Algorithm Management Optimization: Intelligent Series Algorithm Management in Healthcare: Optimization, Security and Scalable Solutions takes a forward-looking approach to the deployment, management, and optimisation of series algorithms within modern healthcare systems.







The book addresses the critical need for efficient, secure, and scalable algorithmic solutions that enhance decision-making, improve operational efficiency, and support real-time healthcare delivery.







Structured across ten chapters, the book explores the design, implementation, and governance of intelligent algorithmic systems in healthcare. It begins by introducing adaptive algorithm frameworks for hospital management, focusing on patient flow optimisation, workforce planning, and predictive modelling using real-time data and machine learning. Subsequent chapters examine cost-effective resource allocation strategies enabled by AI, cloud computing, automation, and parallel processing, highlighting improvements in scheduling efficiency and reductions in operational costs.







The book further explores performance benchmarking in community medicine using key metrics such as accuracy, latency, throughput, and scalability, as well as automated monitoring systems for continuous optimisation. It also addresses the role of advanced intelligent systems in public health governance, including epidemic prediction, genomic analytics, and healthcare resource management. Additional chapters focus on blockchain-based security for electronic health records, algorithm lifecycle management, risk mitigation, and ethical considerations in medical AI deployment.







Key Features







· Comprehensive framework for intelligent series algorithm management in hospital management and patient flow optimisation.







· AI- and ML-based and cloud-based strategies for cost-effective resource allocation with performance-based benchmarking using real-time metrics and monitoring systems.







· Blockchain-enabled security solutions for data integrity in electronic health records.







· Scalable distributed architectures for public health and emergency response systems







· Integration of interdisciplinary team structures for algorithm deployment and oversight







Readership







Researchers, academics, students and professionals in artificial intelligence, machine learning, healthcare informatics, and operations management.