Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis.

Background: Health care technology adoption is key to improving patient care, enhancing operational efficiency, and ensuring better health outcomes. Examining the determinants that influence the acceptance and sustainable use of health care technologies is crucial for system developers, health care providers, and policymakers. The Unified Theory of Acceptance and Use of Technology (UTAUT) and task-technology fit (TTF) theoretical models offer a comprehensive framework to assess these determinants systematically, with UTAUT focusing on usage intentions (UI) and TTF emphasizing task-technology alignment for system usefulness, usability, and satisfaction. Objective: This systematic review and meta-analysis aimed to identify and analyze the key factors influencing the adoption of health care technologies based on an integrated UTAUT and TTF framework. By synthesizing existing literature, the study seeks to provide valuable insights for stakeholders to implement innovative and effective solutions in the health care domain. Methods: A search was conducted across a range of databases, including MEDLINE and Embase, IEEE Xplore, ScienceDirect, Scopus, CINAHL, Google Scholar, and Web of Science. Inclusion criteria covered studies applying either the UTAUT model, the TTF model, or both to health care technology adoption, published in English between 2012 and 2025. Exclusion criteria included nonquantitative studies, studies not focused on a health care setting, and those lacking sufficient data for meta-analysis. The reviewers collaborated to decide on the final papers for inclusion in the review through Covidence, the Cochrane Collaboration's platform for systematic reviews. Data collection involved extracting quantitative data (eg, sample sizes, reliabilities, and standardized path coefficients) analyzed using meta-analytic techniques with a random-effects model in R software (R Development Core Team) to combine findings and calculate effect sizes. Results: A total of 50 studies (35 UTAUT with 20,723 participants and 15 TTF with 4041 participants) met the inclusion criteria, representing various health care technologies, such as electronic health records, telemedicine platforms, and mobile health apps. The meta-analysis revealed that performance expectancy emerged as the most significant predictor of UI (β=.304; P<.001), while UI was the primary predictor of usage behavior (β=.199; P<.001). Other UTAUT predictors included effort expectancy (β=.177; P<.001), social influence (β=.167; P<.001), and facilitating conditions (β=.105; P<.001). For TTF, technology characteristics had the strongest effect on TTF (β=.445; P<.001), followed by TTF on UI (β=.271; P<.001) and task characteristics on TTF (β=.263; P<.001). Variability across settings and regions suggests contextual influences, with high heterogeneity (I²=81.90%-94.87%). Conclusions: This study provides valuable insights for enhancing health care technology adoption by integrating UTAUT and TTF, highlighting performance expectancy, effort expectancy, social influence, facilitating conditions, task characteristics, technology characteristics, and TTF as key drivers. The findings, assessing system usefulness, usability, and satisfaction, can guide interventions to improve adoption and health care delivery.