Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 15, 2024
Date Accepted: Oct 13, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Determinants of Healthcare Technology Adoption: A Systematic Review and Meta-Analysis Using the Unified Theory of Acceptance and Use of Technology (UTAUT)
ABSTRACT
Background:
Healthcare technology adoption is key for improving patient care, enhancing operational efficiency and better health outcomes. Examining the determinants that influence the acceptance and use of healthcare technologies is crucial for developers, healthcare providers and policymakers. The Unified Theory of Acceptance and Use of Technology (UTAUT) offers a comprehensive framework to assess these determinants systematically.
Objective:
This systematic review and meta-analysis aim to identify and analyse the key factors influencing the adoption of healthcare technologies based on UTAUT framework. By synthesizing existing literature, the study seeks to provide valuable insights for stakeholders to implement effective and innovative solutions in healthcare domain.
Methods:
A search was conducted across databases including Medline and Embase, IEEE Xplore Science Direct, Scopus, CINAHL, Google Scholar, and Web of Science. Inclusion criteria covered studies applying the UTAUT model to healthcare technology adoption, published in English between 2014 and 2024. Exclusion criteria included non-quantitative studies, studies not focused on healthcare settings, and those lacking sufficient data for meta-analysis. Data were analysed using meta-analytic techniques to combine findings and calculate effect sizes for UTAUT constructs.
Results:
A total of 35 studies with 20,723 participants met the inclusion criteria, representing various healthcare technologies such as Electronic Health Records (EHRs), telemedicine platforms, and mobile health applications. The meta-analysis revealed that Performance Expectancy (PE) emerged as the most significant predictor of Usage Intention (UI) (β=.304; P<.01), while UI was the primary predictor of Usage Behavior (UB) (β=.199; P<.01). The study synthesized data from a total of 35 studies and other significant predictors included Effort Expectancy (EE) (β=.177; P<.01), Social Influence (SI) (β=.167; P<.01) and Facilitating Conditions (FC) (β=.105; P<.01). Variability was observed across different healthcare settings and geographical regions, indicating that contextual factors play a crucial role. Limitations include potential publication bias among included studies.
Conclusions:
This study delivers valuable insights for researchers, developers and healthcare providers aiming to enhance technology adoption within the healthcare industry. The findings highlight the importance of performance expectancy, effort expectancy, social influence, and facilitating conditions in driving healthcare technology adoption. These results can guide future interventions to improve the adoption of health technologies, finally enhancing patient care and efficiency. Clinical Trial: N/A
Citation
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