ArticleDigital health
Artificial intelligence machine learning-driven outpatient appointment management: A qualitative study on acceptability.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 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
- Evaluation of an AI-Based Constraint-Optimization Scheduler to Optimize On-Call Schedule Equity and Reduce Administrative Burden in a Pediatric Residency: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
- Barrier check study: why predictive machine learning struggles to reach the operating room.BMJ health & care informatics · 2026Article
- Exploring an AI-First Healthcare System.Bioengineering (Basel, Switzerland) · 2026Article
- Analysis of the anti-scalping mechanism of hospital appointment registration based on Bayesian theory.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Managing outpatient appointments is challenging, with missed appointments wasting capacity. Artificial Intelligence (AI) machine learning-driven automated reminders offer a solution, but their success relies on patient and staff engagement, highlighting the need for impact assessment. Objective: To investigate the acceptability of AI machine learning-driven appointment management for patients and staff, identifying barriers and facilitators. Methods: Semi-structured interviews with seven staff and twelve patients. Despite scheduling efforts and incentives, practical constraints limited the sample size and generalizability. Interviews were analysed separately using Thematic Analysis, with one researcher coding and categorizing data, followed by discussions to refine themes and validate quotes. Results: Five themes emerged. Patients: ethical concerns, AI understanding, reminder efficacy, user satisfaction, and usability. Staff: AI understanding and hesitancy, barriers and drivers, technology experiences, appointment management, and sustainability. Barriers included privacy concerns, limited interactivity, fragmented integration, and operational challenges. Facilitators were perceived prediction accuracy and reminder usefulness. Patients valued usability, convenience, and reminders but sought better interactivity and integration. Staff emphasized ethics, operations, and sustainability, with motivation linked to reduced DNAs. Both valued accuracy and reliability, highlighting the need for tailored strategies. Conclusions: This study explores patient and staff perceptions of AI in NHS appointment management. Despite high trust in data security, privacy concerns, inefficiencies, and limited interactivity hinder adoption. Accuracy and convenience drive engagement. Findings highlight the need for better integration, clarity, interactivity, and accessibility to enhance user experience and AI adoption in healthcare.
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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.