Safety Science最新文献

{"title":"A scoping review of organizational resilience measurement – instruments, indicators, and implications","authors":"Hsuan-Hao Lo ,&nbsp;Sheuwen Chuang ,&nbsp;Cheng-Yu Lin","doi":"10.1016/j.ssci.2026.107114","DOIUrl":"10.1016/j.ssci.2026.107114","url":null,"abstract":"<div><div>As organizations navigate an era of unprecedented disruption, organizational resilience has emerged as a critical capability, prompting corresponding growth in instruments for its measurement. This paper explores the organizational resilience measurement (ORM) literature through a scoping review, systematically mapping the origins, evolution, and features of its primary instruments. We searched three academic databases (Scopus, Web of Science, PubMed) for literature published through February 2025, augmented by a manual review of seminal publications, yielding a final corpus of 55 articles. Our findings indicate a field dominated by two instruments: the Resilience Assessment Grid (RAG) (49.1%) and the Benchmark Resilience Tool (BRT) (20.0%). Application of these tools is highly concentrated, with the healthcare sector accounting for 36.4% of studies. Comparative analysis reveals that the two dominant tools represent distinct philosophical approaches: RAG offers contextual, in-depth diagnosis of system potentials, while BRT provides standardized, cross-organizational benchmarking of organizational management. The study concludes that the ORM field may be maturing and consolidating around a two-paradigm model, raising intriguing questions about whether sequential integration of these instruments could offer advantages beyond their individual applications.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107114"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in hospital fall Prevention: Current Applications, Challenges, and Future Directions","authors":"Adewoyin A. Osonuga ,&nbsp;Ayokunle Osonuga ,&nbsp;Deborah Omeni ,&nbsp;Gloria C. Okoye ,&nbsp;Eghosasere Egbon ,&nbsp;David B. Olawade","doi":"10.1016/j.ssci.2025.107104","DOIUrl":"10.1016/j.ssci.2025.107104","url":null,"abstract":"<div><div>Hospital falls represent a critical patient safety challenge, affecting millions of patients globally and resulting in substantial morbidity, mortality, and healthcare costs. Traditional fall prevention strategies, whilst beneficial, often lack the precision and real-time responsiveness needed for optimal patient protection. This narrative review with systematic search examines the current applications, effectiveness, and implementation challenges of artificial intelligence (AI) technologies in hospital fall prevention. A comprehensive search was conducted across PubMed, EMBASE, IEEE Xplore, and Google Scholar databases from January 2015 to October 2024. AI technologies demonstrate promise across four primary domains: machine learning predictive models achieving AUROC of 0.85–0.97 (with calibration reported variably), computer vision systems enabling real-time behavioural monitoring (94–97% detection accuracy in controlled settings), sensor-based technologies providing continuous patient surveillance (89–96% accuracy with multi-sensor fusion), and natural language processing enhancing risk factor extraction from clinical documentation (sensitivity 95% CI in selected studies). These metrics represent primarily single-site, retrospective studies with limited external validation and variable baseline fall rates. Successful implementations report fall reduction rates of 0.9–1.2 falls per 1,000 patient-days (15–40% relative reduction) across various healthcare settings, though baseline rates ranged from 2.8 to 5.1 falls per 1,000 patient-days across different care settings, and secular trends and study design heterogeneity limit causal inference. AI-driven systems offer enhanced prediction accuracy, real-time monitoring capabilities, and personalised risk assessment. However, implementation challenges include alarm fatigue (alert rates and positive predictive value rarely reported), algorithmic bias requiring ongoing fairness audits, liability concerns when AI systems fail to prevent falls, data privacy concerns, integration complexities, clinical workflow adaptation, and substantial cost barriers for smaller institutions. Future developments should prioritize explainable AI systems, multisite external validation with standardised metrics (AUROC, AUPRC, calibration), federated learning approaches, and implementation trials examining both fall rates and care process outcomes.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107104"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping micro-mobility risk: AI-powered geospatial analysis and predictive modelling","authors":"Ömer Kaya ,&nbsp;Nuriye Kabakuş","doi":"10.1016/j.ssci.2025.107107","DOIUrl":"10.1016/j.ssci.2025.107107","url":null,"abstract":"<div><div>Micro-mobility vehicles have rapidly become widespread as a sustainable and practical alternative for urban transportation in recent years. In this study, micro-mobility vehicles refer to traditional bicycles, electric bicycles, and electric scooters, which represent the main categories of such modes involved in traffic crashes in Türkiye. Despite their growing popularity, the safety implications of these vehicles have not yet been fully understood, and comprehensive research addressing crash patterns and associated risk factors is required. To this end, this study employs an artificial intelligence-driven geospatial and statistical methodology. Crash reports involving micro-mobility vehicles in Türkiye between 2015 and 2023 were analysed. Seventeen independent variables and 102 sub-variables were identified and integrated into a GIS environment for spatial analysis. The impact levels of risk factors were assessed using six different Large Language Models (DeepSeek, GEMINI, Perplexity, ChatGPT, Copilot, and Poe). Crash risk maps and corresponding weight values were combined to produce an crash suitability map indicating the potential risk of micro-mobility crashes. Furthermore, the significance of these factors across different collision types was tested using a multinomial logistic regression model. To the best of the authors’ knowledge, this is the first study to apply a macro-scale dataset and an AI-enhanced geospatial decision-making approach to analyse micro-mobility crashes. The findings highlight the need for local governments and urban planners to implement targeted safety measures in regions with high crash potential.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107107"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of data censorship on pre-movement time prediction in building fire evacuation: focusing on people with learning disabilities","authors":"Naser Kazemi Eilaki ,&nbsp;Trond Nordvik ,&nbsp;Carolyn Ahmer ,&nbsp;Ilona Heldal ,&nbsp;Håkan Frantzich ,&nbsp;Bjarne Christian Hagen","doi":"10.1016/j.ssci.2025.107103","DOIUrl":"10.1016/j.ssci.2025.107103","url":null,"abstract":"<div><div>In fire evacuations, pre-movement time (PT) is an important part of the total egress time. Data from fire drills is commonly used to investigate occupant behavior, including PT, but these data are often subjected to censoring. This study examines the impact of data censoring on PT analysis during fire evacuation drills, which is important for predicting overall evacuation time. It identifies various types of data censorship and highlights the importance of accounting for censored data in statistical analyses. The research utilizes the Accelerated Failure Time model (AFT), a parametric survival analysis method, to address data censoring issues. Data from two fire evacuation drills in residential buildings with occupants with learning disability were analyzed using two scenarios: one excluding censored data and another incorporating it. The results demonstrate that accounting for censored data significantly alters the interpretation of the causal model, with covariates such as fire incident time emerging as significant only when censored data is considered. The AFT model effectively manages censored data by updating predictions for observed events and extrapolating values for censored observations using the best-fit distribution. This study underscores the importance of incorporating censored data into evacuation models emphasizing that excessive data censoring reduces the validity of AFT model predictions. Although limited by sample size, these findings offer valuable insights into effective covariates influencing PT and provide guidance for future evacuation modeling and simulation tools.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107103"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural causality hierarchy analysis: A novel data-driven integrated method for assessing occupational accidents in maritime transportation","authors":"Xinjian Wang ,&nbsp;Guohong Zhao ,&nbsp;Liang Cao ,&nbsp;Jilong Yu ,&nbsp;Yongqiang Li ,&nbsp;Hongzhen Wang ,&nbsp;Wenjie Cao","doi":"10.1016/j.ssci.2025.107086","DOIUrl":"10.1016/j.ssci.2025.107086","url":null,"abstract":"<div><div>The growing complexity of transportation systems necessitates advanced, data-driven methodologies for the accurate identification and systematic analysis of risk factors in occupational accidents. Although Generative Artificial Intelligence (GAI) demonstrates transformative potential for safety enhancement, its effective implementation in high-stakes domains like maritime transport remains constrained by the lack of structured, domain-specific causal knowledge. To bridge this gap, this study proposes a novel analytical framework that effectively generates an interpretable causal hierarchy from accident data, serving as a foundational knowledge base for future GAI systems. Leveraging the European maritime occupational accidents case database, this study proposes an integrated methodology that incorporates association rule mining, the weighted influence non-linear gauge system, and the adversarial interpretive structure model. This framework is designed to systematically identify risk factors and elucidate their complex multidimensional interactions. The findings reveal the interaction patterns among risk factors in maritime occupational accidents, identify dynamic risk evolution pathways initiated by specific factor combinations, and demonstrate the practical applicability and effectiveness of the integrated framework for risk identification and accident mechanism analysis. These results provide a solid theoretical foundation and practical guidance to support of maritime transportation enterprises in developing customized, dynamic safety management systems. Furthermore, the proposed data-driven framework not only advances risk assessment techniques but also establishes a structural foundation conducive to future integration with GAI-driven decision support systems, thereby contributing to the overarching goals of enhancing transportation safety and resilience.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107086"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delphi assessment of occupational hazards in Spanish dentists","authors":"Carmen Anaya-Aguilar ,&nbsp;Eva Rosel ,&nbsp;Alberto Rodríguez-Archilla ,&nbsp;Rosa Anaya-Aguilar ,&nbsp;Manuel Bravo ,&nbsp;Yolanda Martínez-Beneyto","doi":"10.1016/j.ssci.2025.107091","DOIUrl":"10.1016/j.ssci.2025.107091","url":null,"abstract":"<div><div>Despite technological advancements in dentistry, professionals remain exposed to numerous occupational hazards that compromise their health and well-being. These include chronic exposure to hazardous chemicals, aerosols, ionizing radiation, excessive noise, and significant ergonomic and psychosocial stressors.</div><div>A review of current scientific literature reveals persistent challenges for dental professionals, such as daily contact with harmful substances, invasive procedures, contaminated aerosols, radiation, noise, and a high prevalence of musculoskeletal and psychosocial disorders. These risks persist despite technological improvements and continue to hinder the development of a strong culture of occupational health and safety within the profession.</div><div>This study aimed to assess occupational risks in dental clinics by analyzing expert opinion through the Delphi method. A panel of 18 experts, each with over ten years of experience, participated. Results indicated that “rotary dental instruments (e.g., turbines, contra-angle handpieces)” were consistently rated as the highest risk (mean score: 3.50). In the deviation category, the “risk of SARS-CoV-2 transmission due to inappropriate PPE use” received the highest scores (3.83 and 3.61). In the contact category, “exposure to sharp, cutting, or abrasive instruments/materials” was rated highest (3.94 and 3.78).</div><div>The study concludes that rotary instruments and sharp tools contribute significantly to the generation of potentially pathogenic bioaerosols and elevate the risk of infection. While strict safety protocols are essential, they may impair working conditions by reducing visibility, fogging protective eyewear, limiting breathability, and increasing humidity factors associated with decreased efficiency and increased stress among dental professionals.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107091"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The dose-response relationship between work hours and mental health in healthcare workers: a cross-sectional study in a Chinese medical consortium","authors":"Yuting Tang,&nbsp;Min Zhang,&nbsp;Yiming Huang,&nbsp;Fuyuan Wang,&nbsp;Chuning He,&nbsp;Xinxin Fang,&nbsp;Xuechun Wang,&nbsp;Yiran Zhang","doi":"10.1016/j.ssci.2025.107093","DOIUrl":"10.1016/j.ssci.2025.107093","url":null,"abstract":"<div><h3>Background</h3><div>Long working hours and overtime among healthcare workers (HCWs) have become a global occupational health concern. Evidence on dose–response thresholds and subgroup vulnerabilities remains limited, particularly in middle-income settings. This study examined associations between weekly working hours and mental health outcomes in a Chinese medical consortium.</div></div><div><h3>Methods</h3><div>A cross-sectional survey was conducted among 983 HCWs from four hospitals in Southern China (June–July 2023; response rate 92.1 %). Weekly working hours were classified using WHO/ILO cut-points (35–40, 41–48, 49–54, ≥55 h/week). Burnout, depression, sleep troubles, stress, somatic stress, and cognitive stress were measured using the COPSOQ III. Multivariable regression estimated associations, with subgroup analyses by gender, shift status, and exposure duration.</div></div><div><h3>Results</h3><div>A clear dose–response pattern was observed, with risk thresholds at 49 and 55 h/week. Compared with 35–40 h/week, working ≥ 55 h/week was associated with higher burnout (<em>β</em> = 4.23, 95 %CI 0.17–8.30), stress (<em>β</em> = 4.25, 95 %CI 0.24–8.26), and somatic stress (<em>β</em> = 5.30, 95 %CI 1.22–9.38) after adjustment. Female HCWs showed elevated risks at 49–54 h/week, whereas male HCWs mainly deteriorated ≥ 55 h/week. Shift workers demonstrated increased somatic stress from 49–54 h/week, with widespread adverse effects ≥ 55 h/week. Longer exposure duration further lowered tolerance thresholds. Work pace emerged as a robust independent correlate across outcomes.</div></div><div><h3>Conclusions</h3><div>Long working hours were linked to poorer mental health, with 49 and 55 h/week as warning thresholds. A triple burden of prolonged hours, high work pace, and heavy demands magnifies risk. Vulnerabilities varied by gender, shift status, and exposure duration, underscoring the need for tailored protections.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107093"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential cognitive control in response to task severity during technologically-induced concurrent multitasking: The role of risk awareness in ICU nurses","authors":"Huiling Hu ,&nbsp;Tingting Feng ,&nbsp;Hui Ge ,&nbsp;Jiashuai Li ,&nbsp;Xue Wu ,&nbsp;Xuanna Wu","doi":"10.1016/j.ssci.2026.107112","DOIUrl":"10.1016/j.ssci.2026.107112","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate how novice Intensive Care Unit (ICU) nurses engage cognitive control mechanisms when managing multitasking conflicts of differing severity, and how risk awareness modulates these processes.</div></div><div><h3>Background</h3><div>Digital transformation in healthcare has introduced more technological systems into ICUs, heightening multitasking demands and cognitive load. Novice nurses, lacking well-developed clinical schemas, rely more on executive control. Understanding how cognitive control is deployed—and how individual risk awareness shapes these processes—is essential for safeguarding patient safety in high-risk environments.</div></div><div><h3>Design</h3><div>EEG-based experimental study.</div></div><div><h3>Methods</h3><div>Twenty-six novice ICU nurses participated in a simulated technologically-induced multitasking paradigm. Behavioral accuracy, response times, and EEG measures (P1/P3 components, oscillatory power in alpha, beta, and theta bands) were recorded. Risk awareness was assessed via self-report scales.</div></div><div><h3>Results</h3><div>Nurses achieved higher accuracy in high-severity tasks, though response times did not differ significantly. ERP analyses showed significantly lower P1 peak amplitudes for high-severity tasks, particularly among nurses with higher risk awareness. Time–frequency analyses revealed greater alpha, beta, and theta power during low-severity tasks, suggesting increased cognitive flexibility under lower demands. Significant task severity × risk awareness interactions indicated that individuals with higher risk awareness deployed more targeted neural resources under high-severity conditions.</div></div><div><h3>Conclusions</h3><div>Task severity and individual risk awareness jointly shape cognitive control in technologically mediated multitasking among novice ICU nurses. These findings highlight the importance of integrating neurocognitive evidence into simulation-based training and interface design to strengthen nurses’ attentional management, reduce error risks, and enhance patient safety in the digital age of healthcare.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107112"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-related directive speech acts: A vignettes study on differences in leader and follower communication in the German rescue and fire services","authors":"Marc Schütte,&nbsp;Markus Wiedemann","doi":"10.1016/j.ssci.2025.107092","DOIUrl":"10.1016/j.ssci.2025.107092","url":null,"abstract":"<div><div>Tactical teams in fire and rescue services must be capable of adapting to hazardous environments. A key prerequisite for adaptability in the interest of reliability and safety is the ability of team members to actively engage in directive speech acts. That is, to signal potential threats and initiate corrective action. This study compares directive speech acts across the two domains, focusing on risk, team role (leader versus follower), and individual characteristics. A logistic mixed regression approach was applied to data from an online survey. Four models were generated to test two interaction hypotheses: domain × role (H1) and domain × follower characteristics (H2). The sample consisted of 822 members of fire and rescue services, each evaluating three domain-specific and risk-graded scenarios (vignettes). The findings indicate that speech acts of leaders and followers are symmetrical in the rescue service, whereas they follow a complementary pattern in the fire service (H1). In both domains, followers adjust their responses according to perceived risk. Regarding individual characteristics (H2), age emerged as predictor of speak-up in the fire service. Notably, perceived psychological safety did not predict speaking up in either domain. The use of directive speech acts differs between domains, reflecting culturally embedded role expectations. In the rescue service, these are shaped by interprofessional dynamics, particularly negotiation of authority between physicians and paramedics – whereas in the fire service, directive speech acts align with hierarchical structures within a single profession. Safety interventions aimed at improving directive speech acts must be context- and culture-sensitive.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107092"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a televisiting program in a tertiary university hospital: Failure modes and effect analysis (FMEA) and solutions for improving patient safety and sustainable care","authors":"Riccardo Tartaglia ,&nbsp;Chiara Parretti ,&nbsp;Giuseppe Candido ,&nbsp;Micaela La Regina ,&nbsp;Franco Scaldaferri ,&nbsp;Gabriele Rumi ,&nbsp;Daniele Napolitano ,&nbsp;Giuseppe Vetrugno ,&nbsp;Paul Barach","doi":"10.1016/j.ssci.2025.107096","DOIUrl":"10.1016/j.ssci.2025.107096","url":null,"abstract":"<div><div>The World Health Organization (WHO) emphasizes the importance of digital solutions such as telemedicine for improving care quality and safety. Recent surveys reveal the limited adoption in many countries including Italy, particularly in regions with mature healthcare systems. The aim of our study was to identify the critical barriers to the implementation of telemedicine visits and propose robust solutions to mitigate the risks associated with the successful rollout and utilization of sustainable telemedicine.</div><div>We assessed the introduction of telemedicine services for remote follow-up care for chronic gastrointestinal inflammatory bowel disease (IBD) and measured its impact on the risks of patient hospitalizations. We used the Failure Mode and Effects Analysis (FMEA) method to measure the clinical process and identify potential failures and assigned a Risk Priority Number (RPN) to each step. FMEA is a systematic process that involves selecting defined processes, assembling a multidisciplinary team, and analyzing potential failures by establishing links between failure modes, their effects, and causes. The team included medical and nursing leaders and coordinators, and human factors methodology experts, who mapped the care processes and identified potential failure modes for each process. We ranked the failure modes based on severity, frequency, and detectability, and proposed clinical process redesigns to mitigate or prevent failures that could lead to patient harm.</div><div>We identified 6 main process phases for introducing a televisiting service for patients with chronic inflammatory bowel disease (IBD). The FMEA risk analysis highlighted the criticalities as steps with the highest priority: failure to conduct the visit due to programming error (RPN 450), failure to send the final report (RPN 390), privacy issues during the visit (RPN 350), and appointment booking problems (RPN 340) at the central booking center.</div><div>Implementing a telemedicine program requires a comprehensive approach that includes identifying key clinical and organizational issues impacting system usability; targeted training of healthcare staff; developing clear policies and procedures; enhancing digital skills and addressing access concerns among citizens using the system; prioritizing patient care and integrating seamlessly with existing systems within the hospital. The disparities between hospital managers and physicians in evaluating the failure risks highlight the key differences in their risk perceptions. Comparisons with similar cases in the literature are necessary.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"196 ","pages":"Article 107096"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}