Investigating the impact of adoption of smart healthcare technologies on quality of patient care using two-staged SEM-ANN approach

Abstract

This study investigates the impact of the adoption of smart healthcare technologies (ASHT) on the quality of patient care (QoPC) in India by employing a two-staged analytical framework integrating structural equation modeling (SEM) and artificial neural networks (ANN). The research further examines the mediating role of technological literacy and training (TLT) and the moderating effects of organizational support and resources (OSR) and staff engagement (SE). Data were collected from 408 healthcare professionals across diverse healthcare settings in India through a structured online questionnaire. The SEM results reveal a significant positive relationship between ASHT and QoPC, with TLT emerging as a key mediator in this relationship. Additionally, OSR and SE significantly moderate the impact of ASHT on QoPC, underscoring the importance of institutional infrastructure and workforce motivation in maximizing technology benefits. The ANN analysis supplements the SEM findings by capturing non-linear relationships and highlighting the relative importance of each predictor, with SE and OSR ranking highest in predictive relevance. The findings offer theoretical and practical implications for enhancing digital healthcare transformation, particularly in developing countries. The study recommends focused investments in staff training, organizational readiness, and employee engagement to optimize the impact of smart healthcare technologies on patient outcomes.