Woodcock; Aditi Sen; Jonathan Weiner
Journal:  J Am Med Inform Assoc       Date:  2022-08-16       Impact factor: 7.942

Review 2.  [Adherence to anti-VEGF treatment-Considerations and practical recommendations].

Authors:  Albrecht Lommatzsch; Nicole Eter; Christoph Ehlken; Ines Lanzl; Hakan Kaymak; Alexander K Schuster; Focke Ziemssen
Journal:  Ophthalmologe       Date:  2020-12-03       Impact factor: 1.059

3.  The success of behavioral economics in improving patient retention within an intensive primary care practice.

Authors:  Phillip Groden; Alexandra Capellini; Erica Levine; Ania Wajnberg; Maria Duenas; Sire Sow; Bernard Ortega; Nia Medder; Sandeep Kishore
Journal:  BMC Fam Pract       Date:  2021-12-22       Impact factor: 2.497

4.  Effects of Behavioral Economics-Based Messaging on Appointment Scheduling Through Patient Portals and Appointment Completion: Observational Study.

Authors:  Su-Ying Liang; Cheryl D Stults; Veena G Jones; Qiwen Huang; Jeremy Sutton; Guy Tennyson; Albert S Chan
Journal:  JMIR Hum Factors       Date:  2022-03-30

5.  Protocol for an automated, pragmatic, embedded, adaptive randomised controlled trial: behavioural economics-informed mobile phone-based reminder messages to improve clinic attendance in a Botswanan school-based vision screening programme.

Authors:  Luke N Allen; Bakgaki Ratshaa; David Macleod; Nigel Bolster; Matthew Burton; Min Kim; Andrew Bastawrous; Ari Ho-Foster; Hannah Chroston; Oathokwa Nkomazana
Journal:  Trials       Date:  2022-08-15       Impact factor: 2.728

  5 in total

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