This paper presents a framework for effective last-mile delivery in underdeveloped megacities by combining social media, machine learning, and reinforcement learning. Leveraging a Graph Convolutional Networks and a Long Short-Term Memory model for traffic prediction, the framework incorporates multimodal data sources, such as social media sentiment analysis, to provide real-time insights into traffic dynamics. By framing the delivery problem as a Markov Decision Process, reinforcement learning dynamically adapts routing decisions to obtain delivery efficiency, reduce delays, and minimize fuel consumption. A case study in Bogotá demonstrates the framework’s effectiveness in mitigating urban traffic challenges. This work highlights the transformative potential of integrating adaptive learning technologies to address urban logistics’ environmental, economic, and operational complexities. Future research explores advanced methodologies, including multi-agent systems and transformer-based architectures, to further enhance scalability and adaptability in dynamic urban environments.

This chapter covers the “What” to achieve the goals of this research. What framework is required to follow for healthcare organizations that want to make use of available data, including theirs and/or owned by others. The need to strategically adopt the framework to improve clinical delivery and better-quality outcomes in the digital transformation era. Employing Industrial Engineering (IE) principles in constructing the framework specifies the needed components and steps to integrate people, information, process, and technology into one system. Information engineering is one of the 14 areas listed in IISE body of knowledge that is primarily followed in forming the framework, along with other areas, to ensure employing best practice and guidelines for successful executions. <!----> <!----> <!----> <!---->

This chapter details the system architecture, deployment, and validation of a big data analytics framework for healthcare. The architecture integrates five key domains—business, process, technology, application, and data—to support strategic alignment and real-time decision-making. A lighthouse project was executed in a large healthcare organization (HCO) to assess feasibility and value. The validation included a case study and interviews with subject matter experts (SMEs), whose feedback confirmed the framework's strengths in addressing data integration challenges and enabling organizational alignment. Thematic analysis identified areas for improvement, such as innovation, training, and operational modeling, confirming the framework's value as a foundation for digital transformation in healthcare. <!----> <!----> <!----> <!---->

This chapter introduces the promise and challenges of big data in healthcare, emphasizing the sector's shift toward real-time, data-driven intelligence. It explores the fragmentation of healthcare data and performance systems, which limits timely decision-making and operational efficiency. Despite high spending, US healthcare continues to underperform in key outcomes. The chapter outlines the need for a comprehensive framework that unifies structured, semi-structured, and unstructured data to support predictive analytics, performance improvement, and personalized care. Grounded in Healthcare 4.0 and systems engineering principles, the proposed framework addresses organizational, technical, and analytical gaps—offering a modular, scalable approach to transformation across diverse healthcare environments. <!----> <!----> <!----> <!---->

This part of the book discusses how healthcare organizations are increasingly analyzing historical clinical records and notes to perform predictive risk assessments and monitor real-time key performance indicators (KPIs), aiming for greater accuracy to gain financial advantages over competitors. It presents proposed solutions to help healthcare leaders identify systemic weaknesses that result in lost opportunities, reduced market share, poor patient experiences, and increased costs. A smart relationship diagram and a defined architecture for healthcare are introduced to illustrate these dynamics. Building on this foundation, the chapter also explains how to achieve the research goals by outlining the research methodology, including the design and procedures, data types and sources, data collection access, and financial resources required for execution. The ultimate objective is to create a comprehensive framework that connects the research ideas and addresses the gap in utilizing big data for performance measurement in healthcare. <!----> <!----> <!----> <!---->