Determinants of AI-enabled customer experience across healthcare sectors: a decade in review
Document Type
Article
Publication Title
Cogent Business and Management
Abstract
The application of Artificial Intelligence (AI) has improved the customer experience (CX) in healthcare by enhancing personalization, productivity, and decision-making. Nevertheless, there is still no general knowledge on the key drivers of AI-enabled CX in healthcare as there is no unified definition of the key determinants. The aim of this review is to review the current literature in order to determine the key factors that affect patient satisfaction, trust and service quality in the context of AI based healthcare systems. Comprehensive literature review of published studies in the past decade (2013 to 2024) by applying search strategy and thematic categorization, data extraction, data synthesis and cluster analysis was conducted to examine the determinants of AI-enabled CX. The review identified Technology Acceptance, AI-driven personalization & service quality, trust, transparency & risk perception, psychological & emotional factors, organizational & regulatory, and AI deployment & performance in healthcare sectors as the main factors, which had 26 determinants that shaped AI-driven CX in healthcare. Additionally, it was found that AI bias, privacy concerns, ethical dilemmas, and regulatory constraints were the major barriers to AI adoption. The study highlights the need for more empirical studies exploring real-world AI interactions of patients and healthcare professionals. This review provides future directions to research to benefit healthcare providers, AI developers, and policymakers to enhance patient-centered AI-enabled services.
DOI
10.1080/23311975.2025.2590241
Publication Date
1-1-2025
Recommended Citation
K, Pramodh; Nayak, Smitha; Chavadi, Chandan A.; and P. Pai, Yogesh, "Determinants of AI-enabled customer experience across healthcare sectors: a decade in review" (2025). Open Access archive. 14387.
https://impressions.manipal.edu/open-access-archive/14387