Technology Adoption in Healthcare – A Modified TAM Model & Empirical Analysis AI, ML and Automation in Healthcare

Abstract

Purpose - With the advent of the medical in new technology as a concept, becoming a panacea for most challenging issues is inevitable. The new Technologies envisioned as a convergence of Artificial Intelligence (AI), Machine Learning (ML) and Automation have the prospective to revolutionise healthcare practices & processes, making them more systematic and structured, accessible, and interactive. The current research explores the intention to adopt AI, ML and Automation in healthcare & provides an empirical analysis based on a conceptual model.
Design/methodology/approach – The action research has drawn on the experience of collaboration and integration of AI, ML and Automation & healthcare. Each term and concept of these new technologies and Healthcare has been built together and understanding the challenges and opportunities is the focus of this analysis. This study was conducted to observe the intention for adoption of AI, ML and Automation in Healthcare. PLS-SEM was used in the study to see the impact and effect of indented or exogenous and dependent Indigenous construct.
Findings—The survey results of 309 respondents show that facilitators, doctors, and healthcare staff are influenced by AI, ML, and Automation and intend to adopt them fully in services. A modified TAM model with major parameters & enablers was added to the study to decide and analyse the users' intention to adopt new technologies in healthcare. We elaborate on how immersive experiences, enable healthcare workers & professionals to provide better patient care across geographical boundaries.
Practical implications – Understanding of challenges and opportunities of new technologies & healthcare handshakes and collaboration which have implications for betterment in future and improvement the healthcare services including medical training and education can also benefit from AI, ML & automation offering healthcare practitioners realistic and dynamic simulations for skill enhancement.
Originality/value – This research will outline the impact of AI, ML and Automation enablers on adoption intention and outline the key implications, use cases and potential challenges to using AI, ML and Automation in the healthcare domain and will investigate how AI, ML and Automation will improve healthcare