Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.

Systemic Approach to Failure Analysis: A Study of Sociotechnical Organization

The paper is an attempt to explore systemic approach to accidents analysis within sociotechnical organization. Unsafe behaviors can result in systemic failures and accidents. The research data came from within an industrial radiography organization where radiation source is used to detect cracks or hidden flaws within machinery and welding joints. The radioactive source can cause severe detrimental effects, even death, if not used safely. This paper is on two isolated events happen in the same company, Industrial Services Private Limited (not real name). The company top management was dealing with the financial crisis and to maintain quality of its services and the safety of staff. The leadership of the company made several business and operational decisions to manage the financial crisis. During that time, several near miss incidents took place, but the first major incident took place when the radiography gamma projector, along with the radioactive source, fallen out of the vehicle because of improper back door closure of the vehicle. In the second incident, the radioactive source remained unshielded after a radiography job, this caused overexposure to radiography workers. The paper provides an opportunity to understand how an organization’s leadership can create conditions for errors and mistakes that result in poor safety culture and ultimately the accident which resulted the system failure and operation shutdown. The paper also tries to propose a conceptual framework to improve safety culture within the sociotechnical systems for the future research in this area.

Paediatric major incident triage and the use of machine learning techniques to develop an alternative triage tool with improved performance characteristics.

Background Triage is a key principle in the effective management of major incidents. However, there is an increasing body of evidence demonstrating that existing paediatric methods are associated with high rates of under-triage and are not fit for purpose. The aim of this study was to derive a novel paediatric triage tool using machine learning (ML) techniques. Methods The United Kingdom Trauma Audit Research Network (TARN) database was interrogated for all paediatric patients aged under 16 years for the ten-year period 2008-2017. Patients were categorised as Priority One if they received one or more life-saving interventions from a previously defined list. Six ML algorithms were investigated for identifying patients as Priority One. Subsequently, the best performing model was chosen for further development using a risk score approach and clinically relevant modifications in order to derive a novel triage tool (LASSO M2). Using patients with complete pre-hospital physiological data, a comparative analysis was then performed comparing this to existing pre-hospital paediatric major incident triage tools. Performance was evaluated using sensitivity, specificity, under-triage (1-sensitivity) and over-triage (1-positive predictive value). Results Complete physiological data were available for 4962 patients. The LASSO M2 model demonstrated the best performance at identifying paediatric patients in need of life-saving intervention, sensitivity 88.8% (95% CI 85.5, 91.5) and was associated with the lowest rate of under-triage, 11.2% (8.5, 14.5). In contrast, the Paediatric Triage Tape and JumpSTART both had poor sensitivity when identifying those requiring life-saving intervention (36.1% (31.8, 40.7) and 44.7% (40.2, 49.4)) respectively. Conclusion The ML derived triage tool (LASSO M2) outperforms existing methods of paediatric major incident triage at identifying patients in need of life-saving intervention. Prior to its recommendation for clinical use, further work is required to externally validate its performance and undertake a feasibility assessment in a clinical context.

Comparative analysis of major incident triage tools in children: a UK population-based analysis

IntroductionTriage is a key principle in the effective management of major incidents. There is currently a paucity of evidence to guide the triage of children. The aim of this study was to perform a comparative analysis of nine adult and paediatric triage tools, including the novel ‘Sheffield Paediatric Triage Tool’ (SPTT), assessing their ability in identifying patients needing life-saving interventions (LSIs).MethodsA 10-year (2008–2017) retrospective database review of the Trauma Audit Research Network (TARN) Database for paediatric patients (<16 years) was performed. Primary outcome was identification of patients receiving one or more LSIs from a previously defined list. Secondary outcomes included mortality and prediction of Injury Severity Score (ISS) >15. Primary analysis was conducted on patients with complete prehospital physiological data with planned secondary analyses using first recorded data. Performance characteristics were evaluated using sensitivity, specificity, undertriage and overtriage.Results15 133 patients met TARN inclusion criteria. 4962 (32.8%) had complete prehospital physiological data and 8255 (54.5%) had complete first recorded physiological data. The majority of patients were male (69.5%), with a median age of 11.9 years. The overwhelming majority of patients (95.4%) sustained blunt trauma, yielding a median ISS of 9 and overall, 875 patients (17.6%) received at least one LSI. The SPTT demonstrated the greatest sensitivity of all triage tools at identifying need for LSI (92.2%) but was associated with the highest rate of overtriage (75.0%). Both the Paediatric Triage Tape (sensitivity 34.1%) and JumpSTART (sensitivity 45.0%) performed less well at identifying LSI. By contrast, the adult Modified Physiological Triage Tool-24 (MPTT-24) triage tool had the second highest sensitivity (80.8%) with tolerable rates of overtriage (70.2%).ConclusionThe SPTT and MPTT-24 outperform existing paediatric triage tools at identifying those patients requiring LSIs. This may necessitate a change in recommended practice. Further work is needed to determine the optimum method of paediatric major incident triage, but consideration should be given to simplifying major incident triage by the use of one generic tool (the MPTT-24) for adults and children.

Cost-effectiveness of ‘screen-and-treat’ interventions for post-traumatic stress disorder following major incidents

ObjectivesPost-traumatic stress disorder (PTSD) is commonly experienced in the aftermath of major incidents such as terrorism and pandemics. Well-established principles of response include effective and scalable treatment for individuals affected by PTSD. In England, such responses have combined proactive outreach, screening and evidence-based interventions (a ‘screen-and-treat’ approach), but little is known about its cost-effectiveness. The objective of this paper is to report the first systematic attempt to assess the cost-effectiveness of this approach.MethodsA decision modelling analysis was undertaken to estimate the costs per quality-adjusted life-year (QALY) gained from a screen-and-treat approach compared with treatment-as-usual, the latter involving identification of PTSD by general practitioners and referral to psychological therapy services. Model input variables were drawn from relevant empirical studies in the context of terrorism and the unit costs of health and social care in England. The model was run over a 5-year time horizon for a hypothetical cohort of 1000 exposed adults from the perspective of the National Health Service and Personal Social Services in England.ResultsThe incremental cost per QALY gained was £7931. This would be considered cost-effective 88% of the time at a willingness-to-pay threshold of £20 000 per QALY gained, the threshold associated with the National Institute for Health and Care Excellence in England. Sensitivity analysis confirmed this result was robust.ConclusionsA screen-and-treat approach for identifying and treating PTSD in adults following terrorist attacks appears cost-effective in England compared with treatment-as-usual through conventional primary care routes. Although this finding was in the context of terrorism, the implications might be translatable into other major incident-related scenarios including the current COVID-19 pandemic.

Prehospital major incident management: how do training and real-life situations relate? A qualitative study

ObjectiveTo explore the relationship between preparations and real-life experiences among prehospital major incident commanders.DesignAn explorative, qualitative design was used.SettingPrehospital major incidents in Sweden. Data were collected between December 2019 and August 2020.ParticipantsPrehospital major incident commanders (n=15) with real-life experiences from major events, such as fires, bus accidents, a bridge collapse and terrorist attacks, were included. All but one had participated in 2-day training focusing on the prehospital management of major incidents. In addition, about half of the participants had participated in simulation exercises, academic courses and other training in the management of major incidents.MethodsData from two-session individual interviews were analysed using inductive thematic analysis.ResultsThe conformity between real-life major incidents and preparations was good regarding prehospital major incident commanders’ knowledge of the operational procedures applied in major incidents. However, the preparations did not allow for the complexities and endurance strategies required in real-life incidents. Personal preparations, such as mental preparedness or stress management, were not sufficiently covered in the preparations. To some extent, professional experience (such as training) could compensate for the lack of formal preparations.ConclusionsThis study identified perceived gaps between preparations and real-life experiences of being a prehospital major incident commander. To minimise the gaps between demands and expectations on perceived control and to better prepare individuals for being prehospital major incident commanders, the training and other preparations should reflect complexities of real-life incidents. Preparations should develop both technical skills required, such as principles and methodology used, and personal preparedness. Personal preparations should include improving one’s mental preparedness, self-knowledge and professional self-confidence required to successfully act as a prehospital incident commander. Since little is known about what pedagogical methods that should be used to enhance this, further research is needed.