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OBJECTIVE: Schools play an essential role in providing mental health care for adolescents. School closures during COVID-19, as well as re-opening to remote-only instruction in Fall 2020, may indirectly affect the utilization of emergency psychiatric care. We examine COVID-19-related changes in emergency psychiatric care among youth during the school closure and after school reopening (with remote instruction). METHODS: We use Box-Jenkins interrupted time series methods to analyze psychiatric emergency department (ED) visits among patients 10-19 years at LAC + USC Medical Center (LAC + USC) between January 5th, 2018, and December 31st, 2020. We control for the 1st societal shutdown in LA County (i.e., the nine weeks from March 13 to May 14, 2020) when analyzing the potential return to remote school shock. RESULTS: Youth psychiatric ED visits fell by 15.3 per week during the Spring 2020 school closure (p

Background: National response time targets for ambulance services are known to be more strongly maintained in urban areas compared to rural. That may mean that responses in rural areas could be less immediate which can in turn affect survival of those experiencing cardiac arrest. Thus, analysis of variation in response times using routinely collected data can be used to understand which rural areas have the highest need for emergency intervention. In this study we have focused, given the heterogeneity of demographic make up, on a specific area of the North of England. Some areas in North England have shown to have a large proportion of cardiac arrests occurring in a rural setting, specifically, in the anonymised study region this was almost half of the cases at 46.3%. Response times to these areas were found to be over 3.5 minutes slower than for urban areas making it worthy of further exploration. Methods: A retrospective observation analysis was conducted on routinely collected data from regional ambulance services for areas within the North of England from April 2016 to March 2021. Information was collected on service and geographic characteristics for 1915 incidents. A multivariable linear mixed effect regression model was used to understand the association between geographical, service factors and response times to cardiac arrest patients. To advance previous research which up to now only used visualisations to analyse ambulance response times, the study used a mixed effects model with a variety of predictors, capturing geographical variation alongside service characteristics. Results: From the cases analysed it was found that the mean response time to scene was 9.1 minutes, with a standard deviation of 6.4 minutes. After adjustment for geographic variation and incorporating robust standard errors into the model: distance to the nearest ambulance station (coefficient = 0.61, 95% confidence interval [CI]: 0.56-0.66), urgency of the call (Category 2, second most urgent, compared to the most urgent coefficient = 1.66, 95% CI: 1.13 - 2.18), location of the nearest ambulance station to the incident and the type of crew who attended the incident (Advanced Paramedic when compared to just Paramedic, coefficient = -0.70, 95% CI: -1.24 - -0.16) were all factors which affected response times to scene. Conclusion: For each extra km the incident was away from an ambulance station, the response time to scene increased by 37 seconds. The ambulance station which displayed the largest increase in response time, Station L was 170 seconds (95% CI: 79, 261) longer than Station N, which had a median performance across all stations, as measured by median survival rate to return of spontaneous circulation (ROSC). The rural geography of the North of England means that lots of cardiac arrest incidents occur a considerable distance away from the stations, emphasising the need to use alternative emergency services technologies within these rural areas to attend to patients sooner. [ABSTRACT FROM AUTHOR]

Introduction: A disproportionate number of mental health presentations to emergency services are made by frequent presenters. No current consensus definition of a frequent presenter exists. Using a statewide population-based ambulance database, this study (i) applied previous statistical methods to determine thresholds for frequent presenters, (ii) explored characteristics of the identified frequent presenter groups compared to non-frequent presenters and (iii) assessed the reliability of these methods in predicting continued frequent presenter status over time. Methods: Statistical methods utilised in previous studies to identify frequent presenters were applied to all ambulance attendances for mental health symptoms, self-harm and alcohol and other drug issues between 1 January 2017 and 31 December 2020 in Victoria, Australia. Differences in characteristics between identified frequent and non-frequent presenter groups were determined by logistic regression analysis. The consistency of agreement of frequent presenter status over time was assessed using intraclass correlation coefficients. Results: Thresholds for frequent presenters ranged from a mean of 5 to 39 attendances per calendar year, with groups differing in size, service use and characteristics. Compared to non-frequent presenters, frequent presenters had greater odds of being female, presenting with self-harm, experiencing social disadvantage or housing issues, involving police co-attendance and being transported to hospital. All frequent presenter definitions had poor reliability in predicting ongoing frequent presentations over time. Conclusion: A range of methods can define frequent presenters according to thresholds of yearly service use. Reasons for identifying frequent presenters may influence the method chosen. Future studies should explore definitions that capture the dynamic nature of presentations by this group. [ABSTRACT FROM AUTHOR]

Emergency workers are confronted with trauma as an essential part of their work. Learning how to moderate the impact and coping with traumatic experiences is at the center of this study. The researchers empirically chart ways that emergency workers cope with trauma, process and integrate these experiences, and describe what supports their learning. Findings suggest a need to engage in alternative forms of communication, differentiating between professional and emotional feedback in order to foster transformative learning. The study reveals two different, complementary ways to constructively communicate, each with its own architecture: structured debriefs and transformative conversations. Mezirow borrowed from Habermas's idea of rational discourse in dialogue, some of which is seen in debriefs, wherein emotions are set aside in the intensity of the moment in favor of processing facts and identifying decisions and actions for the future. In transformative conversations, by contrast, emergency workers seek to understand their inner selves and make meaning of their experiences. The research shows that certain conditions for transformative conversations need to be in place in order to work through emotions and unblock cognition by recognizing and working through affect, emotion, and potential vicarious trauma. This qualitative analysis derives from 57 reflective writings collected from paramedics and firefighters. [ABSTRACT FROM AUTHOR]

We propose a new framework to address the territory design problem of emergency services in collaboration with two police authorities in Europe. Our framework serves as a strategic decision support system to assess different districting layouts, department locations, staffing decisions and dispatching strategies. First, we introduce a novel modification of the p-median problem with a combined approach to the contiguity and compactness of district layouts solvable by a commercial solver. Second, we utilize a new discrete event simulation that accounts for the variability of spatial and temporal incident patterns and driving times to evaluate the district layouts according to several criteria based upon up to 1.8 million historical incidents. Our simulation results demonstrate that our proposed district layouts can lead to a reduction of the response time by up to 14.52% while also lowering the dispatch time, the overall driving time, and the number of unanswered calls for service. Additionally, we examine the computational complexity of optimally locating district centers and analyze the more restricted problem of optimally reassigning districts to fixed district centers. [ABSTRACT FROM AUTHOR]

Background: Defence and Emergency Services personnel are exposed to a high number of traumatic events during their employment, which may result in post-traumatic stress disorder (PTSD). Art therapy serves as a possible as a trauma-focused treatment for PTSD. However, as health funding becomes more difficult to secure, there is a need to be able justify value to funding bodies. A Social Return on Investment (SROI) analysis provides the opportunity to value typically intangible social value outcomes through a ratio of X:1. Aim: The current study outlines the process and results of a SROI of a culturally informed art therapy programme for defence and emergency services personnel with PTSD symptoms in Australia. Methods: Six steps for conducting an SROI were followed: (1) establishing the scope and identifying the participants; (2) mapping the outcomes; (3) evidencing and valuing those outcomes; (4) establishing impact; (5) calculating the SROI; and (6) reporting the ratio. Results: Eight outcomes were identified, and proxy figures calculated for these outcomes. After weighting and discounting, a ratio of 3.05:1 was identified, indicating $3.05 of social value was generated for every $1 invested in the programme. Conclusion: Culturally informed art therapy offers a social return for military and emergency services personnel as a PTSD intervention. Implications for practice/policy/research: The findings of this study can be used by art therapy practitioners and programme leaders to justify art therapy practice for veterans with PTSD in policy and funding applications. They may also guide conducting SROI analyses contextualised to local cultural settings. Plain-language summary: Defence and Emergency Services personnel may be exposed to a high number of traumatic events during their employment, which can result in severe emotional distress. This distress can develop into post-traumatic stress (PTS) symptoms, or even a diagnosis of post-traumatic stress disorder (PTSD). Art therapy has been growing in popularity as a possible trauma-focused treatment for PTSD, especially for Defence and Emergency Services personnel. However, as health funding becomes more and more difficult to secure, there is a need to justify the impact of well-being programmes to funding bodies. Social Return on Investment (SROI) analysis provides the opportunity to measure and provide a monetary ratio value to typically intangible social value outcomes structurally and unambiguously. This is represented by detailing how many dollars of social value is returned for every dollar invested into the programme as a ratio of X:1. This study conducted an SROI analysis for an art therapy programme with current and former military and emergency services with PTSD in Australia. The following methods were undertaken: (1) establishing the scope and identifying the participants; (2) mapping the outcomes; (3) evidencing and valuing those outcomes; (4) establishing impact; (5) calculating the SROI; and (6) reporting the ratio of amount of social value. Conservative estimates were used. Eight outcomes were identified, and proxy figures calculated for these outcomes. After weighting and discounting, $3.05 of social value was generated for every $1 invested. This study suggests that a culturally informed art therapy as an intervention for PTSD symptoms with military and emergency services offers a social return for the money invested and may be worth funding. The results of this study can be utilised by art therapy practitioners and programme leaders to justify art therapy practice for veterans with PTSD in policy and funding applications, and guide conducting their own SROI analyses contextualised to their cultural settings. [ABSTRACT FROM AUTHOR]

Introduction: The SARS-CoV-2 pandemic presented unique challenges to the health-care system and prehospital emergency medical services. An increasing prevalence of burnout has been described, which in turn is associated with mental illness. The aim of this paper was to evaluate burnout through a sex comparison and to analyze associations of burnout with personality traits during the SARS-CoV-2 pandemic. Methods: Eighty-eight emergency physicians and field shearers of Kharkiv City (Ukraine) emergency medical services (52% women) participated in the quantitative cross-sectional study. In addition to sociodemographic and occupational data, the Maslach Burnout Inventory (MBI) and the Freiburg Personality Inventory (FPI) were applied and analyzed in the sex comparison. Correlation analyses were performed to describe the relationships between the MBI dimensions and FPI traits. Results: The average age of the respondents was 35.1 ± 13.5 years. The prevalence of burnout during the pandemic was 6.5% in women and 2.4% in men. Only the scores on the MBI dimension cynicism were significantly (p = 0.027) higher in women than in men. Two personality traits differed between sexes: inhibition and male/female self-reports. Predominantly moderate correlations were found between the FPI traits and the MBI dimensions. Conclusions: Although the prevalence of burnout in this occupational group during the pandemic was similar to prepandemic figures reported in the literature, more than half of the male and female paramedics showed average-to-high scores on the three MBI dimensions. Because burnout is associated with other mental illnesses and prolonged incapacity, workplace-based interventions should be implemented. [ABSTRACT FROM AUTHOR]

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Introduction: Telehealth has emerged as an important clinical setting for managing acute respiratory tract infections (ARIs), potentially reducing emergency department and urgent care overcrowding, and reducing nosocomial transmission. Many current algorithms for ARI management incorporate information on patient vital signs. However, the accuracy of vital signs collected by patients using readily available home devices and techniques has not been studied. Methods: A cross-sectional sample of patients seen for urgent conditions at a hospital emergency and urgent care center were given instructions and low-cost, readily available devices to collect their vital signs. A trained research coordinator collected a parallel set of vital signs using standard hospital equipment, serving as the gold standard. We analyzed the performance of patient-collected vital signs compared with vital signs collected by a trained research coordinator. Results: A total of 300 patients completed the study. Patient-collected vital signs were highly specific for traditional levels of abnormalities (HR >100 beats per min, RR >24 breaths per min, temperature >100.4 degrees Fahrenheit, oxygen saturation <94 percent); however, sensitivity was poor for elevated heart rate by pulse estimation (25%) and elevated respiratory rate (60%). Heart rate and oxygen saturation by pulse oximeter and oral temperature had higher sensitivity. Conclusions: Vital signs measured and provided by patients are not uniformly accurate, particularly when using manual techniques rather than automated devices. Telehealth algorithms that rely on these values could provide incorrect triage and management advice. [ABSTRACT FROM AUTHOR]

Young Australians living in rural and remote locations have poorer mental health outcomes and higher rates of self-harm and suicide than their major city counterparts. Significant service gaps and barriers exist in accessing general and youth-specific mental health services. With a lack of access, comes delays in treatment and associated poorer outcomes. This paper describes the characteristics of young people requiring an aeromedical retrieval (AR) for acute inpatient psychiatric care. A retrospective secondary analysis was conducted of Royal Flying Doctor Service ARs for a six-year period from 2016 to 2021. Data were summarised by demographic, geographic, and diagnostic factors. The total sample size was 1534 (60% male, 40% female; and 31% aged 12–17 years, 69% aged 18–24 years), with 668 (43.5%) affected by schizophrenia and related disorders. Port Augusta, 300 km north of Adelaide, had the highest proportion of aeromedical retrievals (4.4%). The Women's and Children's Hospital in Adelaide received the highest proportion of retrievals (25.6%). Statistically significant gender and age differences were identified as were specific high-usage geographical locations across several Australian states. AR is essential for young people in accessing specialist acute health services. Developmentally appropriate, responsive, youth mental health services are mostly located in large, already well-resourced major cities. Our study provides valuable information to assist governments, communities, and services to enhance the resources available for young people who live rurally. [ABSTRACT FROM AUTHOR]

Purpose: Cauda equina syndrome (CES) may have significant individual consequences if diagnostic delays occur. Our aim was to evaluate the presenting subjective and objective features of patients with suspected CES in comparison to those with radiologically confirmed cauda equina compression (CEC).. Methods: This was a retrospective analysis of all cases presenting with suspected CES to a tertiary emergency care unit over a two-year period. CEC was defined as radiological confirmation of CEC by Consultant Musculoskeletal (MSK) Radiologist report (MSK-CEC) and by measured canal occupancy due to an acute disc extrusion (> 75%)[measured by a Senior Spinal Surgeon (SP-CEC)]. Routine data collection was compared between categories. Chi square, multivariate regression analyses and ROC analysis of multiple predictors was performed. Results: 530 patients were included in this analysis, 60 (11.3%) had MSK-CEC, and 470 had NO- CEC. Only 43/60 (71.7%) had emergent surgery. Those with MSK-CEC and SP-CEC were statistically more likely to present with bilateral leg pain [(MSK-CEC OR 2.6, 95%CI 1.2, 5.8; p = 0.02)(SP-CEC OR 4.7, 95%CI 1.7, 12.8; p = 0.003)]; and absent bilateral ankle reflexes [(MSK-CEC OR 4.3; 95% CI 2.0, 9.6; p < 0.001)(SP CEC OR 2.5; 95%CI 1.0, 6.19; p = 0.05)] on multivariate analysis. The ROC curve analysis acceptable diagnostic utility of having SP-CEC when both are present [Area under the curve 0.72 (95%CI 0.61, 0.83); p < 0.0001]. Conclusion: This study suggests that in those presenting with CES symptoms, the presence of both bilateral leg pain and absent ankle reflexes pose an acceptable diagnostic tool to predict a large acute disc herniation on MRI scan.. Highlights: The most predictive features of radiological cauda equina compression were bilateral leg pain and absent bilateral ankle reflexes when patients present with other features of suspected CES (including any of urinary, bowel, sexual dysfunction and/or perineal sensory change). Radiological definitions of cauda equina compression vary across studies making comparisons difficult across existing evidence. In cases where there is progression of symptoms, in severity, distribution or laterality of symptoms, we would suggest a low threshold for same-day emergency imaging. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To recognise that overcrowded emergency departments result in ambulance handover delays causing avoidable harm to patients awaiting assessment • To identify how forward planning and anticipatory escalation processes can reduce the potential for patients to experience avoidable harm • To learn how a strategic quality improvement project improved patient safety and the working lives of front-line healthcare professionals. Ambulance handover delays arise when emergency departments become overcrowded as patients waiting prolonged periods for admission occupy clinical cubicles designed to facilitate the assessment and treatment of emergency arrivals. In response, many organisations become reliant on temporarily lodging acutely unwell patients awaiting admission in undesignated areas for care such as corridors, to provide additional space. This results in a significant risk of avoidable harm, indignity and psychological trauma for patients and has a negative effect on the well-being of healthcare professionals, since unacceptable standards of care become normalised. A two-phase strategic quality improvement project was implemented at the authors’ acute trust. Ambulance handover data from between 2 November 2020 and 26 July 2021 provided a benchmark for the project. The first phase was implemented between 2 November 2021 and 26 July 2022 and aimed to reduce 60-minute ambulance handover delays. The second phase was implemented between 2 November 2022 and 26 July 2023 and aimed to eradicate 60-minute ambulance handover delays and improve overall performance. Phase one resulted in a 32% reduction in 60-minute ambulance handover delays. Phase two resulted in a 97% reduction in 60-minute ambulance handover delays. Over the course of the project there was a 24% increase in handovers completed within 15 minutes. This project demonstrates how strategic planning and collaboration between healthcare teams can reduce the potential for avoidable patient harm, while simultaneously promoting workforce well-being and retention. [ABSTRACT FROM AUTHOR]

Objective: The authors sought to investigate whether utilization of inpatient occupational therapy (OT) was associated with reduced risk for 30-day psychiatric readmission in the Veterans Health Administration (VHA). Methods: The authors conducted a secondary analysis of VHA medical record data for veterans who received inpatient psychiatric care from 2015 to 2020 (N=176,889). Mixed-effects logistic regression was used to model psychiatric readmission within 30 days of discharge (yes or no) as a function of inpatient psychiatric OT utilization (none, one, two, three, or four or more encounters) and other care utilization (e.g., previous psychiatric hospitalization), as well as clinical (e.g., primary diagnosis), sociodemographic (e.g., race-ethnicity), and facility (e.g., complexity) characteristics. Sensitivity analyses were conducted to evaluate the robustness of findings (e.g., stratification by discharge disposition). Results: Relatively few veterans received inpatient psychiatric OT (26.2%), and 8.4% were readmitted within 30 days. Compared with veterans who did not receive inpatient psychiatric OT, those with one (OR=0.76), two (OR=0.64), three (OR=0.67), or four or more encounters (OR=0.64) were significantly (p<0.001) less likely to be readmitted within 30 days. These findings were consistent across all sensitivity analyses. Conclusions: Veterans who received inpatient OT services were less likely to experience psychiatric readmission. A clear dose-response relationship between inpatient psychiatric OT and readmission risk was not identified. These findings suggest that OT services may facilitate high-value inpatient psychiatric care in the VHA by preventing readmissions that stymie recovery and incur high costs. Future research may establish the causality of this relationship, informing policy regarding increased access to inpatient psychiatric OT. [ABSTRACT FROM AUTHOR]

Abstract Background National response time targets for ambulance services are known to be more strongly maintained in urban areas compared to rural. That may mean that responses in rural areas could be less immediate which can in turn affect survival of those experiencing cardiac arrest. Thus, analysis of variation in response times using routinely collected data can be used to understand which rural areas have the highest need for emergency intervention. In this study we have focused, given the heterogeneity of demographic make up, on a specific area of the North of England. Some areas in North England have shown to have a large proportion of cardiac arrests occurring in a rural setting, specifically, in the anonymised study region this was almost half of the cases at 46.3%. Response times to these areas were found to be over 3.5 minutes slower than for urban areas making it worthy of further exploration. Methods A retrospective observation analysis was conducted on routinely collected data from regional ambulance services for areas within the North of England from April 2016 to March 2021. Information was collected on service and geographic characteristics for 1915 incidents. A multivariable linear mixed effect regression model was used to understand the association between geographical, service factors and response times to cardiac arrest patients. To advance previous research which up to now only used visualisations to analyse ambulance response times, the study used a mixed effects model with a variety of predictors, capturing geographical variation alongside service characteristics. Results From the cases analysed it was found that the mean response time to scene was 9.1 minutes, with a standard deviation of 6.4 minutes. After adjustment for geographic variation and incorporating robust standard errors into the model: distance to the nearest ambulance station (coefficient = 0.61, 95% confidence interval [CI]: 0.56-0.66), urgency of the call (Category 2, second most urgent, compared to the most urgent coefficient = 1.66, 95% CI: 1.13 - 2.18), location of the nearest ambulance station to the incident and the type of crew who attended the incident (Advanced Paramedic when compared to just Paramedic, coefficient = -0.70, 95% CI: -1.24 - -0.16) were all factors which affected response times to scene. Conclusion For each extra km the incident was away from an ambulance station, the response time to scene increased by 37 seconds. The ambulance station which displayed the largest increase in response time, Station L was 170 seconds (95% CI: 79, 261) longer than Station N, which had a median performance across all stations, as measured by median survival rate to return of spontaneous circulation (ROSC). The rural geography of the North of England means that lots of cardiac arrest incidents occur a considerable distance away from the stations, emphasising the need to use alternative emergency services technologies within these rural areas to attend to patients sooner.

Abstract Background As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient’s health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient’s recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient’s health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient’s sensitive information in the blockchain network. Results The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient’s sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals. Conclusions The proposed mechanism informs proactively the emergency team of professional clinicians about patients’ critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users’ trust to the access control mechanism.

Abstract Natural and artificial (human‐made) disasters have been steadily increasing all over the world, signifying the importance of providing reliable and energy friendly communication network to survivors in the aftermath of a disaster. On the other hand, low‐battery devices running optimised link state routing (OLSR) protocol often experience quick power failure which restricts their ability to communicate for a necessary period during rescue operations. To extend the lifespans and prioritise message delivery on low‐battery devices, the authors examine disaster scenario optimised link state routing (DS‐OLSR) protocol ALERT message and propose an innovative solution to prioritise messages based on the device battery energy level, leading to more energy conservation, packet delivery as well as better emotional state of survivors. An ALERT message is a novel message type added to mobile ad‐hoc network's (MANET) popular OLSR protocol for energy efficiency. The proposed DS‐OLSR Protocol and Message Prioritisation (DS‐OLSRMP) as an extension of DS‐OLSR modifies the multipoint relay mechanism and uses a prioritisation technique which classify nodes into four priority groups: Critical, High, Medium, and Low priorities. These priority groups help in prioritising both message delivery and message status notifications for devices with low battery energy. The DS‐OLSRMP was implemented in a Network Simulator, version 3.29 and compared with DS‐OLSR, OLSRv1 and OLSRv2. The simulation results show that DS‐OLSRMP performs better than DS‐OLSR, OLSRv1 and OLSRv2 in terms of energy conservation and packets delivery in the simulation of both sparse and dense network scenarios.

Abstract Background Crowding has been a longstanding issue in emergency departments. To address this, a fast-track system for avoidable patients is being implemented in the Paediatric Emergency Department where our study is conducted. Our goal is to develop an optimized Decision Support System that helps in directing patients to this fast track. We evaluated various Machine Learning models, focusing on a balance between complexity, predictive performance, and interpretability. Methods This is a retrospective study considering all visits to a university-affiliated metropolitan hospital’s PED between 2014 and 2019. Using information available at the time of triage, we trained several models to predict whether a visit is avoidable and should be directed to a fast-track area. Results A total of 507,708 visits to the PED were used in the training and testing of the models. Regarding the outcome, 41.6% of the visits were considered avoidable. Except for the classification made by triage rules, i.e. considering levels 1,2, and 3 as non-avoidable and 4 and 5 as avoidable, all models had similar results in model’s evaluation metrics, e.g. Area Under the Curve ranging from 74% to 80%. Conclusions Regarding predictive performance, the pruned decision tree had evaluation metrics results that were comparable to the other ML models. Furthermore, it offers a low complexity and easy to implement solution. When considering interpretability, a paramount requisite in healthcare since it relates to the trustworthiness and transparency of the system, the pruned decision tree excels. Overall, this paper contributes to the growing body of research on the use of machine learning in healthcare. It highlights practical benefits for patients and healthcare systems of the use ML-based DSS in emergency medicine. Moreover, the obtained results can potentially help to design patients’ flow management strategies in PED settings, which has been sought as a solution for addressing the long-standing problem of overcrowding.

ABSTRACT Lesbian, gay, bisexual, transgender, queer and other diverse sexual orientations and gender identity groups (LGBTQ+) face high rates of poor mental health. In the most severe and emergent of instances, inpatient psychiatric care may be required. LGBTQ+ people report experiences of mistreatment in healthcare settings broadly, such as denial of healthcare services and harassment from healthcare providers and other patients. However, little is known about the experiences of LGBTQ+ people in inpatient psychiatric care settings, specifically. The purpose of this review was to assess the existing literature for descriptions of LGBTQ+ people's experiences within inpatient psychiatric care. We searched multiple databases (i.e., PubMed, PsychINFO, CINAHL, Web of Science and Google Scholar) for peer‐reviewed articles that described the experiences of LGBTQ+ people within inpatient psychiatric care that were published in English. The included articles (N = 14) were analysed using a conceptual model of stigma and organised within those strata (structural, interpersonal and individual stigma) across the inpatient experience, (admission, inpatient unit, and discharge). Themes identified included: noninclusive intake tools and pervasive misgendering during the admission process; lack of healthcare infrastructure, inadequate training and lack of cultural humility, pervasive discrimination and victimization, silencing of LGBTQ+ patients, and feelings of fear and shame while on inpatient units, and lack of community resources during the discharge process. Clinicians should consider the perspectives and experiences of LGBTQ+ people to enact identity‐affirming care practices that may increase mental healthcare engagement and improve long‐term mental health outcomes. [ABSTRACT FROM AUTHOR]

Background: As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient's health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods: A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient's recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient's health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient's sensitive information in the blockchain network. Results: The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient's well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient's sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals. Conclusions: The proposed mechanism informs proactively the emergency team of professional clinicians about patients' critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users' trust to the access control mechanism. [ABSTRACT FROM AUTHOR]

Purpose: To present the social network analysis-based approach used to create a new workspace layout for three hospital services as part of a campus expansion at a large tertiary care public hospital. Objective: To analyze the relationships among service members across four healthcare resilience abilities (monitor, anticipate, respond, and learn) and utilize network metrics to indicate the suitability of a shared workspace layout for the services. Background: The hospital expanded by 70%, providing space for relocating key services—the rapid response team, medical on-call team, and nursing supervision. Initial observations suggested a shared workspace layout based on anecdotal evidence. Method: Stakeholders have reached a consensus on a three-stage process to assess the suitability of a shared workspace layout for these services: first, collecting data on social interactions with a focus on resilience abilities; second, presenting layout alternatives based on sociograms; and third, evaluating these alternatives and devising a strategy for allocating personnel to shifts based on a resilience score derived from social network metrics. Case Study: The examination of social network metrics allowed identifying key individuals contributing to the overall resilience of the three services. Sociograms provided visual representations of how these individuals were spatially distributed within the shared layout. Discussion: The process was designed to shape a resilient layout and incorporated initial data, preferences, and constraints into layout proposals. Additionally, it utilized a resilience score from existing literature to formulate a strategy for staff allocation to shifts, ensuring consistent collective resilience ability across all shifts. [ABSTRACT FROM AUTHOR]

The Internet of vehicles (IoV) generates a massive amount of network connections and diversified data. To address the challenge that a single agent struggles to collect channel state information and perform service-differentiated resource allocation and link scheduling in dynamic scenarios, a multiagent deep reinforcement learning-based service-differentiated resource allocation method for IoV is proposed. This method aims to maximize the successful delivery rate of V2V link data packets and the total capacity of V2I links, under the constraint of minimizing interference to emergency service links. It employs deep reinforcement learning algorithms to optimize spectrum allocation and power selection strategies in a single-antenna vehicle-mounted network where multiple cellular users and device-to-device users coexist. Each agent is trained using deep Q-network(DQN), and they interact with the communication environment collectively, achieving coordination through a global reward function. Simulation results show that, in high-load scenarios, compared to traditional random allocation schemes, this scheme increases the total throughput of V2I links by 3.76 Mbps, improves the packet delivery rate of V2V links by 17.1%, and reduces the interference to emergency service links by 1.42 dB compared to ordinary links. This achieves priority guarantee for emergency service links and effectively enhances the overall transmission capacity of V2I and V2V links. [ABSTRACT FROM AUTHOR]

As demands in the way of healthcare delivery increase, nurse practitioners have become essential components in optimizing access to healthcare and improving outcomes around the globe. The authors recognize the vital role nurse practitioners play globally and that education, practice, and credentialing differ significantly across countries. Many countries have a wide variation in educational requirements, resulting in a lack of adequate advanced practice nursing training and regulation of practice. Additionally, many countries are experiencing increased demands in the form of emergency services, with long waiting times and severe crowding in emergency departments. Worldwide, many programs are being developed to educate advanced practice nurses on improving emergency medical care, but developing such programs is a complex process This article aims to discuss the scope of practice and roles of advanced practice nurses globally. We will also attempt to clarify and improve these global standards and discuss competencies in this role. [ABSTRACT FROM AUTHOR]

The utilization of digital health in India is playing a crucial role in enhancing healthcare services by transitioning from the current inadequate public health structure to a more efficient and patient-centric system. Digital health includes various digital tools, such as electronic health records (EHRs), telemedicine, mobile health applications, health information exchange systems, and other technological advancements to improve access, efficiency, and quality of healthcare delivery. This study investigates the prospects and challenges encountered by the newly-digitized Maharashtra Emergency Medical Services (MEMS). Utilizing the 38,823 MEMS calls from November 2022, this study investigates the current status of emergency service delivery mechanisms in Maharashtra. Through spatial analyses, this study also explores the causes behind calls. The findings of the study show that calls for 108 ambulance services were distributed across the districts and had variable service delivery time periods. Current challenges to the system arise from various urban and healthcare infrastructure problems, as well as socio-cultural challenges. Implementation of the digitized MEMS system reveals key factors that influence the service's success, assisting the policymakers and health administrators in identifying and further improving the service. [ABSTRACT FROM AUTHOR]

Customer satisfaction is a universal concern that impacts enterprises of all sizes, whether they are for-profit or non-profit, and whether they operate on a global or local scale. Therefore, it is crucial for every firm, including hospitals, to have a comprehension of the aspects that impact customer satisfaction. A study proposed that the correlation between the use of resources and patient satisfaction may be due to the perception that patients have. They believe that doctors who allow them to stay in the hospital for a longer period or conduct additional tests demonstrate a greater concern for their well-being. Consequently, these patients feel more confident that their concerns are being acknowledged and dealt with by their physicians. A comprehensive analysis of primary care patients revealed numerous studies that established a direct correlation between meeting patient expectations and their overall satisfaction with the care received. However, it was also observed that physicians often misinterpret patients' desire for information as an expectation for a specific course of action. [ABSTRACT FROM AUTHOR]

Decision‐making in emergency situations, such as those faced by fire, police, and health service personnel, presents unique challenges due to the high‐stakes and time‐pressured environment. Here we aim to better understand what emergency responders regard as constituting 'good' and 'bad' decisions in emergency situations. We administered a modified Delphi study, eliciting opinions from decision‐makers across all these sectors towards consensus around the key elements of good and bad decision‐making. Participants were first asked to define what makes a 'good' and a 'bad' decision, and subsequently to identify the top five most important elements of each. While consensus was not found, important insights were identified that can assist improve the standard of decision making at all levels of emergency response and management. We observed (i) a lack of a common understanding between participants of what a decision is, and how a decision differs from pre‐decision and postdecision components; (ii) responses varied according to whether a free text description or the identification of separate elements was requested; (iii) respondents valued 'goodness' across different and at times unrelated components a scaled measure of decision quality as opposed to a binary evaluation of 'rightness'; and (iv) pre‐ and postdecision elements are considered more important than the decision itself when determining the quality of a decision. To address the issues highlighted by the study we recommend improvements in training, and improvements to organization doctrine related to decision making, risk tolerance, assessment of decision quality and development of intent‐based or principle‐based operational guidance. Ultimately, incident controllers from all services must remain cognizant that they will face considerable scrutiny if they cannot explain how they arrived at the decision they made. [ABSTRACT FROM AUTHOR]

This study investigates the spatiotemporal variations in ambulance demand in southern Sweden, and how land use factors and sociodemographic factors are associated with variations in demand. A standardized ambulance demand ratio (SADR) indicating whether the risk of requiring an ambulance exceeded the expected was calculated using ambulance dispatch data in southern Sweden in 2018. Spatial analysis using a geographic information system (GIS) and spatial statistical methods underlie the methodology. Findings show that although rural areas are associated with a higher risk of requiring an ambulance (especially in daytime), this risk may be obscured by the more numerous dispatches occurring in urban areas. The increased risk in areas with bars and nightclubs, schools, industrial areas or road crossings reflects temporal variations in people's routine activities. By dictating how and when population's reshuffle, land use factors can help explain variations in risk for requiring an ambulance. Confirming previous research, areas with a higher share of older adults are associated with a higher ambulance demand while areas with a high median income are associated with lower demand. Population size may be an inadequate indicator of ambulance demand. Planning should account for daily changes in population sizes and compositions as this impacts ambulance demand. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To understand why demand for ambulances has increased in recent years • To reflect on why people experiencing mental health issues often contact emergency services • To learn how mental health professionals working within 999 call centres can help to reduce demand on emergency services. Demand for ambulances has increased significantly in recent years due, for example, to ongoing public health issues and lack of availability of alternative healthcare services. However, as demand increases, so too do ambulance waiting times, partly due to significant pressures on emergency departments (EDs) resulting in handover delays. People experiencing mental health distress who cannot access the care they need often contact ambulance services or present to the ED. Ambulance trusts across the UK are attempting to address this by employing mental health professionals (MHPs) in various capacities. In this article, the authors explore some of the issues related to mental health-related calls to 999 services. The authors then describe a service improvement initiative in Wales which involves MHPs working in 999 call centre clinical support desk services to improve the quality of care delivered to people with mental health issues and reduce demand on ambulance and ED services. [ABSTRACT FROM AUTHOR]

Natural and artificial (human‐made) disasters have been steadily increasing all over the world, signifying the importance of providing reliable and energy friendly communication network to survivors in the aftermath of a disaster. On the other hand, low‐battery devices running optimised link state routing (OLSR) protocol often experience quick power failure which restricts their ability to communicate for a necessary period during rescue operations. To extend the lifespans and prioritise message delivery on low‐battery devices, the authors examine disaster scenario optimised link state routing (DS‐OLSR) protocol ALERT message and propose an innovative solution to prioritise messages based on the device battery energy level, leading to more energy conservation, packet delivery as well as better emotional state of survivors. An ALERT message is a novel message type added to mobile ad‐hoc network's (MANET) popular OLSR protocol for energy efficiency. The proposed DS‐OLSR Protocol and Message Prioritisation (DS‐OLSRMP) as an extension of DS‐OLSR modifies the multipoint relay mechanism and uses a prioritisation technique which classify nodes into four priority groups: Critical, High, Medium, and Low priorities. These priority groups help in prioritising both message delivery and message status notifications for devices with low battery energy. The DS‐OLSRMP was implemented in a Network Simulator, version 3.29 and compared with DS‐OLSR, OLSRv1 and OLSRv2. The simulation results show that DS‐OLSRMP performs better than DS‐OLSR, OLSRv1 and OLSRv2 in terms of energy conservation and packets delivery in the simulation of both sparse and dense network scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations. [ABSTRACT FROM AUTHOR]

Background: Crowding has been a longstanding issue in emergency departments. To address this, a fast-track system for avoidable patients is being implemented in the Paediatric Emergency Department where our study is conducted. Our goal is to develop an optimized Decision Support System that helps in directing patients to this fast track. We evaluated various Machine Learning models, focusing on a balance between complexity, predictive performance, and interpretability. Methods: This is a retrospective study considering all visits to a university-affiliated metropolitan hospital's PED between 2014 and 2019. Using information available at the time of triage, we trained several models to predict whether a visit is avoidable and should be directed to a fast-track area. Results: A total of 507,708 visits to the PED were used in the training and testing of the models. Regarding the outcome, 41.6% of the visits were considered avoidable. Except for the classification made by triage rules, i.e. considering levels 1,2, and 3 as non-avoidable and 4 and 5 as avoidable, all models had similar results in model's evaluation metrics, e.g. Area Under the Curve ranging from 74% to 80%. Conclusions: Regarding predictive performance, the pruned decision tree had evaluation metrics results that were comparable to the other ML models. Furthermore, it offers a low complexity and easy to implement solution. When considering interpretability, a paramount requisite in healthcare since it relates to the trustworthiness and transparency of the system, the pruned decision tree excels. Overall, this paper contributes to the growing body of research on the use of machine learning in healthcare. It highlights practical benefits for patients and healthcare systems of the use ML-based DSS in emergency medicine. Moreover, the obtained results can potentially help to design patients' flow management strategies in PED settings, which has been sought as a solution for addressing the long-standing problem of overcrowding. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusions To determine whether availability of behavioral health crisis care services is associated with changes in emergency department (ED) utilization.We used longitudinal panel data (2016–2021) on ED utilization from the Healthcare Cost and Utilization Project's State ED Databases and a novel dataset on crisis care services compiled using information from the Substance Abuse and Mental Health Services Administration's National Directories of Mental Health Treatment Facilities. A total of 1002 unique zip codes from Arizona, Florida, Kentucky, Maryland, and Wisconsin were included in our analyses.To estimate the effect of crisis care availability on ED utilization, we used a linear regression model with zip code and year fixed effects and standard errors accounting for clustering at the zip code‐level. ED utilization related to mental, behavioral, and neurodevelopmental (MBD) disorders served as our primary outcome. We also examined pregnancy‐related ED utilization as a nonequivalent dependent variable to assess residual bias in effect estimates.We extracted data on crisis care services offered by mental health treatment facilities (n = 14,726 facility‐years) from the National Directories. MBD‐related ED utilization was assessed by applying the Clinical Classification Software Refined from the Healthcare Cost and Utilization Project to the primary ICD‐10‐CM diagnosis code on each ED encounter (n = 101,360,483). All data were aggregated to the zip code‐level (n = 6012 zip‐years).The overall rate of MBD‐related ED visits between 2016 and 2021 was 1610 annual visits per 100,000 population. Walk‐in crisis stabilization services were associated with reduced MBD‐related ED utilization (coefficient = −0.028, p = 0.009), but were not significantly associated with changes in pregnancy‐related ED utilization.Walk‐in crisis stabilization services were associated with reductions in MBD‐related ED utilization. Decision‐makers looking to reduce MBD‐related ED utilization should consider increasing access to this promising alternative model. [ABSTRACT FROM AUTHOR]