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Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12177252 | PMC |
| http://dx.doi.org/10.1177/20552076251321016 | DOI Listing |
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