A Cognitive Model for Emergency Management in Hospitals: Proposal of a Triage Severity Index

Hospitals play a critical role in providing communities with essential medical care during all types of disasters. Any accident that damages systems or people often requires a multifunctional response and recovery effort. Without an appropriate emergency planning, it is impossible to provide good care during a critical event. In fact, during a disaster condition, the same “critical” severity could occur for patients. Thus, it is essential to categorize and to prioritize patients with the aim to provide the best care to as many patients as possible with the available resources. Triage assesses the severity of patients to give an order of medical visit. The purpose of the present research is to develop a hybrid algorithm, called triage algorithm for emergency management (TAEM). The goal is twofold: First, to assess the priority of treatment; second, to assess in which hospital it is preferable to conduct patients. The triage models proposed in the literature are qualitative. The proposed algorithm aims to cover this gap. The model presented exceeds the limits of literature by developing a quantitative algorithm, which performs a numerical index. The hybrid model is implemented in a real scenario concerning the accident management in a petrochemical plant

Fourth Industrial Revolution: Current Practices, Challenges, and Opportunities

The globalization and the competitiveness are forcing companies to rethink and to innovate their production processes following the so-called Industry 4.0 paradigm. It represents the integration of tools already used in the past (big data, cloud, robot, 3D printing, simulation, etc.) that are now connected into a global network by transmitting digital data. The implementation of this new paradigm represents a huge change for companies, which are faced with big investments. In order to benefit from the opportunities offered by the smart revolution, companies must have the prerequisites needed to withstand changes generated by “smart” system. In addition, new workers who face the world of work 4.0 must have new skills in automation, digitization, and information technology, without forgetting soft skills. This chapter aims to present the main good practices, challenges, and opportunities related to Industry 4.0 paradigm

The importance of human error and reliability management in critical conditions and infrastructures

Protection and safeguarding of critical infrastructures (such us chemical industry, oil & gas industry, nuclear industry, etc.) is an important subject of study in the contemporary society. The study of risks associated to critical infrastructure required models of good practice to investigate the complexity of processes in case of accidents. The risk management can be viewed in two ways: human error and system reliability. In other words in terms of human error it is essential to ensure the operator performance to manage a complex system or an unexpected situation. While in terms of system reliability it is essential to ensure that a system is at least as reliable as the system it is replacing. The present chapter aims to analyze the main disasters occurred in critical infrastructures related to human errors or lack of reliability of systems

A Disaster Risk Management Performance Index to Assess Safety and Security in Industrial Plants

The goal of the research is to present an integrated approach that combines multi criteria analysis and disaster and emergency management. The study is based on a well know multi criteria technique, the Analytic Hierarchy Process (AHP). An AHP model is proposed in order to define a Disaster Risk Management Performance Index (DRMPI) that quantifies the performance and effectiveness of risk management to evaluate emergency alternative problems. This model is implemented in a real case study

Human factors challenges in disaster management scenario

The present chapter aims to propose a model to manage complexity during a disaster accident caused by human factors and errors. The model allows to evaluate the human error probability under critical conditions and stress conditions. A hybrid model based on Simulator for Human Error Probability Analysis (SHERPA) is proposed and analyzed. A specific area of application is investigated concerning the human behavior during an emergency situations in a petrochemical plant. Furthermore, the chapter proposes an innovative approaches for monitoring the human factors in industrial plant through KPIs indicators. The model is implemented in a real case study concerning a petrochemical company