In this study, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank the telemedicine performance of seven major Japanese cities: Fukuoka, Sapporo, Yokohama, Kyoto, Osaka, Nagoya, and Tokyo. The ranking was consistent throughout the analysis based on the five criteria—Infrastructure, Healthcare System Integration, Accessibility & Equity, Service Breadth & Quality, and Regulatory Environment. Fukuoka was the best performer, followed by Sapporo, and Tokyo, even with its high medical resources, ranked last. The same ranking was observed even when the cost criterion was given a 50% weight, which means that structural and governance factors have more influence than affordability in determining performance. The findings question the view that telemedicine success relies exclusively on economic and medical scale. Rather, they point to the effectiveness of Japan's regional innovation policies; for example, proactive local governance, as in the case of Fukuoka's Special Zone status, and a clear demographic mandate for rural healthcare access, as in Hokkaido, are considered more important drivers of a robust and equitable telemedicine ecosystem.

This study evaluates the relative efficiency of digital health adoption across ten major German cities and regions. In a landscape where digital transformation is critical, this research moves beyond mere technological assessment to determine which locales most effectively convert inputs into outputs. A multi-criteria decision-making framework is employed, integrating Data Envelopment Analysis (DEA) under both constant and variable returns-to-scale assumptions and the TOPSIS method. Key metrics include Infrastructure, Healthcare System Integration, Service Breadth, Regulatory Environment, and a critical cost factor, Accessibility & Equity. Results from DEA models highlight a system-wide scale inefficiency, yet identify specific efficient units under variable returns, including Berlin, Rhineland-Pfalz, Leipzig, and Aachen. The TOPSIS analysis, particularly when prioritizing cost-equity, reveals a distinct ranking: Rhineland-Pfalz, Leipzig, and Aachen emerge as top performers, while major economic hubs like Munich, Frankfurt, and Stuttgart demonstrate lower efficiency due to high costs and systemic fragmentation. The findings challenge the presumption that economic scale guarantees digital health efficiency, instead underscoring the superior performance of strategically coordinated and equity-focused models. This study provides policymakers with a robust framework for benchmarking and highlights governance and integration, not just investment, as the key levers for enhancing digital health system performance.

Hospital departments are continuously challenged to allocate their limited resources—beds, nursing staff, and operating room (OR) time—among patients with different clinical priorities on the waiting list. This paper presents a solution to this intricate planning problem by constructing a novel Integer Linear Programming (ILP) model to schedule elective patient admissions optimally. The goal is to increase the total priority score of the patients admitted, where the score indicates clinical urgency and waiting time, while at the same time honoring the restrictions on bed capacity, nursing hours, and OR availability through a multi-day planning horizon. The model uses binary decision variables for patient admission as well as pre-processed parameters to map the individual patient resource consumption over the time they are expected to stay in the hospital. We showcase the model's application through a realistic scenario with synthetic data. The outcomes reveal that the suggested framework not only effectively creates an optimum admission timetable but also considerably enhances the use of vital resources when compared to a first-come-first-served standard. The ILP model is not only potent but also clear and just decision-support tool for hospital management, according to the study's findings. By assigning priority to clinical criteria and making patient access more equitable, it provides a data-guided way to improve operational productivity, as well as to decide the trade-offs that come with the limitations of healthcare facilities in terms of resources. Besides, the model is computationally capable of meeting the needs for the inclusion of extra real-world constraints and adjustments.

Modern healthcare's growing intricacy, which is a result of an aging population and increased use of technology, necessitates the use of very advanced operational management. A solution is provided to the issue of hospital optimization through the use of a unified mathematical programming method that combines two main areas, resource allocation and workforce management. We present a new multi-objective Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) framework made especially for the "smart hospital" setting. The model is able to do several things at once; it is able to come up with the best patient admission scheduling, use the beds and operating rooms in the most efficient way possible, and schedule the nurse staff all while being subjected to the real-world constraints of demand fluctuations, staff preferences, skill mixes, and labor regulations. A detailed case study is presented which clearly shows the effectiveness of the model, revealing a 15% boost in resource utilization, a 12% decrease in operational costs, and increased nurse schedule satisfaction, all whilst still being able to offer high standards of patient care coverage. The findings point out the great opportunity of using integrated optimization models to make healthcare delivery systems more efficient, cheaper, and more adaptive, thus giving hospital administrators a strong decision-support tool.

The Quadratic Assignment Problem (QAP) is still considered to be one of the most difficult and widely used models in combinatorial optimization. The layout of healthcare facilities has been its most significant application area since the 1970s, representing a crucial field of study for increasing operational efficiency, patient safety, and staff flow. The QAP context has been continually altered and supplemented to cover the particular intricacies of the healthcare sector. After Elshafei's groundbreaking paper in 1977, the QAP framework was reinvented and extended to the point where it gained acceptance in the healthcare facility location planning area. This review offers a synthesis of the existing literature from 1977 to 2025 and classifies the research into ten different methodological streams: Exact Solution Methods, Classical Heuristics, Metaheuristics, Hybrid Approaches, Robust Optimization, Fuzzy QAP, Stochastic Programming, Multi-Objective QAP, Special Structure Exploitation, and Parallel & Dis-tributed Computing. The critical assessment of the transition of solution procedures and how the techniques for handling uncertainty have been developed shows how the research has progressed from modeling with one deterministic objective to a sophisticated data-driven approach where multiple objectives are characterized as well as the inherent uncertainties of the system. The analysis indicates the integration and hybridization trend—in the case of algorithms, objectives, and data sources is quite strong—pointing out the future lines of research in areas such as real-time adaptive layouts, deep learning integration, and pandemic-responsive design.