New development: A ‘journey of personal and professional emotions'—emergency ambulance professionals during Covid-19

IMPACTThis article explores the consequences of emotional labour on UK NHS ambulance staff and their response to the Covid-19 pandemic. It highlights the challenges faced by ambulance crews while dealing with their emotional labour within the context of organizational settings. Research findings also explain the importance of emergency responders' psychosocial wellbeing. The article has clear relevance as to how frontline staff manage their emotional labour in other emergency service settings, such as the police and fire and rescue services.Alternate :Managing emotions are essential aspect of many jobs, and frontline healthcare workers have to manage and control their emotions while caring for critically ill patients and working in an emotionally-charged dynamic environment;this was particularly the case during Covid-19. Ambulance workers are an important group in this respect but they are currently under-researched. Evidence behind this article comes from data collected from an NHS ambulance trust in England. One of the key contributions of this article is to highlight how frontline ambulance professionals manage their emotional labour while working within the stipulations of organizational constraints.

Ozone Efficacy for the Disinfection of Ambulances Used to Transport Patients during the COVID-19 Pandemic in Peru.

We assessed the disinfection efficacy of an ozone generator prototype in ambulances used to transport patients with coronavirus disease (COVID-19). This research consisted of three stages: in vitro tests using microbial indicators, such as Candida albicans, Escherichia coli, Staphylococcus aureus and Salmonella phage, which were experimentally inoculated onto polystyrene crystal surfaces within a 23 m3 enclosure. They were then exposed to ozone at a 25 ppm concentration using the ozone generator (Tecnofood SAC) portable prototype, and the decimal reduction time (D) was estimated for each indicator. The second stage involved the experimental inoculation of the same microbial indicators on a variety of surfaces inside conventional ambulances. The third stage consisted of exploratory field testing in ambulances used to transport patients with suspected COVID-19. During the second and third stages, samples were collected by swabbing different surfaces before and after 25 ppm ozonisation for 30 min. Results suggested that ozone was most effective on Candida albicans (D = 2.65 min), followed by Escherichia coli (D = 3.14 min), Salmonella phage (D = 5.01 min) and Staphylococcus aureus (D = 5.40 min). Up to 5% of the microbes survived following ozonisation of conventional ambulances. Of the 126 surface samples collected from ambulances transporting patients with COVID-19, 7 were positive (5.6%) for SARS-related coronavirus as determined on reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). Ozone exposure from the ozone generator prototype inside ambulances at a concentration of 25 ppm for 30 min can eliminate gram positive and negative bacteria, yeasts, and viruses.

Smart Ambulance Monitoring System Using Iot

A vehicle which is used to transport patients to hospitals is called as ambulance, this ambulance vehicle is equipped with some vital lifesaving equipment's and first aid medicines. With these medicines and equipment's, the patients are given first aid till it reaches the desired hospital. Whenever a patient is in life-threatening emergency, then the ambulance paramedics should act promptly and must update the status of the patient to their doctors. This type of treatment or service is called Emergency Medical Service (EMS). It deals with immediate treatment and timely movement of the patients. For implementing EMS in ambulance several solutions have been developed for faster means of communication between the equipment's used in ambulance and the doctors. But the solutions are not viable in emergency situations. In additions during Covid wave in 2020 there was a scarcity for hospitals beds, it was filled with many covid patients. In many hospitals there was a queue of ambulances with patients waiting outside the hospitals. In some cases, the patients even died before getting any treatment, to overcome such crucial situations we have come out with a new system combining IoT, Sensor system and Embedded Controllers. The system developed here has sensors to monitor patients' vital parameters and transmits to the hospital server, such that a doctor can know the live condition of the patient and he can give instructions to the paramedics to do emergency aids. The system is a compactable and connects to the server with a mobile hotspot. The information is updated every 10 seconds. By implementing this system in ambulances, it saves many valuable lives of the people.

The emergency medical services of Madrid tested: analysis of the spatial-temporal performance of SAMUR-PC in the first months of the postCOVID-19 new normality period

Those requiring emergency health care cannot wait. Ambulances must arrive at the scene as quickly as possible. Ambulances are usually assigned to bases distributed throughout the city to minimize arrival time to the event. However, the spatial distribution of events changes during the day according to people's rhythm and city use. This paper evaluates, through location-allocation modelling, the Spatio-temporal performance of SAMUR-PC, the Emergency Medical Service of Madrid (Spain), in two different scenarios, before the COVID-19 pandemic and during the first months of the new normality period. The results show that the system responded relatively well to the change in event patterns due to the pandemic. However, some interventions would have been necessary to guarantee the same service as before the epidemiological crisis. © 2023 Asociacion de Geografos Espanoles. All rights reserved.

A Covid-19 Tracker for Medical Front-liners

The management of Covid-19 affected patients is a very difficult task. The current healthcare system of India is not able to cope with the enormous flow of patients and is in a dire need for improvement. This implementation paper provides a system which will manage all the affected patients right from the time they are Covid-19 positive till the time they are treated and discharged. This paper includes all the technical details of a fully implemented healthcare management system which is a significant improvement in the current system. The proposed system is a cross platform multi user web app which can be used by multiple stakeholders to carry out smooth management of the patients. It consists of a lot of key features like dynamic location-wise patient status, an accurate tracking system of ambulances, a statistical trend analysis of patients and categorical report generation of patients. This system aims to help the medical Front-liners in efficient management of Covid-19 patients, and it is a common site for all the different health workers like field workers and medical officers to work togetherand fight against this deadly disease affecting our country. © 2022 IEEE.

The new soundscape after the Covid-19 lockdown

We present a pilot study on the perception of noise and noise annoyance from various environmental sources after the COVID-19 lockdown in Serbia. We used an online platform to conduct an anonymous survey on about 190 respondents aged 15 to 75 years from all over the country. After the lockdown, there was a significant increase in the proportion of respondents who were highly annoyed by noise from the typical environmental sources, such as road traffic, air traffic, industrial facilities, and construction works on the streets, compared to the time during the lockdown. At the same time, the proportion of respondents who were highly annoyed by the sirens of ambulance vehicles decreased significantly. However, the most annoying environmental sound during and after the lockdown was that emitted by the ambulance vehicles, which respondents described as "unpleasant”, "horrifying” and "irritating”. The perception of the sounds of nature and church bells dropped significantly after the lockdown, whereas the perception of noise from the neighbors, household members, and indoor sources remained the same. In summary, after the lockdown, respondents perceive the new soundscape as louder, burdened with a cacophony of noises, and subsequently more annoying. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

pandemic-transformed activities inclusive of remote learners

The article focuses on how the Covid-19 pandemic has transformed activities to become more inclusive of remote learners with acrylic sheet nets for heat bending, and modeling of repeated patterns for K-12 students within the makerspace pre- and post-pandemic manufacturing. It further explains the activity, STEM education, how the activity ensures accessibility for all learners, and how pandemic activities influenced the development of each activity.

Impact of school closures on pediatric ambulance transport in Japan.

BACKGROUND: After the coronavirus disease (COVID-19) pandemic, nationwide school closures were implemented in many countries. This study aimed to determine the impact of the school closure on pediatric ambulance transport, comparing the situation before and after the COVID-19 pandemic. METHODS: This retrospective observational study was conducted using data from the Kochi-Iryo-Net database. In Kochi Prefecture, schools were closed from March 6 to May 24, 2020. Pediatric emergency transports during the school closure period in 2020 was compared to that of the same period in 2019 (before the COVID-19 pandemic) and in 2021 (schools are not closed). Chi-squared tests and correction for multiple testing was performed by Bonferroni adjustments. To adjust for patient backgrounds, we also performed multiple logistic regression analyses for cases of pediatric ambulance transports. RESULTS: The rate of pediatric ambulance transports was significantly lower (p=0.008) in 2020 (276; 3.97%) than in 2019 (391; 4.87%), but there was no significant difference (p=0.360) between 2019 (391; 4.87%) and 2021 (352; 4.56%). Multivariable analysis revealed similar trends (2019 vs. 2020: odds ratio [OR] 0.86, 95% confidence interval [CI] 0.73-1.00; 2019 vs. 2021: OR 0.96, 95% CI 0.82-1.11). Regarding the characteristics of pediatric ambulance transport cases, there were no significant differences in 2019, 2020, and 2021 in terms of sex, severity, locations of fire stations, and injury mechanism. CONCLUSIONS: There was a decrease in pediatric ambulance transportation due to the public health interventions for COVID-19, including school closures. However, this decrease was not solely due to school closures.

Not only COVID-19 disease impacts ambulance emergency demands but also lockdowns and quarantines.

BACKGROUND: The pandemic has impacted both patients infected by the SARS-CoV-2 virus and patients who seek emergency assistance due to other health issues. Changes in emergency demands are expected to have occurred during the pandemic, the objective of this investigation is to characterize the changes in ambulance emergency demands during the first year of the COVID-19 pandemic in the Vaud State of Switzerland. The goal of this research is to identify the collateral effects of the COVID-19 pandemic on emergency demands. To do so, this study quantifies the differences in health issues, level of severity, and patients' sociodemographic characteristics (age, location, gender) prior to and during the outbreak. METHOD: This is a retrospective, descriptive and comparative statistical analysis of all ambulance emergency missions from 2018 to 2020 (n = 107,150) in the State of Vaud in Switzerland. Variables analyzed were the number of ambulance missions, patient age and gender, health issues, severity (NACA scores), number of non-transports, mission times and locations. Variables were compared between prepandemic and pandemic situations across years and months. Comparative analysis used bivariate analysis, χ2 test, Student's t test, and Mann‒Whitney U test. RESULTS: The pandemic has had two major impacts on the population's emergency demands. The first appears to be due to COVID-19, with an increase in respiratory distress cases that doubled in November 2020. The second relates to the implementation of lockdown and quarantine measures for the population and the closures of restaurants and bars. These might explain the decrease in both the number of traumas and intoxications, reaching more than 25% and 28%, respectively. An increase in prehospital emergency demands by the older population, which accounted for 53% of all demands in 2020, is measured. CONCLUSION: Collateral effects occurred during 2020 and were not only due to the pandemic but also due to protective measures deployed relative to the population. This work suggests that more targeted reflections and interventions concerning the most vulnerable group, the population of people 65 and older, should be of high priority. Gaining generalizable knowledge from the COVID-19 pandemic in prehospital settings is critical for the management of future pandemics or other unexpected disasters.

Reciprocal Abstracts January 2023

B Editor's note: b The I Hong Kong Journal of Emergency Medicine i has partnered with a small group of selected journals of international emergency medicine societies to share from each a highlighted research study, as selected monthly by their editors. B Conclusions: b There was an increased odds of delayed intracranial hemorrhage within 90 days in older ED head injured patients prescribed warfarin compared to patients not on anticoagulation. Among patients not anticoagulated, 586 (1.0%) had a delayed intracranial hemorrhage, 54 (1.8%) patients on warfarin, and 78 (1.0%) patients on a direct oral anticoagulant had a delayed intracranial hemorrhage. [Extracted from the article]

From Other Journals

The article presents the discussion on various topics in the journal. Topics include database linkage study showing an association between ambulance offload times, 30-day mortality, and ambulance reattendance rates for patients with chest pain;and importantly fluid administration in the standard fluid group not strictly protocolised.

Artificial Intelligence-Powered Contactless Face Recognition Technique for Internet of Things Access for Smart Mobility

A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.

Intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities

One of the greatest challenges for a traffic control system is to synchronize the flow of vehicles to prevent traffic jams. This issue gets worse when there are priority vehicles, such as ambulances, trying to move through the traffic. Given the current situation, with the COVID-19 pandemic, and the trends of smart cities, in this work, we propose and simulate a traffic control system that prioritizes ambulances within large urban centers, using Fuzzy logic and IoT devices. The simulation of our proposed model was performed on the software Dojot, which is an open platform for IoT modeling. It addressed a real situation, in a path that is usually used by ambulances to get to a reference hospital in the city of Campinas, Brazil. The proposed traffic control system can also be used after the COVID-19 pandemic is over in order to improve traffic flow for other priority vehicles (e.g., firefighters and police) and increase people’s life quality within smart cities. © 2022, Springer Nature Switzerland AG.

Application of Data Mining Technology-Based Nursing Risk Management in Emergency Department Care

Nursing risk refers to all unsafe events that may occur in clinical nursing work. Common risk events include bed fall, fall, scald, line dislodgement, drug extravasation, and drug administration error, which easily lead to nursing-patient disputes and seriously affect the prognosis of patients. In order to effectively avoid nursing risks, strengthening nursing risk management (NRM), improving nursing management mechanism, and improving nursing operation process have become effective ways to manage risks. The emergency department is an important window for rescuing critically ill patients in the hospital, and it is also the main department where diagnosis, nursing risk events, and medical disputes occur. The traditional risk care model has failed to meet the current demand for emergency patient management, and a more scientific and standardized management scheme is urgently needed. In order to improve the quality of NRM in emergency departments and combine the advantages and characteristics of big data-related technologies, this paper proposes an algorithm based on data mining for application in emergency care. The application of data mining in medical care is summarized and combined with the work content and requirements of hospital emergency care, and the application of big data in patient condition monitoring and early warning, medical and nursing staff scheduling, and patient emotional reassurance is discussed, and then, a solution for hospitals to optimize emergency care using data mining is proposed for the special characteristics of emergency care. Initially, the optimized solution is proposed to improve the efficiency and accuracy of patient condition monitoring and early warning, to improve the real-time scheduling of medical and nursing staff, and to solve medical care problems such as patient emotional calming. The analysis shows that the application of big data in emergency care can improve the efficiency of emergency ambulance, improve the doctor-patient relationship, and promote the development of emergency care. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Research on Airflow Optimization and Infection Risk Assessment of Medical Cabin of Negative-Pressure Ambulance

Medical cabins within negative-pressure ambulances currently only use the front air supply, which causes poor emission of infectious disease droplets. For this problem, based on the classification and design methods of airflow organization, the side and top supply airflow organization model has been designed to study the influence of these airflow organization models on the spread of droplet particles. The distribution of droplet particles within airflow organization models, under conditions in which the patient is coughing and sneezing, is analyzed. According to the comparison and analysis of this distribution, the state of droplet particles, the emission efficiency, and the security coefficient are studied. The response surface method is used to optimize the emission efficiency and security coefficient of the airflow organization. According to the characteristics of the medical cabin within negative-pressure ambulances, a dose-response model is used to evaluate the infection risk of medical personnel and then the infection probability is obtained. These research results can be used to improve the ability of negative-pressure ambulances to prevent cross-infection.

Prevention is better …

The article presents the discussion on service tackling tuberculosis in London's homeless. Topics include obesity levels among nurses and supporting workers remaining high showing the consequences;increasing the risks of musculoskeletal conditions and mental health conditions being the the main causes of sickness absence in health services.

Determining response time factors of emergency medical services during the COVID-19 pandemic

Purpose>The lack of studies about the response time of emergency medical service during the coronavirus disease 2019 (COVID-19) pandemic in a dense city of a developing country has triggered this study to explore the factors contributing to a high response time of ambulance service to reach patients in need. An evaluation of contributing factors to the response time is necessary to guide decision-makers in keeping a high service level of emergency medical service.Design/methodology/approach>This research employed an agent-based modeling approach with input parameters from interviews with emergency medical service staff in Bandung city, Indonesia. The agent-based model is established to evaluate the relevant contribution of the factors to response time reduction using several scenarios.Findings>According to agent-based simulation, four factors contribute to the response time: the process of preparing crew and ambulance during the pandemic, coverage area, traffic density and crew responsiveness. Among these factors, the preparation process during the pandemic and coverage area significantly contributed to the response time, while the traffic density and crew responsiveness were less significant. The preparation process is closely related to the safety procedure in handling patients during the COVID-19 pandemic and normal time. The recommended coverage area for maintaining a low response time is 5 km, equivalent to six local subdistricts.Research limitations/implications>This study has explored the factors contributing to emergency medical response time. The insignificant contribution of the traffic density showed that citizens, in general, have high awareness and compliance to traffic priority regulation, so crew responsiveness in handling ambulances is an irrelevant factor. This study might have different contributing factors for less dense population areas and focuses on public emergency medical services provided by the local government.Practical implications>The local government must provide additional funding to cover additional investment for ambulance, crew and administration for the new emergency service deployment point. Exercising an efficient process in ambulance and crew preparation is mandatory for each emergency deployment point.Originality/value>This study evaluates the contributing factors of emergency medical response time in the pandemic and normal situation by qualitative analysis and agent-based simulation. The performance comparison in terms of medical response time before and after COVID-19 through agent-based simulation is valuable for decision-makers to reduce the impact of COVID-19.

Design and Development of a low cost Ultraviolet Disinfection system to reduce the cross infection of SARS-CoV-2 in ambulances

In this study, a low-cost Ultraviolet disinfection system is proposed to be used inside ambulances for minimizing the cross-infection of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) during patient transfer. The disinfection system consists of a tower unit that contains the Ultraviolet type C (UVC) light fixture and a control box where the power unit is placed. The UVC tower unit is portable, lightweight, and can be easily placed anywhere inside an ambulance. Two ultraviolet (UV) lamps used in the tower part have 254 nm wavelength with a total power of 180 Watt. The disinfection system can provide a dose of 16.9mj/cm2 within 1.06 seconds and 26.83 seconds if the distance of the targeted surface inside the ambulance from UV sources are 0.3 meters and 1.5 meters respectively. We have chosen various distances from UV source to targeted surface inside an ambulance and calculated the required corresponding times to reach the required dose to inactivate all viral concentrations. The designed disinfection system not only reduces the spread of SARS-CoV-2 by the semi-autonomous way inside ambulances but also requires the least labor efforts which are crucial in the current Covid-19 pandemic. © 2021 IEEE.

Process Approach to the Management of the Ambulance Dispatcher Service

The paper is devoted to the substantiation of ways to improve the work of emergency medical care in the context of the spread of coronavirus. The pandemic has increased the burden on the ambulance service, with the number of departures handled by one team increasing to 25 or more per day, compared to 11 in previous years. The average call waiting time and patient hospitalization time have significantly increased. This leads to a decrease in the level of customer satisfaction with the quality of medical services. The key process and point of application of efforts to reorganize the work of the emergency medical care in this situation is the activity of the dispatching service. Improving the dispatch service required building and analyzing a process model and decomposing it into three main sub-processes: resource planning, call handling, analysis, and reporting. The use of a process approach to the organization of emergency services allowed to reengineer and automate the activities of the dispatch service, to achieve coordination of the actions of the departments of medical organizations, to systematize information flows, to create a basis for the development and implementation of a set of measures to improve the quality of customer service. © 2021 IEEE.