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

Kerry V Wood1, Daniel Frings1, Chris Flood1

  • 1Department of Psychology, London South Bank University, London, UK.

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|June 20, 2025
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) appointment reminders show promise for reducing missed outpatient appointments. However, patient and staff engagement is key, with privacy concerns and integration challenges needing to be addressed for successful AI adoption in healthcare.

Keywords:
Appointment managementacceptabilityartificial intelligencedigital healthtechnology adoption

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Patient Engagement Strategies

Background:

  • Managing outpatient appointments is a significant challenge, with missed appointments leading to substantial waste of healthcare capacity.
  • Automated appointment reminders driven by Artificial Intelligence (AI) machine learning present a potential solution to improve efficiency.
  • Successful implementation of AI reminders necessitates understanding and addressing both patient and staff engagement, as well as assessing the overall impact.

Purpose of the Study:

  • To investigate the acceptability of AI machine learning-driven appointment management systems among patients and healthcare staff.
  • To identify key barriers and facilitators influencing the adoption and effectiveness of AI in outpatient appointment management.
  • To gather insights for optimizing AI-driven healthcare solutions.

Main Methods:

  • Semi-structured interviews were conducted with a sample of seven staff members and twelve patients.
  • Thematic Analysis was employed to analyze interview data, involving coding, categorization, and refinement of themes through researcher discussion.
  • Limitations in sample size and generalizability due to practical constraints were acknowledged.

Main Results:

  • Five key themes emerged: for patients, ethical concerns, AI understanding, reminder efficacy, user satisfaction, and usability; for staff, AI understanding, hesitancy, barriers, drivers, technology experiences, operational aspects, and sustainability.
  • Identified barriers included privacy concerns, limited interactivity, fragmented system integration, and operational challenges.
  • Facilitators were perceived prediction accuracy and the usefulness of reminders, with patients valuing convenience and usability, while staff focused on operational benefits like reduced DNA rates.

Conclusions:

  • Patient and staff perceptions of AI in NHS appointment management reveal a need for improved integration, clarity, interactivity, and accessibility.
  • Despite trust in data security, privacy concerns and operational inefficiencies currently hinder widespread AI adoption.
  • Enhancing user experience through tailored strategies is crucial for successful AI implementation in healthcare settings.