How Advanced Practice Nurses Can Be Better Managed in Hospitals: A Multi-Case Study

The labor markets for care professionals are a perennial topic of discussion. Advanced practice nurses (APN), as an advanced role in nursing, arose to solve the shortage of primary health care professionals. Prior research has advanced several areas of exploration for APNs' training or employing methods in Chinese hospitals. However, this leaves a key imperative unexplored: the management strategy of APNs in hospitals. The present study seeks to explore the management strategy of APNs in Chinese hospitals. The resource orchestration theory served as the guide as the multi-case study method investigated 18 case hospitals, gathered information from a variety of case data sources, and summarized the management strategies for hospitals' advanced practice nurses. Four types of APN management strategies-expert customized type, hierarchical linkage type, multidisciplinary benefit type, and professional penetration type-have been identified through resource orchestration. Hospitals can utilize the APN management strategy model as a guide to manage APNs in accordance with the unique characteristics of APN resources.

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.