Enhancing Hospital Operations and Patient Care: The Role of AI in Smart Healthcare Systems

Abstract

This study explores the potential of hybrid computational architectures to solve complex scientific problems, underlining their capacity to improve performance, adaptability, and problem-solving capabilities. It answers five research questions: definition of characteristics of hybrid architectures, efficiency improvement, applications in various scientific domains, challenges of implementation, and mitigating strategies. Using a qualitative approach, this study examines case studies and expert interviews to identify important themes and patterns. Findings include hybrid architectures' adaptability and efficiency in diverse scientific domains, modular design as a solution to implementation challenges, and the importance of community-driven standards for integration. Scalability and domain-specific challenges remain despite these advancements. The research concludes with recommendations for further exploration of hybrid architectures to expand their application and efficacy in scientific research.