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.

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.

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.

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.

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)

Management of adverse events in a Moroccan regional hospital: a state of art and perspectives.

Introduction: the risk management system is useful to identify, analyze, and reduce the risk occurrence of adverse events (AEs) in health services. This system suggests useful improvements to patients and to the whole institution and also contributes to the acquisition of a collective and organizational safety culture. This study presented a state of the art of the management of AEs identified in different services of a regional hospital in the north of Morocco. Methods: this is a retrospective cross-sectional exploratory study carried out from 2017 to 2019 using observations and semi-structured interviews, which were recorded, re-transcribed, and analyzed. Data was also collected from audit reports, results of investigations of the nosocomial infection control committee and the risk management commission, AEs declaration sheets, and meetings reports. Results: a number of 83 AEs were recorded, 10 of which were urgent. The reported events were related to care, infection risk, the drugs circuit, and medico-technical events. Two hundred cases of nosocomial infections were also recorded, of which 75 occurred in the intensive care unit and 35 in the maternity service. Surgical site infections were the most frequently reported complication. Adverse events were related to organizational failure, equipment problems, and errors related to professional practices. Conclusion: our findings may guide the improvement of the event management system in order to reduce the occurrence of future incidents. Thus, improving the risk management system requires setting up training strategies for staff on the importance of this system and its mode of operation.

Racial disparities in breast cancer risk factors and risk management.

Racial disparities in breast cancer outcomes are well described across the spectrum of screening, diagnosis, treatment, and survivorship. Breast cancer mortality is markedly elevated for Non-Hispanic Black women compared with other racial and ethnic groups, with multifactorial causes. Here, we aim to reduce this burden by identifying disparities in breast cancer risk factors, risk assessment, and risk management before breast cancer is diagnosed. We describe a reproductive profile and modifiable risk factors specific to the development of triple-negative breast cancer. We also propose that screening strategies should be both risk- and race-based, given the prevalence of early-onset triple-negative breast cancer in young Black women. We emphasize the importance of early risk assessment and identification of patients at hereditary and familial risk and discuss indications for a high-risk referral. We discuss the subtleties following genetic testing and highlight "uncertain" genetic testing results and risk estimation challenges in women who test negative. We trace aspects of the obesity epidemic in the Black community to infant feeding patterns and emphasize healthy eating and activity. Finally, we discuss building an environment of trust to foster adherence to recommendations, follow-up care, and participation in clinical trials. Addressing relevant social determinants of health; educating patients and clinicians on factors impacting disparities in outcomes; and encouraging participation in targeted, culturally sensitive research are essential to best serve all communities.

Bibliometric and visualization analysis of risk management in the doctor-patient relationship: A systematic quantitative literature review.

OBJECTIVES: This paper analyzed the research on risk management in the doctor-patient relationship (DPR) based on a systematic quantitative literature review approach using bibliometric software. It aims to uncover potential information about current research and predict future research hotspots and trends. METHODS: We conducted a comprehensive search for relevant publications in the Scopus database and the Web of Science Core Collection database from January 1, 2000 to December 31, 2023. We analyzed the data using CiteSpace 6.2.R2 and VOSviewer 1.6.19 software to examine the annual number of publications, countries/regions, journals, citations, authors, and keywords in the field. RESULTS: A total of 553 articles and reviews that met the criteria were included in this study. There is an overall upward trend in the number of publications issued; in terms of countries/regions, the United States and the United Kingdom are the largest contributors; Patient Education and Counseling is the most productive journal (17); Physician communication and patient adherence to treatment: a meta-analysis is the most cited article (1637); the field has not yet to form a stable and obvious core team; the analysis of high-frequency keywords revealed four main research directions: the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the keyword burst analysis revealed several shifts in the research hotspots for risk management in the DPR, suggesting that chronic disease management, is a future research direction for the continued development of risk management in the DPR. CONCLUSIONS: The visualization analysis of risk management literature in the DPR using CiteSpace and VOSviewer software provides insights into the current research status and highlights future research directions.

Development of a scoring system to quantify errors from semantic characteristics in incident reports.

OBJECTIVES: Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques. METHODS: We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'. RESULTS: Overall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d=0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments. CONCLUSION: Our error scoring system could provide insights to improve patient safety using aggregated incident report data.

Developing an Ontology Representing Fall Risk Management Domain Knowledge.

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults.

Risk management and empirical study of the doctor-patient relationship: based on 1790 litigation cases of medical damage liability disputes in China.

BACKGROUND: Compensation for medical damage liability disputes (CMDLD) seriously hinders the healthy development of hospitals and undermines the harmony of the doctor-patient relationships (DPR). Risk management in the DPR has become an urgent issue of the day. The study aims to provide a comprehensive description of CMDLD in China and explore its influencing factors, and make corresponding recommendations for the management of risks in the DPR. METHODS: This study extracted data from the China Judgment Online - the official judicial search website with the most comprehensive coverage. Statistical analysis of 1,790 litigation cases of medical damage liability disputes (COMDLD) available from 2015 to 2021. RESULTS: COMDLD generally tended to increase with the year and was unevenly distributed by regions; the compensation rate was 52.46%, the median compensation was 134,900 yuan and the maximum was 2,234,666 yuan; the results of the single factor analysis showed that there were statistically significant differences between the compensation for different years, regions, treatment attributes, and trial procedures (P < 0.05); the correlation analysis showed that types of hospitals were significantly negatively associated with regions (R=-0.082, P < 0.05); trial procedures were significantly negatively correlated with years (R=-0.484, P < 0.001); compensat- ion was significantly positively correlated with years, regions, and treatment attributes (R = 0.098-0.294, P < 0.001) and negatively correlated with trial procedures (R=-0.090, P < 0.01); regression analysis showed that years, treatment attributes, and regions were the main factors affecting the CMDLD (P < 0.05). CONCLUSIONS: Years, regions, treatment attributes, and trial procedures affect the outcome of CMDLD. This paper further puts forward relevant suggestions and countermeasures for the governance of doctor-patient risks based on the empirical results. Including rational allocation of medical resources to narrow the differences between regions; promoting the expansion and sinking of high-quality resources to improve the level of medical services in hospitals at all levels; and developing a third-party negotiation mechanism for medical disputes to reduce the cost of medical litigation.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine.

BACKGROUND: The nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PURPOSE: This study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. METHODS: Failure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FINDINGS: Results prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). CONCLUSIONS: Despite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

Research on risk management incentive strategy based on the green financial ecosystem.

Taking the green financial ecosystem composed of innovators, green financial institutions and regulators as the object of research, it explores the issue of how to improve the level of efforts of the three types of subjects and the benefits of risk management in the green financial ecosystem. The optimal level of effort, optimal level of return, and optimal level of return on risk management of green financial ecosystems for innovators, green financial institutions, and regulators under the three modes of No-incentive Contract, Cost-sharing Contract, and Synergistic Cooperation Contract are investigated and analyzed respectively, and verified by numerical simulation analysis. The results show: (1) Compared to the No-incentive Contract, the Cost-sharing Contract and the Synergy Cooperation Contract generate more significant incentives, and returns increase over time in both models. (2) The effort level of the participating subjects under the Synergistic Cooperation Contract is the highest, which can realize the Pareto optimization of the participating subjects and the green financial ecosystem at the same time. The study's findings contribute to a deeper understanding of cooperation among innovators, green financial institutions and regulators in facilitating risk management in green financial ecosystems and provide a realistic reference for risk managers in green financial ecosystems.

Analysis of Critical Incident Reports Using Natural Language Processing.

A Critical Incident Reporting System (CIRS) collects anecdotal reports from employees, which serve as a vital source of information about incidents that could potentially harm patients. OBJECTIVES: To demonstrate how natural language processing (NLP) methods can help in retrieving valuable information from such incident data. METHODS: We analyzed frequently occurring terms and sentiments as well as topics in data from the Swiss National CIRRNET database from 2006 to 2023 using NLP and BERTopic modelling. RESULTS: We grouped the topics into 10 major themes out of which 6 are related to medication. Overall, they reflect the global trends in adverse events in healthcare (surgical errors, venous thromboembolism, falls). Additionally, we identified errors related to blood testing, COVID-19, handling patients with diabetes and pediatrics. 40-50% of the messages are written in a neutral tone, 30-40% in a negative tone. CONCLUSION: The analysis of CIRS messages using text analysis tools helped in getting insights into common sources of critical incidents in Swiss healthcare institutions. In future work, we want to study more closely the relations, for example between sentiment and topics.

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks.

This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network's non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model's performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study's model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.