ArticleThe patient2024
Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment.
Article in The patient, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Patient and carer perceptions and experiences of technology-facilitated appointment reminders in healthcare: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Parent and Adolescent Preferences for Online Youth Mental Health Support Programs: A Discrete Choice Experiment.The patient · 2026Article
- Preferences for Personalized Text Message Appointment Reminders Among Outpatients in a Universal Health System: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Preferences for Infection Prevention in Hospital-Acquired Infections.JAMA network open · 2026Article
- Eliciting Patient Preferences for the Hospital at Home Programme in Singapore; A Discrete Choice Experiment.The patient · 2026Article
- Cancer survivor preferences for breast cancer follow-up care: a discrete choice experiment.Journal of cancer survivorship : research and practice · 2026Article
- Preferences for Neurodevelopmental Follow-Up Care for Children: A Discrete Choice Experiment.The patient · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundIt is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments.
objectivesThis study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system.
methodsA discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a 'neither' option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems.
resultsRespondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (β = 2.42, p < 0.001) less than 3 days before the appointment (β = 0.99, p < 0.001), with basic details including the appointment cost (β = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (β = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%.
conclusionsOur findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.
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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.