Authors:
Md. Asif Hasan, Md. Tanvir Rahman Mazumder, Md. Caleb Motari, Md. Shahadat Hossain Shourov, Mrinmoy Sarkar, Tamanna Anjum Toma
Addresses:
School of Business, Montclair State University, Montclair, New Jersey, United States of America. School of Information Technology, Washington University of Science and Technology, Alexandria, Virginia, United States of America. Department of Information Technology Management, Webster University, Webster Groves, Missouri, United States of America. Department of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia.
Abstract:
AI chatbots are being used in healthcare more often to make services better, cut down on administrative work, and boost patient involvement. This study aimed to determine if they help to decrease the number of people who don’t attend appointments and increase patient satisfaction in U.S. healthcare services. The survey of 400 adults relied on a closed-ended questionnaire based on a cross-sectional design. Statistical analysis methods included chi-square tests, the Mann–Whitney U test, the Kruskal–Wallis H test, independent t-tests, and multiple linear regression. Results showed that chatbots did not significantly help reduce missed appointments (p = .985), although satisfaction, ease of use, and clear responses all had a strong impact on the overall patient experience (R² = 0.47). It was also found that response clarity is associated with having a provider connection (p = 0.003), and people who were recommended a chatbot showed higher satisfaction (p = 0.040). The findings suggest that AI chatbots can increase patient satisfaction by being usable and easy to communicate with, rather than just reminding them to follow their treatment plan. If integrated into healthcare with respect for cultural diversity, proper supervision, and ongoing evaluation, they can help improve patient-centred care in primary care.
Keywords: AI Chatbot; No-Show Appointments; Patient Satisfaction; Digital Health; U.S. Healthcare; Primary Care; Specialised Care; Regression Analysis; Chatbot Usability; Healthcare Technology.
Received: 18/06/2024, Revised: 27/09/2024, Accepted: 03/11/2024, Published: 03/03/2025
DOI: 10.64091/ATIHL.2025.000116
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 1 , Pages: 1-15