High-risk Scenarios Research Articles

Tramadol utilization among patients with higher risk of adverse drug events: A claims analysis of commercially insured and Medicare Advantage members.

To assess prescribing of tramadol among patients with contraindications and higher risks of adverse events in a large population of commercially insured and Medicare Advantage members. We performed a cross-sectional analysis evaluating tramadol utilization in patients with higher risk of adverse outcomes. This study utilized 2016-2017 data from the Optum Clinformatics Data Mart. Patients with at least one tramadol prescription without a cancer or sickle cell diagnosis during the study period. We first determined if tramadol was prescribed among patients with contraindications or risk factors for adverse outcomes. We then determined if patient demographic or clinical factors were associated with the use of tramadol in these higher-risk scenarios using multivariable logistic regression models. Among patients with at least one prescription for tramadol, 19.66 percent (99 percent CI: 19.57-19.75) concurrently received an interacting cytochrome P450 isoenzyme medication, 19.24 percent (99 percent CI: 19.15-19.33) concurrently received a serotonergic medication, and 7.93 percent (99 percent CI: 7.88-8.00) concurrently received a benzodiazepine. Additionally, 1.59 percent (99 percent CI: 1.56-1.61) of patients who received tramadol also had a seizure disorder, while 0.55 percent (99 percent CI: 0.53-0.56) of patients were under the age of 18. Overall, nearly one in three patients (31.17 percent) received tramadol in the presence of at least one of these risks (99 percent CI: 31.06-31.27). Almost one in three patients prescribed tramadol had a clinically significant drug interaction or contraindication for use, suggesting that prescribers often disregard these concerns. Real-world studies are needed to better understand the likelihood of harms associated with the use of tramadol in these contexts.

A First-Class Simulation: In-Situ In-Flight Medical Emergencies Curriculum for Emergency Medicine Residents Aboard a Commercial Airliner.

In-flight medical emergencies occur in an estimated one out of 604 flights. Responding in this environment poses a unique set of challenges unfamiliar to most emergency medicine (EM) providers, including physical space and resource limitations. We developed a novel high-fidelity in-situ training curriculum focused on frequent or high-risk in-flight medical scenarios while replicating this austere environment. Our residency program coordinated with our local airport's chief of security and an airline-specific station manager to arrange the use of a grounded Boeing 737 commercial airliner during late evening/early morning hours. Eight stations reviewing in-flight medical emergency topics were reviewed, five of which were simulation scenarios. We created medical and first-aid kits that reflect equipment used by commercial airlines. Residents' self-assessed competency and medical knowledge were assessed both initially and post-curriculum using a standardized questionnaire. Forty residents attended the educational event as learners. Self-assessed competency and medical knowledge increased after curriculum participation. All tested aspects of self-assessed competency had a statistically significant increasefrom a mean of 15.04 to 29.20 out of a maximum score of forty. The mean medical knowledge score increased from 4.65 to 6.93 out of 10 maximum points. A five-hour in-situ curriculum for reviewing in-flight medical emergencies increased self-assessed competency and medical knowledge for EM and EM-internal medicine residents. The curriculum was overwhelmingly well-received by learners.

Driving Decisions for Autonomous Vehicles in Intersection Environments: Deep Reinforcement Learning Approaches with Risk Assessment

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed.

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

BackgroundWhen treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes toward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma. MethodsThree clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neoadjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents’ attitudes toward neoadjuvant therapy. ResultsThe lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P < .001). The regret of the commission was diametrically opposite to the regret of omission (P ≤ .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P ≤ .001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy. ConclusionRegret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.

Murine cytomegalovirus reactivation concomitant with acute graft-versus-host disease is controlled by antibodies.

Reactivation of human cytomegalovirus (HCMV) from latency is a frequent complication following hematopoietic stem cell transplantation (HSCT). The development of acute graft-versus-host disease (GVHD) is a significant risk factor for HCMV disease. Using a murine GVHD model in animals latently infected with murine CMV (MCMV), we studied preventive and therapeutic interventions in this high-risk scenario of HSCT. Mice latently infected with MCMV experienced reactivated MCMV and developed disseminated MCMV infection concomitant with the manifestations of GVHD. Dissemination was accompanied by accelerated mortality. We demonstrate that MCMV reactivation and dissemination was modulated by MCMV-specific antibodies, thus demonstrating in vivo protective activity of antiviral antibodies. However, the efficacy of serum therapy required repetitive doses of high-titer immune serum secondary to the shortened serum half-life of IgG in animals with GVHD. In a complementary approach, treatment of GVHD by adoptive transfer of donor-derived Tregs facilitated production of MCMV-specific antibodies from newly developing donor-derived B cells. Together, our findings strongly suggest that antibodies play a major role in controlling recurrent MCMV infection that follows GVHD, and they argue for reassessing the potential of antibody treatments as well as therapeutic strategies that enhance de novo antibody development against HCMV.

Interpretable Image Recognition Models for Big Data With Prototypes and Uncertainty

Deep neural networks have been applied to big data scenarios and have achieved good results. However, trust in high-accuracy deep neural networks is not necessarily achieved when facing high-risk decision scenarios. Every wrong decision has incalculable consequences in medicine, autonomous driving, and other fields. For an image recognition model based on big data to gain trust, it is necessary to simultaneously solve the interpretability of decisions and risk predictability. This paper introduces uncertainty into a self-explanatory image recognition model to show that the model can interpret decisions and predict risk. This approch enables the model to trust its decisions and explanations and to provide early warning and detailed analysis of risky decisions. In addition, this paper introduces the process of using uncertainty and explanations to achieve model optimization, which will significantly improve the application value of the model in high-risk scenarios. The scheme proposed in this paper solves the trust crisis caused by using black box image recognition models.

The impact of retrofitted ventilation approaches on long-range airborne infection risk for lecture room environments: design stage methodology and application

There is a continued need to improve the energy (and by extension carbon) performance of the existing building stock in Europe and globally. Recent experiences with the COVID-19 pandemic have demanded even more of the built environment disciplines when it comes to air quality and ventilation rates particularly in teaching environments which are seen as necessary developmental sectors for the learning of the population. As such it is important that these spaces are safe for students and staff where, until now, infectious disease risk assessments were not typical. In addition to this, there is a risk that envelope refurbishment (which is critical for energy performance) without the provision of adequate ventilation may lead to high-risk scenarios for occupants in university spaces and additional measures may be required. This study presents a design stage risk assessment methodology and applies this to evaluate different retrofitted ventilation approaches combined with additional control measures for a lecture room environment. A real case study lecture room in Ireland was used to demonstrate the methodology for evaluating the airborne infectious disease risk (using the Wells-Riley model) under different ventilation approaches (natural and mechanical), infiltration rates (existing and upgraded), class sizes and times, mask efficiencies as well as the use of an air cleaner. The methodology that was adopted was shown to be flexible and capable of considering a wide range of different retrofit and user specific combinations. The results from the evaluation indicate a wide range in event specific reproductive numbers, however, the use of a well-designed natural or mechanical ventilation system was seen as significant in stabilising or suppressing virus transmission and reducing the likelihood of highly reproductive events. It was found that combinations of measures with ventilation were the most effective at suppressing reproductive numbers. It is recommended that during known pandemics (in the short-term) infectious control strategies should use filtration (through masks or air cleaners) and adequate ventilation. Long-term refurbishment strategies should consider the provision of hybrid ventilation systems which integrate key energy saving measures (such as heat recovery and demand-controlled ventilation), virus control (such as HEPA filtration) and low energy cooling solutions (such as passive cooling).

Looking for Possible Benefits of Combining Short-Chain Fructo-Oligosaccharides (scFOS) with Saccharomyces cerevisiae Sc 47 on Weaned Pigs Orally Challenged with Escherichia coli F4.

The objective of this work was to evaluate the effect of supplementing short-chain fructo-oligosaccharides (scFOS) combined or not with live yeast Saccharomyces cerevisiae Sc 47 on weanling pigs challenged with Escherichia coli F4+. We allocated ninety-six piglets to four experimental diets: control (CTR); supplemented with scFOS (5 g/kg Profeed® P95) (scFOS); S. cerevisiae Sc 47 (1 g/kg Actisaf® Sc 47 HR +) (YEA); or both (SYN). Parameters included: performance; E. coli F4+ detection; fermentation activity; inflammatory biomarkers; and ileal histomorphology. Our results showed that supplementing scFOS was able to reduce the incidence of diarrhea, and both supplements were able to lower counts of EHEC along the gut. Supplementing scFOS was mostly associated with changes in the gut ecosystem and increases in the lactobacilli population, while S. cerevisiae Sc 47 registered increases in the numbers of ileal intraepithelial lymphocytes. The synbiotic mixture showed the lowest diarrhea incidence and fecal scores, benefiting from complementary modes of action and possible synergistic effects due to a hypothesized yeast-LAB cross-feeding phenomenon in the foregut. In conclusion, our results evidence that supplementing scFOS or Saccharomyces cerevisiae Sc 47 is efficacious to fight post-weaning colibacillosis, and combining both could be beneficial in high-risk scenarios.

A Fuzzy Profit Maximization Model Using Communities Viable Leaders for Information Diffusion in Dynamic Drivers Collaboration Networks

Assigning shipping orders to the most appropriate driver in the shortest time but with the highest profit is one of the major concerns of transportation companies. Many studies have been conducted on transportation service procurement systems; however, due to the lack of a framework for modeling human interactions, none of them has used the concept of information diffusion for this purpose. In this article, a monoplex weighted drivers’ collaboration network is developed to model drivers’ relationships within the transportation system. Besides, to identify and track communities during a given time interval of the network, a new community detection algorithm, called dynamic overlapping community detection (DOCD) algorithm, is designed, which can identify viable leaders in each community. In addition to detecting community leaders, the algorithm is able to monitor, assess, and detect the durability of these community leaders over time, which other algorithms are not able to. To evaluate the performance of the algorithm, it is compared with five different algorithms in terms of 14 evaluation measures. The results show the proposed DOCD algorithm outperforms the other algorithms with 88% superiority in the evaluation measures. Then, a fuzzy profit maximization model is developed using information diffused by the identified communities’ viable leaders and information diffusion power of each community. Analyzing a real case study obtains two achievements in the form of “high-risk scenario” and “low-risk scenario” for well-known and novice transportation companies, respectively. Therefore, the obtained results show that transportation companies allocate orders to drivers based on their reputation and risk levels.

Numerical modeling of a sneeze, a cough and a continuum speech inside a hospital lift

The global COVID-19 and its variants put us on notice of the importance of studying the spread of respiratory diseases. The most common means of propagation was the emission of droplets due to different respiration activities. This study modeled by computational fluid dynamics (CFD) techniques a high risk scenario like a hospital elevator. The cabin was provided with an extraction fan and a rack for air renewal. Inside, a sneeze, a cough and a continuum speech were simulated. Inside the lift, two occupants were introduced to observe the risk of propagation of emitted droplets and the impact in diseases spreading risk. The fan effectivity over the droplets ejection was analyzed, as well as environmental condition of a clinical setting. For this purpose the amount of droplets inside were counted during whole time of simulations. The effect of the fan was concluded as able to eject the 60% of small droplets, but also a high performance in spreading particles inside. Among the three cases, the riskiest scenario was the continuum speech due to the saturation of droplets in airborne.

Factors associated with medical radiation and imaging professionals’ willingness to work during the SARS-COV-2 pandemic: A cross-sectional study

BackgroundResilient health systems effectively respond to health crises and coordinate post-event recovery. Central to a resilient system is the willingness of its workforce to work in high-risk scenarios. This study explored traits that may affect Medical Radiation and Imaging Professionals (MRIPs) willingness to work during a pandemic. Specifically, intrapersonal traits of compassion, empathy, and resilience were examined. Understanding such relationships can inform interventions that enhance individuals' willingness and health system resilience (HSR). MethodsIn Fall 2020, a cross-sectional survey of Nova Scotian MRIPs was conducted. Witte's Extended Parallel Process Model, Professional Quality of Life, Intrapersonal Reactivity Index and Adult Resilience Measures-17 tools were used to assess willingness, compassion, empathy, and resilience. Statistical tests included chi-square, binomial, and multinomial logistic regression. ResultsMRIPs were willing to report to work during the SARS-CoV-2 pandemic if required (92%); asked but not required (89%); or regardless of severity (94%). Individuals with very high levels of willingness reported lower personal distress (a facet of empathy) compared to those with high (p=.018) or moderate levels (p=.005). MRIPs with < 10 years experience were more likely to report high personal distress (p≤.37). Job role was associated with willingness (p<.001). There were no significant associations between level of willingness, and gender, age, experience, education, or the intrapersonal traits resilience and compassion. ConclusionWillingness was associated with job role and levels of personal distress. Personal distress was higher in participants with less years of practice. Interventions to reduce personal distress in early career MRIPs may enhance willingness to work during crises and thereby support HSR.

Does COVID-19 Affect the Accessibility of Outdoor Sports Venues? A Case Study in Nanchang, China

The COVID-19 pandemic has been a great challenge to society, the economy, and population health. It has become a significant public health event and social problem. Exploring the impact of COVID-19 on the accessibility of outdoor sports venues is crucial for people’s health. Based on spatial theory, the quantitative and qualitative analyses of outdoor sports venues’ spatial distribution and accessibility were conducted, and the epidemic’s impact on them was analyzed. The results show that: (1) The existing outdoor sports venues in Nanchang show a distribution pattern of “sparse in the north and south, and strong aggregation in the middle”. (2) As a result of the epidemic, the center of the standard deviation ellipse in outdoor sports sites shifted to the southeast, while the number of open venues decreased by 68%. (3) Before COVID-19, the entire study area could achieve full coverage by driving for 17 min, riding for 70 min, or walking for 119 min. After COVID-19, the time increased to 29, 109, and 193 min, respectively. (4) Under the high-risk scenario of COVID-19, the average walking time for people to reach outdoor sports venues increased from 6.2 min to 14.0 min in the study area, with an increase of 126%. Finally, according to the findings of this study, recommendations were made on how government departments could build or re-open outdoor sports venues during and after this epidemic.

Selection of Areas for Effective GNSS Spoofing Attacks to a Vehicle-mounted MSF System based on Scenario Classification Models

The inherent vulnerability of the Global Navigation Satellite System (GNSS) leads to the ease of implementation of spoofing attacks. The latest GNSS spoofing attack schemes still suffer from low success rate, long attack time, and poor concealment. To improve the success rate, an efficient GNSS spoofing attack method for a vehicle-mounted Multi Sensors Fusion (MSF) system is proposed based on the scenario classification models with a spatial database. Firstly, the influence of the two typical urban scenarios, which are 1) the road with buildings on both sides and 2) tunnels, on the GNSS spoofing attack is analyzed. Then a dynamic Bayesian network model considering the sky visibility generated with the 3D building models and tunnel models inside the spatial database is established to quantify the difficulty of the attack. Furthermore, the scenarios of the victim can be classified into high-risk and low-risk scenarios. When the vehicle is just out of the tunnel or in open scenarios, attackers can select these high-risk scenarios and implement aggressive spoofing attacks. Then the efficiency of the GNSS spoofing attack can be significantly improved. Finally, the proposed attack scheme is demonstrated by actual world data with simulated spoofing attacks in urban areas.

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

Different Murine High-Risk Corneal Transplant Settings Vary Significantly in Their (Lymph)angiogenic and Inflammatory Cell Signatures.

Pathologic conditions in the cornea, such as transplant rejection or trauma, can lead to corneal neovascularization, creating a high-risk environment that may compromise subsequent transplantation. This study aimed to evaluate the impact of different types of corneal injury on hemangiogenesis (HA), lymphangiogenesis (LA) and immune cell pattern in the cornea. We used five different corneal injury models, namely, incision injury, alkali burn, suture placement, and low-risk keratoplasty, as well as high-risk keratoplasty and naïve corneas as control. One week after incision and 2 weeks after all other different injuries, corneal HA and LA were quantified by morphometric analysis. In addition, immune cell patterns of the whole cornea and the recipient rim were analyzed by immunohistochemistry. Immune cells in the draining lymph nodes (dLNs) were quantified by flow cytometry. Different types of corneal injury caused significantly different HA and LA responses (both P < 0.0001). The infiltration of corneal macrophages, dendritic cells, neutrophils, major histocompatibility complex (MHC) II+ cells, CD4+ T cells, and CD8+ T cells varied significantly in different high-risk settings (all P < 0.0001). Both the expression of MHC II on macrophages (P = 0.0005) and the frequency of MHC II+ dendritic cells (P = 0.0014) in the draining lymph nodes were significantly different across the various high-risk scenarios. Murine high-risk settings caused by different underlying pathologies vary significantly in their (lymph)angiogenic and inflammatory cell patterns. Therefore, anti(lymph)angiogenic or immunomodulatory strategies to prevent and/or treat immune responses after subsequent corneal transplantation may need to be customized according to their immune-vascular "signatures."

Factors Potential Patients Deem Important for Decision-Making in High-Risk Surgical Scenarios.

Surgical futility and shared decision-making to proceed with high-risk surgery are challenging for patients and surgeons alike. It is unknown which factors contribute to a patient's decision to undergo high-risk and potentially futile surgery. The clinical perspective, founded in statistical probabilities of survival, could be misaligned with a patient's determination of worthwhile surgery. This study assesses factors most important to patients in pursuing high-risk surgery. Via anonymous survey, lay participants recruited through Amazon's Mturk were presented high-risk scenarios necessitating emergency surgery. They rated factors (objective risk and quality-of-life domains) in surgical decision-making (0 = not at all, 4 = extremely) and made the decision to pursue surgery based on clinical scenarios. Repeated observations were accounted for via a generalized mixed-effects model and estimated effects of respondent characteristics, scenario factors, and likelihood to recommend surgery. Two hundred thirty-six participants completed the survey. Chance of survival to justify surgery averaged 69.3% (SD = 21.3), ranking as the highest determining factor in electing for surgery. Other factors were also considered important in electing for surgery, including the average number of days the patient lived if surgery were and were not completed, functional and pain status after surgery, family member approval, and surgery cost. Postoperative independence was associated with proceeding with surgery (p < 0.001). Recommendations by patient age was moderated by respondent age (p = 0.002). Patients highly value likelihood of survival and postoperative independence in shared decision-making for high-risk surgery. It is important to improve the understanding of surgical futility from a patient's perspective.

Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach

Construction risk assessment (RA) based on expert knowledge and experience incorporates uncertainties that reduce its accuracy and effectiveness in implementing countermeasures. To support the construction of RA procedures and enhance associated decision-making processes, machine learning (ML) approaches have recently been investigated in the literature. Most ML approaches have difficulty processing dependency information from real-life construction datasets. This study developed a novel RA model that incorporates a graph convolutional network (GCN) to account for dependency information between construction accidents. For this purpose, the construction accident dataset was restructured into an accident network, wherein the accidents were connected based on the shared project type. The GCN decodes the construction accident network information to predict each construction activity’s severity outcome, resulting in a prediction accuracy of 94%. Compared with the benchmark feedforward network (FFN) model, the GCN demonstrated a higher prediction accuracy and better generalization ability. The developed GCN severity predictor allows construction professionals to identify high-risk construction accident scenarios while considering dependency based on the shared project type. Ultimately, understanding the relational information between construction accidents increases the representativeness of RA severity predictors, enriches ML models’ comprehension, and results in a more reliable safety model for construction professionals.

Survival and Prognostic Factors of Ultra-Central Tumors Treated with Stereotactic Body Radiotherapy.

Introduction: Stereotactic body radiotherapy (SBRT) reported excellent outcomes and a good tolerability profile in case of central lung tumors, as long as risk-adapted schedules were adopted. High grade toxicity was more frequently observed for tumors directly touching or overlapping the trachea, proximal bronchial tree (PBT), and esophagus. We aim to identify prognostic factors associated with survival for Ultra-Central (UC) tumors. Methods: We retrospectively evaluated patients treated with SBRT for primary or metastatic UC lung tumors. SBRT schedules ranged from 45 to 60 Gy. Results: A total number of 126 ultra-central lung tumors were reviewed. The Median follow-up time was 23 months. Median Overall Survival (OS) and Progression Free Survival (PFS) was 29.3 months and 16 months, respectively. Local Control (LC) rates at 1 and 2 were 86% and 78%, respectively. Female gender, age < 70 years, and tumor size < 5 cm were significantly associated with better OS. The group of patients with tumors close to the trachea but further away from the PBT also correlated with better OS. The acute G2 dysphagia, cough, and dyspnea were 11%, 5%, and 3%, respectively. Acute G3 dyspnea was experienced by one patient. Late G3 toxicity was reported in 4% of patients. Conclusion: risk-adaptive SBRT for ultra-central tumors is safe and effective, even if it remains a high-risk clinical scenario.

Manipulating risk of infection and appeal to public benefit increase compliance with infection control measures in a hypothetical pandemic scenario.

To limit an infectious outbreak, the public must be informed about the infection risk and be motivated to comply with infection control measures. Perceiving a situation as threatening and seeing benefits to complying may be necessary to motivate for compliance. The current study used a preregistered survey experiment with a 2-by-2 between-subject design to investigate if emphasizing high infection risk and appealing to societal benefits impacted intention to comply with infection control measures. The results from a representative Norwegian sample (N = 2533) show that describing a high (as opposed to low) personal risk scenario had a small main effect on compliance. Further, appealing to public (as opposed to self-interested) benefits also had a small main effect. There was no interaction between risk scenario and motivational emphasis. The results suggest that to maximize compliance, information about disease outbreak should emphasize the individual risk of contracting the disease, and could also underline the public value of limiting infection spread. These findings can inform health authorities about the motives underlying compliance with infection control measures during an infectious disease outbreak.

Perception of safety culture in the Nepalese aviation industry: A factor analysis approach

This paper uses industry-wide survey data to assess employees’ perception of safety culture in the Nepalese aviation industry. A factor analysis with Principal Component Analysis extraction and varimax rotation is employed to extract factors from the data set of thirty-three variables. The initial factor solution identifies “Positive safety practices”, “Management and decision making”, “Safety management systems”, and “Implementation of policies and procedures” as the factors influencing the perception of safety in an organization. Likewise, respondents considered “Implementation of safety policies and procedures” as the most important factor among others to ensure safety in the aviation industry. An interesting finding from this study is that the Senior-level management believes that the management practices employed in their organization are proactive enough to allocate resources, to ensure operational safety, address high-risk scenarios, and promote blame-free reporting of safety-related incidents which is challenged by the respondents working in Mid-level management. Another significant revelation is that the more experienced employees perceive that the “Safety management systems” employed in their organization are not effective and there is a significant room for improvement in the safety reporting and safety management practices in their organization. Based on the findings, it is concluded that the service providers could do better in improving individual’s safety practices, facilitating safety reporting, and managing safety.