Prioritizing Indicators for Rapid Response in Global Health Security:A Bayesian Network Approach

This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

An integrative risk assessment approach to enhancing patient safety in Continuous Renal Replacement Therapy(CRRT)

Continuous Renal Replacement Therapy(CRRT)serves as an intervention strategy for the management of acute kidney injury(AKl)in critically ill patients.However,owing to its complex nature and the potential for com-plications,the implementation of CRRT demands continuous monitoring to prevent patient safety risks.This study aims to identify and validate prevalent risks linked to CRRT within a real-world clinical setting,intending to propose preventive measures grounded in expert insights.To systematically categorize and visually depict the risks,their consequences,preventive measures,and recovery controls,our study employed the Bowtie method in conjunction with the Systems Engineering Initiative for Patient Safety(SEIPS)model.In addition to considering patient-related factors that exhibit variability among critically ill individuals,our key findings showed that the most influential risks impacting the effective delivery of CRRT are incidents of clotted filters,bleeding risks arising from the necessity of anticoagulation for filter efficacy,vascular catheter-related bloodstream infections,variations in proficiency levels among healthcare professionals regarding CRRT modalities,especially in oper-ating the CRRT machines,high nursing workload,frequent nursing turnover,occurrences of hypophosphatemia,variability in CRRT prescribing patterns,and issues related to communication among stakeholders.This research sheds light on the primary risks associated with CRRT and provides practical and viable strategies for effective management.Furthermore,the Bowtie diagram developed as part of this study serves as a valuable tool for visually representing the healthcare system and facilitating the identification of system-related risks within healthcare settings.