Artificial intelligence machine learning-driven outpatient appointment management: A qualitative study on acceptability

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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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251321016
Citations
3
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.96
Controls
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article

Artificial intelligence machine learning-driven outpatient appointment management: A qualitative study on acceptability

Daniel Frings, Kerry Wood, Nicola Thomas, Chris Flood
3 citations
Digital Health
Artificial Intelligence in Healthcare and Education
1.96
article

Artificial intelligence machine learning-driven outpatient appointment management: A qualitative study on acceptability

Daniel Frings, Kerry Wood, Nicola Thomas, Chris Flood
article en
3 citations

Abstract

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.

Digital HealthVol. 11
London South Bank University (GB)
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
1.96
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