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End-of-life care for frail and elderly patients is frequently characterised by high healthcare utilisation, fragmented service delivery, and limited coordination, resulting in variable quality and excess cost. This study presents a proof-of-concept framework, tested using synthetic data to illustrate potential applications in strategic planning. Few planning approaches integrate patient-level pathways into operational models that balance efficiency with patient-centred outcomes. Optimisation models were developed to support strategic resource planning for frail, elderly, and palliative patients in the final year of life. Two formulations were explored: one minimising overall cost and another aligning demand with available capacity. Patients were stratified into ten representative categories and assigned to structured pathways with varying resource intensities across hospital beds, palliative beds, community nursing, and virtual wards. A synthetic dataset representing plausible twelve-month service trajectories was used to assess model performance. Both models produced feasible allocations that satisfied expected demand within capacity limits. Most patient groups were consistently assigned to dominant pathways, while some shifted depending on the optimisation objective, illustrating trade-offs between cost efficiency and balanced utilisation. Demand intensified in the final months of life but remained manageable under planning assumptions. The modelling framework demonstrates the feasibility of applying optimisation to anticipatory planning, enabling comparison of service configurations and supporting more coordinated, efficient, and patient-centred end-of-life care.

Effective biomedical waste management is critical for ensuring health safety, environmental sustainability, and regulatory compliance in healthcare settings. This study introduces an integrated decision-making framework that combines the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Network Process (ANP) to evaluate and prioritise sustainable waste management practices, with a focus on composting and recycling. The framework incorporates fuzzy logic to handle uncertainties in expert-driven evaluations, ensuring robust and adaptive assessments. Using criteria such as environmental impact, health safety, cost-efficiency, social equity, and regulatory adherence, the DEMATEL analysis highlights key causal relationships, providing targeted prioritisation of strategies. Results indicate that composting achieves superior performance, with higher scores in environmental sustainability (0.85) and health impact (0.88), making it the preferred approach for fostering circular economy practices. The objective of this study is to support data-driven decision-making by integrating expert opinions and system interdependencies. This study underscores the importance of multi-criteria, data-driven methods like Fuzzy DEMATEL-ANP in advancing sustainable biomedical waste management systems, promoting resource optimisation and sustainable development within healthcare facilities.

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R2 of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R2 ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R2 ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation.

Background/Objectives: Efficient allocation of hospital resources is crucial in managing lower extremity trauma. Selected patients with stable injuries may not require inpatient hospitalization while awaiting surgical fixation. This study describes the feasibility and safety of a structured Home-based Preoperative Management (HPM) pathway for such patients. Methods: We conducted a retrospective, single-center observational study of 187 adult patients with isolated lower extremity fractures managed with HPM between 2019 and 2022. All patients were discharged home from the Emergency Department with standardized instructions, immobilization, anticoagulation, and planned follow-up. No comparator group was included. Results: Of 187 patients (mean age 49.7 y), 23 patients (12.3%) returned to the Emergency Department during the preoperative waiting period. The mean time from Emergency Department presentation to surgery was 8.5 days. Overall, 164 patients (87.7%) completed the preoperative waiting period at home without requiring an additional Emergency Department visit. Within one year after surgery, 51 patients (27.3%) presented to the Emergency Department; 29 of these visits (56.9%) were considered surgery-related. Patients who returned to the Emergency Department before surgery had a higher likelihood of postoperative Emergency Department visits within one year compared with those who did not (69.6% versus 21.3%, p < 0.001). Time to surgery was not associated with postoperative Emergency Department visits (p = 0.763). Conclusions: In this retrospective cohort, Home-Based Preoperative Management was feasible and appeared safe for carefully selected patients with lower extremity trauma. Most patients were able to await surgery at home without unplanned Emergency Department visits. Given the absence of a comparator group, no conclusions regarding comparative effectiveness or superiority over inpatient management can be drawn.

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data and enables dynamic resource allocation. The results demonstrate significant improvements in forecasting performance and healthcare preparedness compared to traditional models. This work enhances decision-making in pandemic management by leveraging machine learning for real-time operational efficiency.

Background While a large body of research has documented socioeconomic and migrant inequities in the effective use of healthcare services, the reasons underlying such inequities are yet to be fully understood. This study assesses the interplay between racial discrimination and socioeconomic position, as conceptualised by Bourdieu, and their contributions to healthcare navigation and optimisation. Methods Using a cross-sectional survey in Luxembourg we collected data from individuals with wide-ranging migration and socioeconomic profiles. We fitted sequential multiple linear and logistic regressions to investigate the relationships between healthcare service navigation and optimisation with perceived racial discrimination and socioeconomic position measured by economic, cultural and social capital. We also investigated whether the ownership of these capitals moderates the experience of racial discrimination in healthcare settings. Results We observed important disparities in healthcare navigation among different migrant communities. These differences were explained by accounting for the experience of racial discrimination. Racial discrimination was also negatively related with the extent of healthcare services optimisation. However, the impact of discrimination on both health service navigation and optimisation was reduced after accounting for social capital. Higher volumes of economic and social capital were associated with better healthcare experience, and with a lower probability of perceived racial discrimination. Conclusions Racial discrimination plays a substantial role in accounting for inequality in healthcare service navigation by different migrant groups. This study highlights the need to consider the complex interplay between different forms of economic, cultural and social capital and racial discrimination when examining migrant, and racial/ethnic differences in healthcare. Healthcare inequalities arising from socioeconomic position and racism need to be addressed via multilevel policies and interventions that simultaneously tackle structural, interpersonal, and institutional dimensions of racism.

Introduction: Spinal disorders are amongst the conditions with the highest burden of disease. To limit the increase of healthcare-related costs in the ageing population, the selection of different types of care for patients with spinal disorders should be optimized. The first step is to investigate the characteristics of these patients and the relationship with treatment. Research Question: The primary aim of this study was to provide insights in the characteristics, symptoms, diagnosis and treatment of patients referred to a specialized spinal health care centre. The secondary aim was to perform an in-depth analysis of resource utilization for a representative subgroup of patients. Methods: This study describes the characteristics of 4855 patients referred to a secondary spine centre. Moreover, an extensive analysis of a representative subgroup of patients (~20%) is performed. Results: The mean age was 58.1, 56% of patients were female, and the mean BMI was 28. In addition, 28% of patients used opioids. Mean self-reported health status was 53.3 (EuroQol 5D Visual Analogue Scale), and pain ranged from 5.8 to 6.7 (Visual Analogue Scale neck/back/arm/leg). Additional imaging was received by 67.7% of patients. Surgical treatment was indicated for 4.9% of patients. The majority (83%) of non-surgically treated patients received out-of-hospital treatment; 25% of patients received no additional imaging or in-hospital treatment. Conclusion: The vast majority of patients received non-surgical treatments. We observed that ~10% of patients did not receive in-hospital imaging or treatment and had acceptable or good questionnaire scores at the time of referral. These findings suggest that there is potential for improvement in efficacy of referral, diagnosis, and treatment. Future studies should aim to develop an evidence base for improved patient selection for clinical pathways. The efficacy of chosen treatments requires investigation of large cohorts.

This study aimed to investigate the future trajectories of last-mile delivery (LMD), and their implications for sustainable urban logistics and smart city planning. Through a Delphi-based scenario analysis targeting the year 2030, this research draws on inputs from a two-round Delphi study with 52 experts representing logistics, academia, and government. Four key thematic areas were explored: consumer demand and behavior, emerging delivery technologies, innovative delivery services, and regulatory frameworks. The projections were structured using fuzzy c-means clustering, and analyzed through the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), supporting a systemic understanding of innovation adoption in urban logistics systems. The findings offer strategic insights for municipal planners, policymakers, logistics service providers, and e-commerce stakeholders, helping align infrastructure development and regulatory planning with the evolving needs of last-mile logistics. This approach contributes to advancing resilient, low-emission, and inclusive smart city ecosystems that align with global sustainability goals, particularly those outlined in the UN 2030 Agenda for Sustainable Development.