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

Publication ,  Journal Article
Berliner Senderey, A; Kornitzer, T; Lawrence, G; Zysman, H; Hallak, Y; Ariely, D; Balicer, R
Published in: PloS one
January 2020

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.

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Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2020

Volume

15

Issue

6

Start / End Page

e0234817

Related Subject Headings

  • Text Messaging
  • Reminder Systems
  • Quality Assurance, Health Care
  • Patient Compliance
  • Middle Aged
  • Male
  • Humans
  • Hospitals
  • General Science & Technology
  • Female
 

Citation

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Berliner Senderey, A., Kornitzer, T., Lawrence, G., Zysman, H., Hallak, Y., Ariely, D., & Balicer, R. (2020). It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates. PloS One, 15(6), e0234817. https://doi.org/10.1371/journal.pone.0234817
Berliner Senderey, Adi, Tamar Kornitzer, Gabriella Lawrence, Hilla Zysman, Yael Hallak, Dan Ariely, and Ran Balicer. “It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates.PloS One 15, no. 6 (January 2020): e0234817. https://doi.org/10.1371/journal.pone.0234817.
Berliner Senderey A, Kornitzer T, Lawrence G, Zysman H, Hallak Y, Ariely D, et al. It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates. PloS one. 2020 Jan;15(6):e0234817.
Berliner Senderey, Adi, et al. “It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates.PloS One, vol. 15, no. 6, Jan. 2020, p. e0234817. Epmc, doi:10.1371/journal.pone.0234817.
Berliner Senderey A, Kornitzer T, Lawrence G, Zysman H, Hallak Y, Ariely D, Balicer R. It's how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates. PloS one. 2020 Jan;15(6):e0234817.

Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2020

Volume

15

Issue

6

Start / End Page

e0234817

Related Subject Headings

  • Text Messaging
  • Reminder Systems
  • Quality Assurance, Health Care
  • Patient Compliance
  • Middle Aged
  • Male
  • Humans
  • Hospitals
  • General Science & Technology
  • Female