Abstract Purpose: The purpose of this article was to analyze impact of edge computing on IoT device latency and data processing efficiency in healthcare systems in United States. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Edge computing has improved IoT device latency and data processing efficiency in U.S. healthcare systems by processing data locally, reducing transmission time and enabling faster decision-making. This results in up to a 50% reduction in latency, enhancing real-time monitoring and enabling quicker interventions. It also optimizes bandwidth and reduces server load, leading to more efficient patient monitoring and faster diagnoses, ultimately improving healthcare outcomes. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), diffusion of innovations theory & systems theory may be used to anchor future studies on the impact of edge computing on IoT device latency and data processing efficiency in healthcare systems in United States. Healthcare providers in remote or low-resource settings should prioritize adopting edge computing technology to overcome latency issues associated with cloud-based systems. Governments and healthcare regulators should create policies that incentivize the adoption of edge computing technologies in healthcare systems.
Ian Clark (Wed,) studied this question.