Healthcare

Background/Objectives: Older individuals are more vulnerable to stress and trauma. Although pressure injuries (PIs) are recognized as a significant complication, the specific impact of frailty on PI development in older Asian trauma patients remains insufficiently explored. This study aims to investigate the factors associated with the occurrence of hospital-acquired pressure injuries (HAPU) in older patients aged ≥65 years, including frailty. Methods: This study is a retrospective secondary data analysis of 3418 older trauma patients admitted to a regional trauma center (including ICU and trauma ward) from 1 January 2020 to 31 December 2023. Patients with PIs present on admission (POA) were excluded to strictly analyze new PI occurrence. Frailty was assessed using the mFI-5. Results: The mean age of participants was 77.33 years. During hospitalization, 2.5% (n = 84) of patients developed new PIs. Multivariate logistic regression identified that higher frailty score (Odds Ratio [OR] = 1.59, 95% Confidence Interval [CI]: 1.26–2.02), lower BMI (OR = 0.93, 95% CI: 0.86–0.99), hypoalbuminemia (OR = 0.55, 95% CI: 0.36–0.84), and prolonged hospital stay (OR = 1.05, 95% CI: 1.04–1.06) were independently associated with PI occurrence. Chronological age was not a significant predictor in the multivariate model. Conclusions: Frailty, nutritional status (BMI, albumin), and prolonged hospital stay are significant factors associated with HAPU in older trauma patients.

Background: In the era of Health 4.0, Emergency Departments (EDs) face increasing crowding and complexity, necessitating smart management solutions to balance efficiency with equitable care. Effective scheduling is critical for optimizing patient throughput and mitigating congestion. Methods: This paper constructs a decision support framework using Discrete Event Simulation (DES) to evaluate three patient scheduling strategies, including the Initial-First policy, Alternating 1:1 policy and a Slack-Based dynamic policy. The simulation framework has been conducted using a standardized operational dataset representing typical ED dynamics. The threshold of SBP was optimized by a grid search method to guarantee an objective comparison. Results: The simulation results show that when adopting the optimized SBP policy, the mean waiting time was shortened by around 23.8%, thus meeting all triage service level targets. Also, it could be seen that Slack-Based dynamic policy was robust under different arrival rates and physician staffing levels. Conclusions: This proposed model can provide a real-time and dynamic solution for ED resource allocation, meeting the demand of modern smart hospitals management.

Artificial intelligence (AI) is rapidly transforming anesthesiology practice across perioperative settings. This review explores the evolution and implementation of hybrid decision-making models that integrate AI capabilities with human clinical expertise. From historical foundations to current applications, we examine how machine learning algorithms, deep learning networks, and big data analytics are enhancing anesthetic care. Key applications include perioperative risk prediction, AI-assisted patient education, automated analysis of clinical records, airway management support, predictive hemodynamic monitoring, closed-loop anesthetic delivery systems, and pain management optimization. In procedural contexts, AI demonstrates promising utility in regional anesthesia through anatomical structure identification and needle navigation, monitoring anesthetic depth via EEG analysis, and improving quality control in endoscopic sedation. Educational applications include intelligent simulators for procedural training and academic productivity tools. Despite significant advances, implementation challenges persist, including algorithmic bias, data security concerns, clinical validation requirements, and ethical considerations regarding AI-generated content. The optimal integration model emphasizes a complementary approach where AI augments rather than replaces clinical judgment—combining computational efficiency with the irreplaceable contextual understanding and ethical reasoning of the anesthesiologist. This hybrid paradigm reinforces the anesthesiologist’s leadership role in perioperative care while enhancing safety, precision, and efficiency through technological innovation. As AI integration advances, continued emphasis on algorithmic transparency, rigorous clinical validation, and human oversight remains essential to ensure that these technologies enhance rather than compromise patient-centered anesthetic care.