Operations Research for Health Care in Red Zone: ORAHS 2022, Bergamo, Italy, July 17–22 (AIRO Springer Series, 10)

The book contains selected contributions from the 48th Annual Meeting of the EURO Working Group on Operational Research Applied to Health Services (ORAHS 2022) held in Bergamo, Italy, July 2022. ORAHS 2022 provided a network for researchers involved in the application of systematic and quantitative analyses to support planning and management in the health services sector, with the ultimate goal of pursuing good health and well-being. It was the ORAHS in the red zone, focused on the organization and reaction of health systems in the face of emergency situations such as the COVID pandemic. The questions addressed were, for example, how can hospitals and public authorities react to extreme scenarios by reorganizing their resources? How and to what extent do local health systems integrate hospitals to deal with a pandemic? How can Decision Support Systems for diagnosis and treatment help when battling a new virus for the first time? Contributions included a variety of methodological viewpoints (optimization, simulation, data analysis, predictive models, decision science, mathematical programming, machine learning, ...) and health services applications (hospital management, therapy calibration, analysis of statistical and epidemiological data, minimization of logistics costs, ...). This work strongly contributes to the Sustainable Development Goals (SDG) Programme. Contents Editors and Contributors Operations Research in the Red Zone A Comparison of Fairness Metrics for Health Care Problems 1 Introduction 2 Model Formulations 2.1 Comparing the Formulations 3 Experimental Plan 3.1 Results with |B|=1 3.2 Results with |B|=8 4 Conclusions References An Overview of Benefits and Limitations of the Process Model Notation Applied for Modeling Patient Healthcare Trajectory 1 Introduction 2 Literature Review 2.1 An Overview of the Health Care Trajectory 2.2 The Business Processing Model Notation 2.3 Decision Model and Notation 2.4 Case Management Model Notation 3 Methodology 4 Preliminary Results on Benefits and Limitations 4.1 Analysis of the Literature 4.2 Discussion of Preliminary Results 5 Conclusion and Future Research References Machine Learning Based Classification Models for COVID-19 Patients 1 Introduction 2 Methods 2.1 Deterministic Formulation 2.2 Robust Formulation 2.3 Distributionally Robust Formulation 3 Experimental Study 3.1 Data Collection 3.2 Numerical Investigation 4 Conclusions References Integrating Decision Support Tools in the COD-19 Platform 1 Introduction 2 Proposed Approach 2.1 Clustering and Policy Assignment 2.2 Health Resources Management 2.3 Disease Course Prediction 3 Case Study 3.1 Clustering and Policy Assignment: Design and Results 3.2 Health Resources Management: Design and Results 3.3 Disease Course Prediction: Design, Integration and Results 4 Conclusions References A Semi-online Ambulance Routing and Scheduling Problem with Complex Patient-Vehicle Relations 1 Introduction 2 Problem Description 3 A Selective Insertion Heuristic 4 Computational Experiments 4.1 Data Description 4.2 Preliminary Results 5 Conclusions References Towards a Unified Framework for Routing and Scheduling Planning in an Integrated Continuous Care Unit 1 Introduction and Literature Review 2 Case Study 3 Modeling Approach 3.1 Enhancing the Models 3.2 Model Extensions 4 Computational Study 5 Conclusions and Future Work References