When people need healthcare, few worry about being harmed by someone from the medical team making a mistake. Unfortunately, mistakes do happen, and many of the adverse events are not only serious but also preventable. Many countries struggle with top-heavy systems, in which decisions are made about how care is provided by those who are far from experienced in caring for patients. This must change. Professionals at the sharp end need support, structure, and help organizing necessary information to create a safe culture, a learning environment, and safe patient care-all at lower costs. This handbook provides tools for designing a structure for a management system, as well as the tools for documenting processes within it. The starting point is based on current safety research. The book is designed for medical professionals, managers, project members, politicians, public officials, and executives-all who work with patient safety matters. The content shows a new way to healthcare management, presenting an alternative approach together with concrete advice on how healthcare executives and practitioners can begin to think and act differently in order to provide safe healthcare.

Artificial Intelligence for Security explores terminologies of security and how AI can be applied to automate security processes. Additionally, the text provides detailed explanations and recommendations for how implement procedures. Practical examples and real-time use cases are evaluated and suggest appropriate algorithms based on the author's experiences. Threat and associated securities from the data, process, people, things (e.g., Internet of things), systems, and actions were used to develop security knowledge base, which will help readers to build their own knowledge base. This book will help the readers to start their AI journey on security and how data can be applied to drive business actions to build secure environment.

Artificial Intelligence Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years.