From isolation to revival: trade recovery amid global health crises.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of designing effective trade recovery measures in response to global health events (GHEs). This study combines international trade risk management theory and multi-case comparative analysis of past GHEs to present a theoretical framework for designing national trade recovery measures for future events. RESULTS: The research finds that during GHEs, trade risks shift to fundamental uncertainty, requiring spatial-temporal-subject dimension recovery measures. The study suggests changing the focus of trade recovery policy design from emergency-oriented and single-dimension measures to reserve-oriented and enduring-effect measures of comprehensive dimensions at micro- and macroeconomic levels. CONCLUSION: The study contributes to the debate on managing trade risks in times of crisis, where there is a need to develop effective trade recovery measures that account for the complexities of global trade and the unique challenges of GHEs. The findings provide practical guidance for trade officials and policymakers to design measures in response to GHEs to improve a country's overall trade recovery.

Fatigue Risk Management Preferences for Consumer Sleep Technologies and Data Sharing in Aviation.

INTRODUCTION: Employees from any type of aviation services industry were asked to give their opinions about the usefulness of consumer sleep technologies (CSTs) during operations and their willingness to share data from CSTs with their organizations for fatigue risk management purposes under a variety of circumstances.METHODS: Respondents provided information about position in aviation and use of CST devices. Respondents ranked sleep issues and feedback metrics by perceived level of importance to operational performance. Respondents rated their likelihood to share data with their organization under a series of hypothetical situations.RESULTS: Between January-July 2023, 149 (N = 149) aviation professionals responded. Pilots comprised 72% (N = 108) of respondents; 84% (N = 125) of all respondents worked short- or medium-haul operations. "Nighttime operations" and "inconsistent sleep routines" ranked as the most important issues affecting sleep. "Sleep quality history" and "projected alertness levels" ranked as most important feedback metrics for personal management of fatigue. Respondents were split between CST users (N = 64) and nonusers (N = 68). CST users did not indicate a strong preference for a specific device brand. The most-reported reason for not using a CST was due to not owning one or no perceived need. Respondents indicated greater likelihood of data sharing under conditions where the device was provided to them by their organization.DISCUSSION: These results suggest that aviation professionals are more concerned about schedule-related disturbances to sleep than they are about endogenous sleep problems. Organizations may be able to increase compliance to data collection for fatigue risk management by providing employees with company-owned CSTs of any brand.Devine JK, Choynowski J, Hursh SR. Fatigue risk management preferences for consumer sleep technologies and data sharing in aviation. Aerosp Med Hum Perform. 2024; 95(5):265-272.

Healthcare Risk Management and Monitoring of patient falls in Italian National Service. The experience of a Local Health Authority.

Falls are a widespread concern in hospitals settings. In Italy, falls are the fourth frequent damage claim type after surgical, diagnostic and therapeutic error and 90% of falls are avoidable. The first necessary action for the prevention of falls consists in identifying the possible risk factors, in relation to the characteristics of the patient and those of the environment and the structure that hosts him, in terms of safety, organization and adequacy of the process welfare. In this work we wanted to evaluate the extent, frequency and characteristics of the phenomenon of falls in the population hospitalized at the Local Health Authority called "Roma 2", with the aim of analyzing the critical issues to allow the identification of possible preventive and improvement interventions as well as reducing the risk of falls.

Managing extreme AI risks amid rapid progress.

Preparation requires technical research and development, as well as adaptive, proactive governance.

An Exploration of Nurses' Attitudes and Beliefs on Reporting Medication Errors.

BACKGROUND: Medication errors in health care are prevalent. Nurses play an important role in reporting; however errors remain underreported in incident reporting systems. Understanding the perspective of nurses will inform strategies to improve reporting and build systems to reduce errors. PURPOSE: The purpose of this study was to explore nurses' perceptions and attitudes of medication error reporting practices. METHODS: This qualitative study used direct content analysis to analyze interview sessions with 21 total nurses. RESULTS: Participant's description of medication error reporting practices fell into 2 themes. Internal factors described circumstances within nurses themselves that affect reporting. External factors described outside influences from processes or places. CONCLUSIONS: Medication error reporting is a multidimensional phenomenon with internal and external factors impacting nurses' attitudes and willingness to report errors. Nurses need support from leadership to understand that reporting medication errors can improve practice and impact patient outcomes.

What is the effectiveness of reporting systems in promoting learning in healthcare?

Patient safety in healthcare remains a top priority. Learning from safety events is vital to move towards safer systems. As a result, reporting systems are recognised as the cornerstone of safety, especially in high-risk industries. However, in healthcare, the benefits of reporting systems in promoting learning remain contentious. Though the strengths of these systems, such as promoting a safety culture and providing information from near misses are noted, there are problems that mean learning is missed. Understanding the factors that both enable and act as barriers to learning from reporting is also important to consider. This review, considers the effectiveness of reporting systems in contributing to learning in healthcare.

Building resilience: The role of enterprise security risk management in developing a resilient organisation.

Enterprise security risk management (ESRM) has continued to gain global acceptance as a management philosophy for the development and implementation of an enterprise-wide corporate security programme. As organisations continue to rebuild and recover from COVID-19, the value of assessing the resilience of an organisation through regular testing of its response to events has gained prominence. There are opportunities to link the development and implementation of a risk-based approach for designing a security programme, to assessing an organisation's resilience to future events. Organisations can benefit from the complementary approaches of ESRM and organisational resilience once the commonalities are identified and embraced. This paper expands upon the ESRM management approach, linking the concepts of ESRM to the design of a resilient enterprise.

Operational resilience towards a global alignment.

Operational resilience lies between operational risk and business continuity. This paper provides a view on the implementation of the operational resilience framework, and its relationship with operational risk and business continuity. It analyses the similarities and differences between these exercises and how management information from these exercises can be leveraged and aligned. The paper also provides answers to three important questions: (1) What pushed the international regulators to add additional oversight? (2) What benefits and challenges are brought by operational resilience? (3) Why is it important to harmonise operational resilience within the international regulatory landscape?

Strategic dilemmas when managing cyber attacks.

Cyber attacks have a significant business impact, with the potential to escalate into crises if poorly managed. A recurring pattern is strategic dilemmas that cannot be resolved satisfactorily. Some dilemmas are more pronounced, others less so, and therefore often catch decision-makers unprepared, leaving only bad options for decision-making. Something that all dilemmas have in common is that the associated decisions can have a lasting impact on relationships with stakeholders. This paper introduces four recurring dilemmas; shows the typical considerations; lists options for mitigating these dilemmas; and describes the basic requirements for implementing mitigations. The dilemmas and options, in turn, are rooted in the organisation-specific design of: cyber security incident management and response; IT service continuity and disaster recovery management; business continuity management; and crisis management and communication.

Mine the past: How to make better risk-based decisions and improve outcomes with historical threat data.

The impact of every crisis has the potential to cascade throughout an organisation's operations, supply chain and market ecosystem. To properly understand and mitigate this ripple of dynamic risk, business continuity, security and risk management leaders need to know where to focus their attention. Looking at historical threat data provides a clearer picture of the risk landscape, helping leaders better anticipate and plan for the future. To date, however, there have been challenges in this process. As the volume of data about critical events continues to grow at an alarming rate, sifting manually through data puts organisations - and business continuity - in jeopardy. This paper discusses the value of historical threat data and innovations in data-mining technology that can unlock the true power of historical data for informed, strategic decision-making and better outcomes during a crisis.

Enhancing patient safety: detection of in-hospital hazards and effect of training on detection (by training in a low-fidelity simulation Room of Improvement based on hospital-specific CIRS cases).

IMPORTANCE: Adequate situational awareness in patient care increases patient safety and quality of care. To improve situational awareness, an innovative, low-fidelity simulation method referred to as Room of Improvement, has proven effective in various clinical settings. OBJECTIVE: To investigate the impact after 3 months of Room of Improvement training on the ability to detect patient safety hazards during an intensive care unit shift handover, based on critical incident reporting system (CIRS) cases reported in the same hospital. METHODS: In this educational intervention, 130 healthcare professionals observed safety hazards in a Room of Improvement in a 2 (time 1 vs time 2)×2 (alone vs in a team) factorial design. The hazards were divided into immediately critical and non-critical. RESULTS: The results of 130 participants were included in the analysis. At time 1, no statistically significant differences were found between individuals and teams, either overall or for non-critical errors. At time 2, there was an increase in the detection rate of all implemented errors for teams compared with time 1, but not for individuals. The detection rate for critical errors was higher than for non-critical errors at both time points, with individual and group results at time 2 not significantly different from those at time 1. An increase in the perception of safety culture was found in the pre-post test for the questions whether the handling of errors is open and professional and whether errors are discussed in the team. DISCUSSION: Our results indicate a sustained learning effect after 12 weeks, with collaboration in teams leading to a significantly better outcome. The training improved the actual error detection rates, and participants reported improved handling and discussion of errors in their daily work. This indicates a subjectively improved safety culture among healthcare workers as a result of the situational awareness training in the Room of Improvement. As this method promotes a culture of safety, it is a promising tool for a well-functioning CIRS that closes the loop.

Species-specific eDNA assay development for enhanced box jellyfish risk management in coastal environments.

Human interaction with marine creatures holds both positive and negative dimensions. Coastal communities benefit from marine environments, relying on them for sustenance and livelihoods. Fishing activities support economies, and marine biodiversity contributes to overall ecosystem health. However, challenges like overfishing, habitat destruction, and pollution pose threats to both marine life and human communities. Recently, there has been widespread concern regarding the potential increase in jellyfish populations across global marine ecosystems, attributed mainly to environmental factors such as climate drivers and anthropogenic forces, or their complex interactions. Encounters with hazardous marine species, such as box jellyfish, exemplify the dangers associated with coastal activities. Unintended interactions may lead to stings, injuries, and even fatalities, necessitating proactive measures and advanced technologies. This study addresses the inadequacies of existing measures in preventing box jellyfish incidents by introducing environmental DNA (eDNA) assays for detecting the deadly Chiropsoides buitendijki and focuses on developing qPCR and dPCR-based eDNA assays. Emphasising prevention over treatment, the study establishes a proactive system to assess C. buitendijki distribution across 63 tourist beaches in the Gulf of Thailand. Comparative analysis highlights the superior performance of dPCR over qPCR and traditional surveys. The dPCR experiment yielded positive results for all eDNA samples collected at sites where C. buitendijki had previously been identified. Remarkably, the eDNA testing also detected positive results in 16 additional sample locations where no physical specimens were collected, despite reported jellyfish stings at some of these sites. These findings underscore the precision and efficacy of the proposed eDNA detection technology in the early detection and assessment of box jellyfish distribution. This advancement therefore not only aids ecological research but also serves as a valuable tool for safeguarding public health, providing an early warning system for potential jellyfish encounters. Balancing positive human-marine interactions with effective risk mitigation strategies is crucial for sustainable coexistence, the preservation of marine ecosystems, and human well-being.

The role of grid management in community risk governance: a case study in Yuelu, China.

Objective: In this study, we aim to provide a comprehensive analysis of the effectiveness of the risk prevention and control mechanism within the grid management model for community risk prevention. We emphasize the importance of thoroughly examining the risk prevention and control mechanism to enhance risk management efforts in urban communities, particularly in response to unforeseen outbreaks such as COVID-19. Methods: Case studies are widely acknowledged as one of the most effective approaches to examine governance in China. In this study, the "Yuelu Model" serves as an illustrative example to demonstrate the application and effectiveness of grid management in community risk governance. To ensure the validity of the case study, it is imperative to adhere to the principle of representativeness. The collection of case data involves a combination of primary and secondary sources, and supplementary information is obtained through follow-up investigations conducted via WeChat, telephone, and other means, thereby enhancing the comprehensiveness and accuracy of the data. Results: Our analysis reveals significant findings regarding the impact of the grid management model, fulfilling a triple role as a "Social Safety Valve" in the management process: (1) Community stress reduction function, (2) Community alarm function, and (3) Community integration function. Furthermore, we explore the adaptability of the grid management mechanism in addressing community risks, highlighting its effectiveness and potential for broader application. Discussion: The findings of this study suggest that: Firstly, it is crucial to establish a shared information repository among different departments on a big data platform. Secondly, a dynamic government public information internal network should be established through collaborative efforts among multiple departments. Thirdly, implementing a regular (or periodic) early warning mechanism is essential. Lastly, the establishment of a high-quality talent team for power grid management is highly recommended. Our research provides valuable insights to enhance community risk governance.

Early warning of systemic risk in stock market based on EEMD-LSTM.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions.

In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

Gerenciamento do protocolo de prevenção de lesão por pressão: construção de painel de bordo informatizado / Management of the pressure injury prevention protocol: construction of a computerized dashboard / Gestión del protocolo de prevención de lesiones por presión: construcción de panel informatizado

Objetivo: Relatar a construção e implantação de painel de bordo, desenvolvido por enfermeiros e profissionais da tecnologia da informação, para gerenciamento do Protocolo de Prevenção de Lesão por Pressão. Métodos: Trata-se de um relato de experiência sobre a construção e implantação de painel de bordo informatizado para gerenciamento de protocolo em um hospital privado universitário, localizado no interior do estado de São Paulo. Resultados: A construção do painel de bordo foi dividida nas seguintes etapas: revisão e atualização do protocolo, construção do modelo eletrônico e implementação. A divulgação foi realizada pela Comissão de Prevenção de Lesão por Pressão. Conclusão: O painel de bordo possibilitou a visualização rápida e em tempo real dos riscos dos pacientes, intervenções propostas e efetividade das medidas de prevenção, além de promover a integração e empoderamento dos profissionais na gestão do cuidado. (AU)

Objective: To report the construction and implementation of a dashboard, developed by nurses and information technology professionals, to manage the Pressure Injury Prevention Protocol. Methods: This is an experience report on the construction and implementation of a computerized dashboard for protocol management in a private university hospital, located in the interior of the state of São Paulo. Results: The construction of the dashboard was divided into the following steps: review and update of the protocol, construction of the electronic model and implementation. The Pressure Injury Prevention Commission disclosed the tool. Conclusion: The dashboard enabled the quick and real-time visualization of patient risks, proposed interventions and effectiveness of prevention measures, in addition to promoting the integration and empowerment of professionals in the management of care. (AU)

Objetivo: Informar la construcción e implementación de un panel, desarrollado por enfermeras y profesionales de tecnologías de la información, para gestionar el Protocolo de Prevención de Lesiones por Presión. Métodos: Se trata de un informe de experiencia sobre la construcción e implementación de un panel computarizado para la gestión del protocolo en un hospital universitario privado, en el interior del estado de São Paulo. Resultados: La construcción del panel se dividió en los siguientes pasos: revisión y actualización del protocolo, construcción del modelo electrónico e implementación. La divulgación fue realizada por la Comisión de Prevención de Lesiones por Presión. Conclusión: El panel permitió la visualización rápida y en tiempo real de los riesgos del paciente, las intervenciones propuestas y la efectividad de las medidas de prevención, además de promover la integración y el empoderamiento de los profesionales en la gestión del cuidado. (AU)

Automatic identification of incidents involving potential serious injuries and fatalities (PSIF).

Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset.

What We Do, What We Say, What We Don't Say: Confidentiality In The Publication Of Clinical Writing.

Questions concerning analysts' publication of material from the analyses of their patients have troubled the field of psychoanalysis since its inception. Disguise inevitably distorts the clinical material and is often insufficient to protect the patient from recognition. Asking the patient's consent for publication intrudes upon and alters the analytic process. While analysts have largely reached a consensus about the need for anonymity in published material, there is still considerable debate about the necessity for obtaining patients' consent when using their material for publication. In this paper, I will trace the evolving meanings of disguise, and particularly of consent, in the analytic literature. I will place a particular emphasis upon the differing theoretical belief systems that underlie the analyst's decision to ask consent from her patient or not to do so, and I will argue that, although decisions on asking consent remain a complex matter, such coherent belief systems should play an important part in analysts' decisions regarding consent. I will illustrate my thought processes and some clinical situations with brief examples, and I will conclude with some practical recommendations, with the hope that these will stimulate further discussion in the analytic community.