Abstract
Introduction: Managing outpatient appointments is challenging, with missed appointments wasting capacity. Artificial Intelligence (AI) machine learning-driven automated reminders offer an efficient solution, but their success relies on patient and staff engagement, highlighting the need to assess their impact on user experiences.
Objective: To investigate the acceptability of AI machine learning driven appointment management for patients and staff and identifying potential barriers and facilitators.
Methods: Semi-structured interviews were conducted with seven staff and twelve patients, based on availability and willingness to participate. Despite efforts to accommodate schedules and incentivize participation, the sample size reflects practical constraints, potentially limiting generalizability. Interviews were
separately analysed using Thematic Analysis, with one researcher coding and categorizing data followed by discussions to refine themes and validate quotes.
Results: Five main themes emerged. For patients: ethical concerns, AI understanding, reminder efficacy, user satisfaction, and technology usability and reliability. For staff: AI understanding and hesitancy, barriers and drivers, technology experiences, appointment management, and sustainability. Barriers included privacy concerns, limited interactivity, fragmented platform integration, and operational challenges for staff. Facilitators were the perceived accuracy of
predictions and reminder usefulness. Patients valued usability, convenience, and effective reminders but sought better interactivity and integration. Staff emphasized ethical concerns, operational issues, and sustainability, with motivation linked to reduced DNA rates. Both groups valued accuracy and reliability, highlighting the need for tailored strategies to address their
priorities.
Conclusions: This study contributes to understanding patient and staff perceptions of AI in NHS outpatient appointment management. While trust in data security is high, privacy concerns, operational inefficiencies, and limited interactivity hinder adoption. However, benefits like accuracy and convenience drive engagement. The findings urge NHS policymakers and developers to improve integration, clarity, interactivity, and accessibility for better user experiences and wider adoption of AI healthcare technologies.
Objective: To investigate the acceptability of AI machine learning driven appointment management for patients and staff and identifying potential barriers and facilitators.
Methods: Semi-structured interviews were conducted with seven staff and twelve patients, based on availability and willingness to participate. Despite efforts to accommodate schedules and incentivize participation, the sample size reflects practical constraints, potentially limiting generalizability. Interviews were
separately analysed using Thematic Analysis, with one researcher coding and categorizing data followed by discussions to refine themes and validate quotes.
Results: Five main themes emerged. For patients: ethical concerns, AI understanding, reminder efficacy, user satisfaction, and technology usability and reliability. For staff: AI understanding and hesitancy, barriers and drivers, technology experiences, appointment management, and sustainability. Barriers included privacy concerns, limited interactivity, fragmented platform integration, and operational challenges for staff. Facilitators were the perceived accuracy of
predictions and reminder usefulness. Patients valued usability, convenience, and effective reminders but sought better interactivity and integration. Staff emphasized ethical concerns, operational issues, and sustainability, with motivation linked to reduced DNA rates. Both groups valued accuracy and reliability, highlighting the need for tailored strategies to address their
priorities.
Conclusions: This study contributes to understanding patient and staff perceptions of AI in NHS outpatient appointment management. While trust in data security is high, privacy concerns, operational inefficiencies, and limited interactivity hinder adoption. However, benefits like accuracy and convenience drive engagement. The findings urge NHS policymakers and developers to improve integration, clarity, interactivity, and accessibility for better user experiences and wider adoption of AI healthcare technologies.
| Original language | English |
|---|---|
| Journal | Digital Health |
| Publication status | Accepted/In press - 1 Feb 2025 |