E-Book, Englisch, 219 Seiten
Kumar / Rathore / Ahuja Algorithm Management Optimization: Intelligent Series Algorithm Management in Healthcare
1. Auflage 2026
ISBN: 979-8-89881-717-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Intelligent Series Algorithm Management in Healthcare
E-Book, Englisch, 219 Seiten
ISBN: 979-8-89881-717-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
A Framework for Adaptive Algorithm Series Management in Dynamic Environments: Applications in Hospital Management
Sweta Colvin1, *, Patil Ashish N.2, Patil Nitin S.1
Abstract
The process of managing a hospital is a complex task that requires planning and constant adjustments. One has to be fast in making decisions and, at the same time, manage numerous moving parts. Algorithms are one of the best-developed tools in hospital administration today. The adaptive algorithm plans are a holistic approach to the uncertainty factors of daily activities, which include variable patient numbers, resource allocation, and scheduling. This report presents a logical approach to implementing adaptive algorithms in the management of hospitals. It emphasizes the pros and cons of these tools in the following critical areas: patient flow, workforce planning, and advanced predictive modeling. According to the framework, it is essential to achieve optimal functions of a hospital by means of the balance between patient acuity and resources at their disposal, through the real-time repetition of the data and machine learning. It is seeking to simplify the operations without compromising the quality of care. Predictive modeling enables hospitals to forecast resource requirements by estimating the number of patients to be hospitalized, identifying peak periods, and planning manpower and equipment allocation. Such proactive quality helps in making decisions more quickly and based on facts, and allows allocation of time to value-added jobs. The barriers to implementation are that they are resistant to automation, have high computational costs, and raise data privacy concerns. These challenges need a multidisciplinary strategy that combines information technology, policy formulation, clinical practice, and other disciplines. The document shows that adaptive algorithms may change the concept of hospital management fundamentally, and it is necessary to conduct more research to streamline the tools and encourage their more commonplace use in healthcare systems.
* Corresponding author Sweta Colin: Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth “Deemed To Be University”, Taluka-Karad, Maharashtra, India;
E-mail: swetacolvinkvv@outlook.com
INTRODUCTION
Hospital patients constantly vary, with new and urgent medical emergencies arising, which demand the presence of certain members of staff on standby. It is this dynamic demand that makes hospital management the most difficult to operate. Conventional management systems are not always flexible and do not enable real-time adjustments, making them inefficient (Teece et al., 2016). One can overcome these difficulties with the help of head-and-tail proof or generalized models that adapt a responsive versus predictive logic in an ASA (series algorithm) model.
A tool is required in the healthcare services, a tool that can offer real-time calculations and assessment to enhance efficiency in the work. Most of the current systems used in hospital management are based on hard templates, hard-drawn schedules, and hard-drawn allocation of resources. These are not useful during the emergencies of last-minute and unplanned conditions and unexpected patient surges. A shortage of resources would cause over-crowded hospitals, long queues of patients, overburdened staff, and acute under-utilization.
The ASA structure is capable of ad-hoc changes, which can be made at the very end of the period to enable the optimization of the operations in the hospital, which demands better control. The essence of this advantage is real-time data analytics and predictive modeling. The model optimizes the management of patients, the flow of resources, staffing, and overall resources (Ali et al., 2022). Its algorithms constantly evaluate data gathered from various sources, including electronic health records, wearable patient monitors, and hospital IoT systems, allowing one to make decisions in advance.
One of the major advantages of ASA frameworks is that they can predict patient surges and use resources in the most efficient way. During flu seasons, pandemics, or significant accidents, hospitals are especially prone to getting a larger number of patients. Even traditional management systems cannot cope with these surges, leading to treatment delays and resource bottlenecks. In contrast to such systems, in ASA frameworks, machine learning models are utilized to predict trends on the premise of historical data, which gives hospitals room to make preparations ahead of time (Abdullah et al., 2023).
Adaptive algorithms can also enhance staff scheduling by dynamically reallocating staff based on real-time requirements. To give an example, an unexpected spike in the emergency room visits can be noticed by an ASA system, which is in turn able to make changes to shift protocols, automatically alert critical staff, reassign personnel, and instruct major staff to accelerate efficiency in patient care. This decreases healthcare workers’ burnout and enhances patient outcomes in terms of reduced mortality and increased staff retention in times of high staffing.
Also, crucially, ASA models could be integrated with hospital information systems, electronic health records, IoT devices, and the rest of the technologies to facilitate real-time and data-driven decision-making processes. The real-time data streams improve predictive abilities, and thus hospitals can predict admissions, occupancy of the beds, and timing of stocking of essential medical supplies.
In this chapter, the authors consider the usefulness of the adaptive series algorithm in hospital management to improve their operational efficiency. They introduce the fundamental concepts of the algorithms, then they formulate the structure and architecture of the framework and offer guidelines for implementation. The claims contained in the framework are supported by the evidence and the proof of concept, case studies, and observational data. Lastly, they mention some challenges of the ASA adoption in hospitals and suggest future research directions.
BACKGROUND AND RELATED WORK
Adaptive Algorithms in Dynamic Environments
Adaptive algorithms change their parameters and decisions in real-time and thus would be appropriate in dynamic environments such as hospital management (Jebreili & Goli, 2024). They are already employed in automated industrial processes, finance, and supply chain logistics with numerous applications of these algorithms. However, their potential in healthcare is mostly unexplored. Fig. (1) shows an adaptive case management system to support integrated care services (Cano et al., 2015).
Fig. (1))An adaptive case management system to support integrated care services (Cano et al., 2015).
Adaptive algorithms applied in the hospital setting can lead to continuous improvement in the allocation of resources, eliminating inefficiencies of the management system that is governed only by fixed rules that are not open to upgrading. The combination of predictive modeling, reinforcement learning, and heuristic optimization has proven to be effective in dealing with uncertain variations of demand, especially in industries where demand must be met within a short period of time.
Algorithmic Applications in Healthcare Management
Hospitals are multifaceted ecosystems with numerous processes intertwined, such as the admission of the patient and his/her treatment plan, allocation of resources, and emergency duty. Researchers have found that in such environments, machine learning and artificial intelligence can enhance the decision-making process. Key applications include:
Patient Flow Optimization
Machine-learning solutions utilize past admission records and the present hospital metrics to predict when it is easy or when it is busy at the emergency department (Guo et al., 2023). External factors like epidemic outbreaks or population shifts are also taken into consideration, and the triage system self-calibrates. Hospital IoT device data allows constant...
