Vital Safety Research Articles

Estimating the impact on patient safety of enabling the digital transfer of patients’ prescription information in the English NHS

ObjectivesTo estimate the number and burden of medication errors associated with prescription information transfer within the National Health Service (NHS) in England and the impact of implementing an interoperable prescription...

UCAN Make a Difference: Over 40 Years of Flying High Above Chicago

The University of Chicago Aeromedical Network (UCAN) was established in 1983 and has been providing critical care transport via both air and ground in and around the Chicago area for over 40 years. Over that time, the program has transported thousands of critically ill individuals, including complex specialty populations, while also maintaining a safe transport environment for its crew members and patients. UCAN has had a profound impact not only on its patients, but also on the entire transport community by providing continuing education, conducting vital safety research while maintaining the highest safety standards, and driving the industry forward through service and leadership. Since inception, the program has experienced many changes, including the transition from a traditional hospital-based program to an alternate delivery model and the conversion of the crew from a nurse-physician team to a nurse-nurse configuration. These changes have allowed for continuous evolution within UCAN, including the introduction of internal process improvements that focus on quality, safety and education, growth of the communications team and its capabilities, and expansion of UCAN's commitment to outreach and education for its community partners.

Enhancing MAUDE Database Utility by GPT-4 and Cause-Effect Visualization.

The MAUDE database is a valuable public resource for understanding malfunctions and adverse events related to medical devices and health IT. However, its extensive data and complex structure pose challenges. To overcome this, we have developed an automated analytical pipeline using GPT-4, a cutting-edge large language model. This pipeline is intended to efficiently extract, categorize, and visualize safety events with minimal human annotation. In our analysis of 4,459 colonoscopy reports from MAUDE (2011-2021), the events were categorized into operational, human factor, and device-related. Ishikawa diagrams visualized a subset stored in a vector database for easy retrieval and comparison through a similarity search. This innovative approach streamlines access to vital safety insights, reducing the workload on human annotators, and holds promise to enhance the utility of the MAUDE database.

Analyzing override patterns in profiled automated dispensing cabinets at a tertiary care hospital in Saudi Arabia

BackgroundInappropriate automated dispensing cabinet (ADC) overrides pose a significant risk to patient safety. Bypassing pharmacist review during these overrides removes a vital safety check, leaving nurses to review complex medication orders alone. This can lead to overlooking incorrect orders and significantly increasing the potential for medication errors. While Institute for Safe Medication Practices (ISMP) guidelines promote profiled ADCs and safe override practices, there is no national standard for acceptable override rates. ObjectivesAssessing the appropriateness of ADC overrides of profiled ADCs at a tertiary hospital in Saudi Arabia in accordance with ISMP guidelines (third core safety process). MethodsThis retrospective observational study reviewed all override transactions for 13 profiled ADCs over a 3-month period, from 1 October 2022 until 31 December 2022. The target override rate was set at no more than 5%. After applying exclusions, the relevant data fields were extracted from electronic records of 2 integrated systems: the Omnicell dashboard and the BESTCare hospital information system. The study assessed whether the override transactions complying with the standard elements required for appropriate ADC overrides in accordance with ISMP recommendations and hospital regulations. ResultsSix hundred sixty-four override transactions from profiled ADCs were reviewed and evaluated. Although the overall override limit for profiled ADCs was met (1.9%), multiple inappropriate override practices were revealed. These inappropriate practices are missing physician orders (33.7%), undocumented verbal orders (31.5%), nonurgent physician orders (11%), missing (8.4%) or delayed (33.7%) administration records, and unrecognized override errors (5.3%). Only 3 (0.5%) met all the standard elements required for ADC overrides. ConclusionThe target override threshold of 5% is inadequate to demonstrate sufficient adherence to the standard elements of appropriate overrides or effectively prevent medication errors. Consequently, a significantly lower target threshold override limit, well below 5%, should be considered, especially with 24-hour pharmacy services and fully integrated computerized physician order entry with ADC system. A multidisciplinary override surveillance team is considered essential.

A vital safety net for families

The latest Institute of Health Visiting survey shows evidence of a worsening crisis in health visiting. What's next for the profession?

Analysis of the Braking System in the Gardapati Prototype Energy Saving Car

The braking system is one of the crucial components in a vehicle because braking is a vital safety feature of the vehicle. The braking system functions to reduce or slow down the speed and stop the vehicle, as well as to enable the possibility of parking the vehicle on a downward slope. The braking system under investigation is examined in the prototype vehicle called Gardapati, which employs a hydraulic brake system. Gardapati is a vehicle created by students from the University of Riau's Mechanical Engineering Department, affiliated with the UKM Selembayung, following the standard references provided by the Energy-Efficient Vehicle Competition organized by BPTI.
 This final project will involve several calculations related to the braking system, following the specifications used in the Gardapati vehicle. First, calculations will determine the dynamic braking force required (Fb) when the vehicle is moving at a speed of 50 km/h with a distance of 20 meters, as well as the static braking force (Fb) required when the vehicle is on an incline of 11.2 degrees, adjusted to the requirements and regulations of the 2023 Energy-Efficient Vehicle Competition (KMHE). Then, the braking distance will be calculated for speeds of 30, 40, and 50 km/h on dry and wet asphalt road conditions.
 From the calculation results and data analysis, it is found that the required dynamic braking force (Fb) is 510,76 N, and the static braking force (Fb) is 830,31 N. Meanwhile, the minimum braking distance is observed on dry asphalt road conditions at a speed of 30 km/h with a percentage error of 1,59 %, where the theoretical value is 5.67 m and the actual value is 5,76 m. On the other hand, the highest braking distance is observed on wet asphalt road conditions at a speed of 50 km/h with a percentage error of 7,39%, where the theoretical value is 14.47 m and the actual value is 15.54 m. Of the two road conditions, the largest percentage error occurs when testing the vehicle at speeds of 30, 40, and 50 km/h on wet asphalt conditions, amounting to 4,61%. With this level of percentage error, it can be concluded that the variation in the obtained data is relatively small, which is less than 10%.

Rainstorm waterlogging vulnerability assessment of subway stations in the central area of Tianjin

ABSTRACTRainstorm and waterlogging disasters of underground rail transit have recently become a vital safety problem. In this paper, a model of vulnerability assessment is established, and 14 indicators are selected to establish an indicator system for 109 stations in the central area of Tianjin. Analytic Hierarchy Process(AHP) and Entropy Weight Method (EWM) were used to determine the subjective and objective weights of indicators. A distance function was introduced to calculate the combined weights and improve the weights’ rationality. The results show that there are 34 high and medium-high vulnerability stations, and 41 low and medium-low vulnerability stations. Except for Line 2, the vulnerability level of subway stations on the rail lines decreased from the edge to the centre. Nankai District had the most high-vulnerability stations (seven of the 20 high-vulnerability stations). The main influence factors were the waterproofing capacity of the subway, the digital elevation model, the station type, the height of the exit, and the entrance steps.

52 An Evaluation of Satisfaction with Emergency Department Care in Children and Adolescents with Mental Health Concerns

Abstract Background Increasingly, the emergency department (ED) is a vital safety net for children and adolescents with mental health concerns seeking crisis care. Evaluating patient and parent/caregiver experience and satisfaction with care is essential to improve the quality of mental health services provided. Objectives The primary objective was to evaluate total satisfaction with the delivery of ED mental health care in two paediatric EDs in Alberta, Canada (Stollery Children's Hospital and Alberta Children's Hospital). Secondary objectives included exploring aspects of ED mental health care (staff interpersonal skills, confidentiality/privacy, and patient-reported outcomes of care) associations with general satisfaction, patient and ED visit characteristic associations with total satisfaction score, and care experience themes. Design/Methods We enrolled patients <18 years of age presenting with a mental health concern—e.g., anxiety, disruptive behaviour, self-harm, depression, situational crisis—over a 12-month period (February 1, 2020—January 31, 2021). Satisfaction data were collected using the Service Satisfaction Scale, which includes quantitative and qualitative data on aspects of ED mental health care. ED visit (length of stay, mental health and psychiatry consultations, hospital admission) and patient (discharge diagnoses, age, sex) data were collected. We evaluated associations of general satisfaction with ED mental health care using Pearson’s correlation coefficient tests, and associations between total satisfaction score with patient and ED visit characteristics using multivariable regression analyses. Inductive thematic analysis of qualitative feedback identified satisfaction and patient experience themes. Results 646 participants were enrolled with most being Caucasian (71.2%; 460/646) and female (56.3%; 364/646); the median age was 13 years (IQR, 11-15 years). The most common discharge diagnosis was anxiety or stress-related disorders (39.5%; 245/620). Parents/caregivers and adolescents were most satisfied with confidentiality and respect in the ED and least satisfied with how the ED services helped reduce symptoms and/or problems. Greater satisfaction was reported for parents/caregivers whose child received care at Alberta Children’s Hospital (p=0.003). General satisfaction was strongly associated with the perceived amount of help received in the ED (r=0.85). ED visit characteristics associated with total satisfaction score included an evaluation by a mental health team member (p=0.004) and psychiatrist consultation (p=0.05). Written comments demonstrated satisfaction with ED provider attitudes and interpersonal skills and dissatisfaction with access to mental health and addictions care, wait time, and COVID-19. Conclusion The results reflect the need to improve the satisfaction with ED mental health care delivery, with a particular focus on enhancing access to care by appropriate mental health providers.

COMPARATIVE STUDY OF DIFFERENT TYPES OF CONTROLLERS IN AN ANTI-LOCK BRAKING SYSTEM USING MATLAB/SIMULINK

Anti-lock Braking System is one of the vital safety features in modern cars and trucks that can prevent the wheels from locking while the brakes are applied in the moving vehicles. During a braking condition, speed sensors in ABS send signals to the Anti-lock Braking System control unit to estimate the wheel slip ratio, and if the ratio is different from the desired value, the control unit will send a message to the braking actuator to control braking torque. In this paper, the dynamics and subsystems of the Anti-lock Braking System are presented and a model of the Anti-lock Braking System is developed in MATLAB/SIMULINK software. Different types of controllers, including the Bang-Bang controller, proportional-integral, proportional derivative, and proportional integral derivative controllers, are integrated into the model to investigate the effects of controlling strategies on the stopping distance, vehicle velocity, slip ratio, and braking torque. The simulation results show that the proportional derivative and proportional integral derivative controllers provide the shortest stopping distances and stopping times as compared with Bang-Bang and proportional-integral controllers on different types of roads. Furthermore, it also observed that the slip ratio during braking is kept similar to the desired value (20%) with the proportional derivative and proportional integral derivative controllers while it fluctuates between 10% and 30% for BangBang and proportional-integral controllers. The trend of the braking torque is also similar to the slip ratio. The model can be used to predict the braking performance of the Anti-lock Braking System under different conditions.

The Construction of a Fire Monitoring System Based on Multi-Sensor and Neural Network

An automated fire alarm system is a vital safety facility for modern fire fighting. It is an essential guarantee for people to find fires early and take effective measures to control and extinguish them in time. This article proposes a multi-sensor data fusion algorithm based on artificial neural network (ANN) technology, which intelligently processes various environmental characteristic parameters detected by multi-sensors, effectively detects real fire signals, and realizes early fire monitoring and alarm. The simulation results show that compared with the fuzzy clustering algorithm (FCM), the MAE of the proposed data fusion algorithm is improved by about 15%, and the recall is improved by about 10%. It can not only overcome the instability and limitation of a single sensor, but also grasp the system information more comprehensively and accurately. The data fusion technology is applied to the fire monitoring system, and multiple sensorsmultiple sensors collect the data collect the data, and then processed by data fusion technology. By making full use of multidimensional information, the fire monitoring and identification can be better completed, the false alarm rate and the false alarm rate can be reduced, the system is more sensitive and reliable, and the performance of the fire alarm system can be improved.

The Impact of Economic Conditions on Social Assistance Programs and Poverty Alleviation

Poverty remains a pervasive global challenge, necessitating effective policy interventions to uplift vulnerable populations. This research delves into the dynamic relationship between social assistance programs and poverty alleviation. Employing a mixed-methods approach, we investigate the impact of these programs on poverty rates, program effectiveness variations, demographic disparities, and the interplay with economic conditions. Our findings offer compelling insights. Firstly, social assistance programs exhibit a statistically significant reduction in poverty rates among beneficiaries, confirming their vital role in poverty alleviation. Moreover, variations in program effectiveness emerge, with cash transfer programs demonstrating immediate impact compared to in-kind assistance programs. Demographic differences further shape program outcomes, as programs targeting children and single-parent households exhibit pronounced effects on poverty reduction. Economic conditions also matter, as these programs prove more effective during periods of robust economic growth, serving as vital safety nets during economic downturns. Conditional social assistance programs demonstrate promise by incentivizing positive behaviors such as education enrollment and healthcare utilization. These findings hold significant policy implications, emphasizing the need for evidence-based program design and implementation. This research concludes with a call for comprehensive approaches to poverty alleviation, considering demographic variations, economic contexts, and the potential benefits of conditional programs. Ongoing monitoring and evaluation are pivotal in adapting programs to evolving circumstances, thus contributing to a more equitable and prosperous society.

Health Insurance & Humanoid ROBOT-Agents: A case study

The Sustainable Development Goal (SDG) Target 3.8 has emphasized the persistence of health risks as a major challenge for emerging and developing countries. This challenge necessitates the achievement of Universal Health Coverage (UHC) by enhancing the infrastructure of the public sector. However, relying solely on public sector healthcare may not be sufficient to meet the needs of large populations, particularly in underdeveloped regions. Consequently, private sector healthcare solutions have emerged to fill service gaps, but they remain unaffordable for many low-income individuals. To the end, Health insurance can play a crucial role in making these services more accessible and affordable, but it faces several challenges including poor accessibility, low awareness, lack of skilled workforce, and corruption. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies can offer solutions to these challenges. However, the adoption of technology in the insurance domain poses behavioral challenges that must be identified and addressed. This is particularly important in developing and emerging countries where markets are still underdeveloped, and information asymmetries are high. This study examines some of these challenges by studying people’s attitude towards humanoid agents. For this study a case study approach has ben used. Overall, addressing the challenges of health insurance and incorporating advanced technologies can provide a vital safety net against health risks to people.

Training as a Vital Safety Intervention Activity for Employees’ Job Commitment: A Case Study of Steel Factory

The need for human resources, otherwise referred to as employees, in the effective running of any establishment cannot be over-emphasized, especially in steel factories. Likewise, the effect of training as one of the vital safety intervention activities on accident rate reduction is highly imperative. Thus, its effect on the rate of employees' commitment to the job is understudied. Therefore, this study investigated the effect of training as one of the key safety intervention activities on the employees' commitment to the job. This study adopted the participatory approach, and the stakeholders, who are 67 employees of the steel factory, were considered for questionnaire administration and interview. This study sought information on the respondents' demographic characteristics, activities carried out, safety management, and influence on job commitments. Data collected were statistically analyzed using the SPSS 20.0 version. The respondents' ages are normally distributed around 26 – 35 and 36 – 45 age brackets, with 56.72 and 31.34 %, respectively. The majority (36 %) of this factory’s employees were in the manufacturing and melting unit, while the least (16 %) were in the packaging unit. The following percent, 7.46 and 44.78 %, strongly agreed and agreed to the management's provision of safety induction and training, while only 37.31 % disagreed. The study established that training intervention has a positive influence on the job commitment of factory employees. Thus, about 93% of the workers believed that safety training should be given priority over PPE or incentive/motivation. Keywords: Safety Management, Job Commitment, Factory Employees, Influence, Steel. Aims Research Journal Reference Format: Ajayeoba A. O., Fajobi M. O., Raheem W. A., Adebiyi K. A., Okuma S. O., Fatukasi S. O. & Ajayeoba, A.S. (2022): Training as a Vital Safety Intervention Activity for Employees’ Job Commitment. Advances in Multidisciplinary and Scientific Research. Vol. 9. No. 1, Pp 37-50. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V9N1P3x

Measurement of vibro-acoustic noise of drum brake under various contact conditions

Drum brakes are vital safety components, widely used in automobiles due to their greater availability and quick replaceability compared to disc brakes. Since automobiles are used in all weather conditions, the braking performance also changes accordingly. Penetration of water, oil, and dust between the brake lining and the drum directly affects the contact parameters. These critical contact parameters (such as friction coefficient, contact damping, and stiffness) in turn affect the dynamic and acoustic responses of the nonlinear system. In this paper, the variation in the vibro-acoustic characteristics of a drum brake due to the presence of water, oil, and dust on the brake lining is studied experimentally by measuring the radiated acoustic signals and the system response. The braking performance under different conditions is quantified based on the system response while the acoustic behaviour of the system is studied using the acoustic measurements. This study would help in determining the safety of the automobile even under such abnormal braking conditions. Further, the acoustic measurements can be used to assess the contact condition of the braking system. It is envisaged to develop an improved health monitoring system for drum brakes.

A novel method for conformity assessment testing of patient monitors for post-market surveillance purposes.

Patient monitors are medical devices used to monitor vital parameters such as heart rate, respiratory rate, blood pressure, blood oxygen saturation, and body temperature during inpatient treatment. As such, patient monitors provide physicians with information necessary to adjust the treatment as well as evaluate the overall status and recovery of the patient. Measurements made by intrinsic sensors of patient monitors must be compliant and provide reliable readings in order to ensure safety and optimal quality of care to the patients. This paper proposes a novel method for conformity assessment testing of patient monitors in healthcare institutions for post-market surveillance purposes. The method was developed on the basis of metrology characteristics of sensors used to monitor vital parameters observed by patient monitors and evaluation of their vital safety and performance parameters. In addition to the evaluation of essential safety and visual integrity of patient monitors, their performance in terms of accuracy of the readings is evaluated. The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of patient monitors as a method used during PMS contributes to significant improvement in devices' accuracy and reliability. A standardized approach in conformity assessment testing of patient monitors during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

A novel method for conformity assessment testing of mechanical ventilators for post-market surveillance purposes.

Mechanical ventilators are medical devices used in intensive care units when patients are in need of mechanical aid to facilitate the process of breathing. As the function of breathing is the exchange of gases, the mechanical ventilator takes over that function while the patient is incapable to spontaneous breathing. As these devices are used to maintain the life of patents, their performance must be ensured and there cannot be significant deviations in the volumes and pressure of gases they introduce to the patient. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. This paper proposes a novel method for conformity assessment testing of mechanical ventilators for post-market surveillance purposes. The method was developed on the basis of metrology characteristics of mechanical ventilators and evaluation of their vital safety and performance parameters. In addition to the evaluation of essential safety and visual integrity of mechanical ventilators, their performance in terms of volume of oxygen delivered to the patient as well as the flow and pressure of the delivered gas is evaluated. The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of mechanical ventilators as a method used during PMS contributes to significant improvement in devices' accuracy and reliability. A standardized approach in conformity assessment testing of mechanical ventilators during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

Numerical study of the effect of geometry on the behaviour of internally heated melt pools for in-vessel melt retention

In-vessel melt retention is a vital safety design that is incorporated in some nuclear reactors to protect against beyond-design basis accidents. This entails the retention of the molten fuel, colloquially known as corium, to be retained within the reactor pressure vessel (RPV) for extended periods of time without any release of radioactivity to the surroundings.Multiple experiments have been done to better understand this phenomenon, such as the COPRA experiment, which is simulated in this study. The study aims to analyse the COPRA experiment’s forced convection cooling (via water) while also simulating cooling via natural convection. After comparing this to the original COPRA experiment’s data, the study showcases the effect of geometry variation in such experiments by testing different RPV geometry sizes.Our results can better inform the design of future severe accident experiments while taking into account the historical aspects of severe accident research, as the popularity of Small Modular Reactors (SMRs) increases. Our analysis of the variation in geometry provides insights for RPVs that are significantly smaller than the traditional pressurised water reactors’ RPV size. The difference in cooling effect and crust formation is presented for this variation of vessel size. The difference is not significant but is noteworthy for the design of future experiments and reactors.

An Accurate and Fast Animal Species Detection System for Embedded Devices

Encounters between humans and wildlife often lead to injuries, especially in remote wilderness regions, and highways. Therefore, animal detection is a vital safety and wildlife conservation component that can mitigate the negative impacts of these encounters. Deep learning techniques have achieved the best results compared to other object detection techniques; however, they require many computations and parameters. A lightweight animal species detection model based on YOLOv2 was proposed. It was designed as a proof of concept of and as a first step to build a real-time mitigation system with embedded devices. Multi-level features merging is employed by adding a new pass-through layer to improve the feature extraction ability and accuracy of YOLOv2. Moreover, the two repeated 3 × 3 convolutional layers in the seventh block of the YOLOv2 architecture are removed to reduce computational complexity, and thus increase detection speed without reducing accuracy. Animal species detection methods based on regular Convolutional Neural Networks (CNNs) have been widely applied; however, these methods are difficult to adapt to geometric variations of animals in images. Thus, a modified YOLOv2 with the addition of deformable convolutional layers (DCLs) was proposed to resolve this issue. Our experimental results show that the proposed model outperforms the original YOLOv2 by 5.0% in accuracy and 12.0% in speed. Furthermore, our analysis shows that the modified YOLOv2 model is more suitable for deployment than YOLOv3 and YOLOv4 on embedded devices.

Preparing Counselors for Potential Violence in the Workplace: Interview With Julie S. Costopoulos, PhD

Did you know that 30 to 40% of psychologists in private practice have experienced an assault by a patient in their care? Even more alarming, more than 50% of counselors in substance-use-disorder programs have or will personally experience some form of violence in the workplace. Dr. Julie Costopoulos guest stars to unpack this situation and provide vital safety information that all current and aspiring psychologists must hear.

Muslim volunteers as social services providers during the COVID-19 pandemic in Israel

In the early stages of the COVID-19 pandemic, the Israeli government paid little attention to its impact on the Palestinian population. Allocations of state aid under-funded Palestinian communities, and all state communications and public announcements were delivered in Hebrew, effectively excluding native Arabic speakers from vital safety information. In response, Muslim volunteers and organisations filled the service and information gaps created by government neglect. In this article, we describe how Muslim volunteers organised and responded in order to support the Muslim community in Israel. Based on in-depth interviews with 19 volunteers in the early days of the pandemic, we show that their motivation was both religious and political, and firmly grounded in Muslim theology. We describe new services provided by volunteers and show that they were uniquely suited to meet Muslim community needs in the moment.