Background: The COVID-19 pandemic exposed substantial shortcomings in emergency hospital systems, demonstrating that conventional linear models fail to accurately depict the intricate interdependencies among clinical, operational, and infrastructural components. This research employs nonlinear and fractal analytical frameworks to characterize and forecast the dynamic behavior of the principal emergency clinical hospital in Iași, Romania, for the years 2020–2022. Methods: The study employed a mixed-methods approach, combining retrospective analysis of hospital risk factors (infection rates, staff absenteeism, and ICU occupancy) with computational simulations. We analyzed time-series data using several nonlinear metrics, including the Higuchi fractal dimension, Lyapunov exponents, Shannon entropy, and multifractal detrended fluctuation analysis (MF-DFA). A modified Lotka–Volterra system was utilized to simulate feedback mechanisms between clinical and operational stressors. Results: The analyzed variables demonstrated nonlinear dynamics and fractal self-similarity across temporal scales (Hurst exponent = 0.73 ± 0.04; D_H = 1.35–1.49). Positive Lyapunov exponents (λ > 0) indicated deterministic chaotic behavior, whereas the multifractal spectrum width (Δα ≈ 0.62) indicated heterogeneous scaling patterns across hospital departments. Increasing fractal complexity was closely associated with diminished clinical efficiency and staff availability. Conclusions: The progression of pandemic-related risk followed a trajectory consistent with self-organized criticality, transitioning from stable to oscillatory and ultimately to chaotic phases as system load intensified. Adding nonlinear markers such as fractal dimension, Lyapunov exponents, entropy, and multifractal parameters to hospital monitoring systems could improve early warning systems and make it easier for healthcare organizations near critical thresholds to manage risk
Background: COVID-19, declared a Public Health Emergency of International Concern, affected hospital operations by increasing the number of patients presenting to hospitals, the necessity to control infections, and the shortage of staff. Through our study, we evaluated the quality and quantity of care and how these changes affected patient experience and outcomes, and we identified actionable methods in order to prepare for the future. Methods: We analyzed data between 2020 and the first half of 2023 from a Romanian tertiary military hospital using patient-reported experience surveys and operational indicators (readmissions, postoperative complications). Associations were assessed using Pearson and Spearman correlation coefficients. We then benchmarked against an ideal surge-ready hospital using a 0-1 Composite Risk Index (CRI) that integrates occupancy, adverse-event density, flow reliability, and readmissions. Results: Overall satisfaction was strongly influenced by satisfaction with medical care (p < 0.001). Higher overall satisfaction was associated with fewer readmissions. Benchmarking showed the highest risk in the ICU, intermediate in Surgery and Internal Medicine, moderate in the ED, and the lowest in the Ambulatory. Satisfaction was highest in the initial period of the COVID-19 pandemic, dropped in 2022, and showed partial recovery at the end of this period. Conclusions: Quality of care is a critical measure of hospital performance, especially in the context of the COVID-19 pandemic. Patient feedback revealed areas of good practice and also the limits of the system, influenced by the burden of multisystem disease and limited resources. Post-COVID-19 disability outcomes are strongly influenced by the quality and continuity of care. Systematic use of patient- reported outcomes, standardized evaluation, and adaptive rehabilitation is essential to preserve the quality of care and reduce long- term functional impairment.
