Prehospital Decision Research Articles

Anticipated prehospital decision delay in response to different symptom clusters in acute coronary syndrome: Results from the Spanish Cardiobarometer study

Background and objectiveReducing patient decision delay – the time elapsed between symptom onset and the moment the patient decides to seek medical attention – can help improve acute coronary syndrome survival. Patient decision delay is typically investigated in retrospective studies of acute coronary syndrome survivors that are prone to several biases. To offer an alternative approach, the goal of this research was to investigate anticipated patient decision delay in the general population in response to different symptom clusters. MethodsWe developed scenarios representing four commonly experienced symptom clusters: classic (chest symptoms only), heavy (a large number of very intense symptoms including chest pain), diffuse (mild symptoms including chest pain), and weary (mild symptoms without clear chest involvement). The scenarios were administered in random order in a representative survey of 1002 adults ≥55 years old from the non-institutionalized general population in Spain. We measured help-seeking intentions, anticipated patient decision delay (waiting >30 min to seek help), and symptom attribution. ResultsPatient decision delay was most common in the diffuse scenario (55%), followed by the weary (34%), classic (22%), and heavy (11%) scenarios. Attributing the symptoms to a cardiovascular cause and intentions to call emergency services were least frequent in the weary and diffuse scenarios. Women were less likely to intend to seek help than men in the classic (OR = 0.48, [0.27, 0.85], diffuse (OR = 0.67, [0.48, 0.92]), and weary (OR = 0.66, [0.44, 0.98]) scenarios, despite being more likely to attribute symptoms to cardiovascular causes. Participants with traditional cardiovascular risk factors (e.g., diabetes, hypercholesterolemia, hypertension) reported faster help-seeking, whereas participants with obesity and history of depression were more likely to delay. DiscussionThe diverse manifestations of acute coronary syndrome generate fundamentally different appraisals. Anticipated patient decision delay varies as a function of socio-demographic characteristics and medical history, supporting findings from studies with patients who experienced ACS. Measuring anticipated patient decision delay in the general population can help reveal potential barriers to help-seeking and capture effects of population interventions.

Impact of Prehospital Ultrasound Training on Simulated Paramedic Clinical Decision-Making.

When used appropriately, focused limited-scope ultrasound exams could potentially provide paramedics with accurate and actionable diagnostic information to guide prehospital decision-making. In this study we aimed to investigate the impact of a 13-hour prehospital ultrasound training course on the simulated clinical decision-making of paramedics as well as their ultrasound skills, knowledge, and self-confidence. We evaluated the ultrasound competence of 31 participants using post-course written and practical assessments. Written clinical decision scenarios were administered pre- and post-training. Post-training scenarios included an uninterpreted ultrasound clip to aid decision-making. Scenarios included extended focused assessment with sonography in trauma, pulmonary exam, and focused echocardiography combined with carotid pulse check exams. Correct answers to scenarios were defined as those selected by a veteran emergency physician. Participants also indicated their confidence in each of their decisions using a Likert scale. Training yielded a statistically significant increase in both mean scenario score (35.5% absolute increase) and mean participant self-confidence (15.8% relative increase), across all exam/decision types assessed (P ≤ 0.001). The focused pulmonary exam yielded the largest increase in both mean score improvement (59.7% absolute increase) and paramedic confidence in their decisions (28.6% increase). Trained paramedics can perform focused ultrasound exams and accurately interpret and apply actionable exam findings in the context of written scenarios. Analysis through our model characterized the theoretical clinical yield of each prehospital ultrasound exam and demonstrated how each exam may provide improved decision accuracy in several specific simulated clinical contexts. These results provide support for growing evidence that focused limited-scope ultrasound may be an effective prehospital diagnostic tool in the hands of trained paramedics.

Implementation of a Novel Prehospital Clinical Decision Tool and ECG Transmission for STEMI Significantly Reduces Door-to-Balloon Time and Sex-Based Disparities

Background An important method employed to reduce door to balloon time (DTBT) for ST segment elevation Myocardial Infarctions (STEMIs) is a prehospital MI alert. The purpose of this retrospective study was to examine the effects of an educational intervention using a novel decision support method of STEMI notification and prehospital electrocardiogram (ECG) transmission on DTBT. Methods An ongoing database (April 4, 2000 – present) is maintained to track STEMI alerts. In 2007, an MI alert program began; emergency medicine physicians could activate a “prehospital MI alert”. In October 2015, modems were purchased for Emergency Medical Services personnel to transmit ECGs. There was concurrent implementation of a decision support tool for identifying STEMI. Sex was assigned as indicated in the medical record. Data were analyzed in two groups: Pre-2016 (PRE) and 2016–2022 (POST). Results In total, 3,153 patients (1,301 PRE; 1,852 POST) were assessed; the average age was 65.2 years, 32.6% female, 87.7% white with significant differences in age and race between the two cohorts. Of the total 3,153 MI alerts, 239 were false activations, leaving 2,914 for analysis. 2,115 (72.6%) had cardiac catheterization while 16 (6.7%) of the 239 had a cardiac catheterization. There was an overall decrease in DTBT of 27.5% PRE to POST of prehospital ECG transmission (p < 0.001); PRE median time was 74.5 min vs. 55 min POST. There was no significant difference between rates of cardiac catheterization PRE and POST for all patients. After accounting for age, race, and mode of arrival, DTBT was 12.2% longer in women, as compared to men (p < 0.001) PRE vs. POST. DTBT among women was significantly shorter when comparing PRE to POST periods (median 77 min vs. 60 min; p = 0.0001). There was no significant sex difference in the proportion of those with cardiac catheterization between the two cohorts (62.5% vs. 63.5%; p = 0.73). Conclusion Introduction of a decision support tool with prehospital ECG transmission with prehospital ECG transmission decreased overall DTBT by 20 min (27.5%). Women in the study had a 17-minute decrease in DTBT (22%), but their DTBT remained 12.2% longer than men for reasons that remain unclear.

Prehospital stroke diagnostics using three different simulation methods: A pragmatic pilot study.

The optimal pathway for ultra-early diagnostics and treatment in patients with acute stroke remains uncertain. The aim of this study was to investigate how three different methods of simulated, rural prehospital computed tomography (CT) affected the time to prehospital treatment decision in acute stroke. In this pragmatic, simulation, pilot study of prehospital CT we investigated a conventional ambulance with transport to a standard care rural stationary CT machine managed by paramedics, a Mobile Stroke Unit (MSU), and a helicopter with a simulated CT machine. Each modality completed 20 real-life dispatches combined with simulation of predetermined animated patient cases with acute stroke symptoms and CT images. The primary endpoint of the study was the time from alarm to treatment decision. Median time from alarm to the treatment decision differed significantly between the three groups (p = 0.0005), with 38 min for rural CT, 33 min for the MSU, and 30 min for the helicopter. There was no difference in time when comparing rural CT with MSU, nor when comparing the MSU with the helicopter. There was a difference in time to treatment decision between the rural CT and the helicopter (p < 0.0001). The helicopter had significantly lower estimated time from treatment decision to hospital (p = 0.001). Prehospital CT can be organized in several ways depending on geography, resources and need. Further research on paramedic run rural CT, MSU in rural areas, and helicopter CT is needed to find the optimal strategy.

Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study

A myriad of early warning scores (EWSs) exist, yet there is a need to identify the most clinically valid score to be used in prehospital respiratory assessments to estimate short-term and midterm mortality, intensive-care unit admission, and airway management in life-threatening acute respiratory distress. This is a prospective, observational, multicentre, ambulance-based, external validation study performed in 44 ambulance services and four hospitals across three Spanish provinces (ie, Salamanca, Segovia, and Valladolid). We identified adults (ie, those aged 18 years and older) discharged to the emergency department with suspected acute respiratory distress. The primary outcome was 2-day all-cause in-hospital mortality, for all the patients or according to prehospital respiratory conditions, including dyspnoea, chronic obstructive pulmonary disease (COPD), COVID-19, other infections, and other conditions (asthma exacerbation, haemoptysis, and bronchoaspirations). 30-day mortality, intensive-care unit admission, and invasive and non-invasive mechanical ventilation were secondary outcomes. Eight EWSs, namely, the National Early Warning Score 2, the Modified Rapid Emergency Medicine Score, the Rapid Acute Physiology Score, the Quick Sequential Organ Failure Assessment Score, the CURB-65 Severity Score for Community-Acquired Pneumonia, the BAP-65 Score for Acute Exacerbation of COPD, the Quick COVID-19 Severity Index, and the Modified Sequential Organ Failure Assessment (mSOFA), were explored to determine their predictive validity through calibration, clinical net benefit as determined through decision curve analysis, and discrimination analysis (area under the curve of the receiver operating characteristic [AUROC], compared with Delong's test). Between Jan 1, 2020, and Nov 31, 2022, 902 patients were enrolled. The global 2-day mortality rate was 87 (10%); in proportion to various respiratory conditions, the rates were 35 (40%) for dyspnoea, nine (10%) for COPD, 13 (15%) for COVID-19, 28 (32%) for other infections, and two (2%) for others conditions. mSOFA showed the best calibration, a higher net benefit, and the best discrimination (AUROC 0·911, 95% CI 0·86-0·95) for predicting 2-day mortality, and its discrimination was statistically significantly more accurate (p<0·0001) compared with the other scores. The performance of mSOFA for predicting 2-day mortality was higher than the other scores when considering the prehospital respiratory conditions, and was also higher for the secondary outcomes, except for non-invasive mechanical ventilation. Our results showed that mSOFA outperformed other EWSs. The inclusion of mSOFA in prehospital decision making will entail a quick identification of patients in acute respiratory distress at high risk of deterioration, allowing prioritisation of resources and patient care. Gerencia Regional de Salud, Public Health System of Castilla y León (GRS Spain). For the Spanish translation of the abstract see Supplementary Materials section.

Prehospital COVID-19 patients discharged at the scene – an observational study

BackgroundEmergency medical services (EMS) were the first point of contact for many COVID-19 patients during the pandemic. The aim of this study was to investigate whether the non-conveyance decision of a COVID-19 patient was more frequently associated with a new EMS call than direct ambulance transport to the hospital.MethodsAll confirmed COVID-19 patients with an EMS call within 14 days of symptom onset were included in the study. Patients were compared based on their prehospital transport decision (transport vs. non-conveyance). The primary endpoint was a new EMS call within 10 days leading to ambulance transport.ResultsA total of 1 286 patients met the study criteria; of these, 605 (47.0%) were male with a mean (standard deviation [SD]) age of 50.5 (SD 19.3) years. The most common dispatch codes were dyspnea in 656 (51.0%) and malaise in 364 (28.3%) calls. High-priority dispatch was used in 220 (17.1%) cases. After prehospital evaluation, 586 (45.6%) patients were discharged at the scene. Oxygen was given to 159 (12.4%) patients, of whom all but one were transported.A new EMS call leading to ambulance transport was observed in 133 (10.3%) cases; of these, 40 (30.1%) were in the group primarily transported and 93 (69.9%) were among the patients who were primarily discharged at the scene (p<.001). There were no significant differences in past medical history, presence of abnormal vital signs, or total NEWS score. Supplemental oxygen was given to 33 (24.8%) patients; 3 (2.3%) patients received other medications.ConclusionNearly half of all prehospital COVID-19 patients could be discharged at the scene. Approximately every sixth of these had a new EMS call and ambulance transport within the following 10 days. No significant deterioration was seen among patients primarily discharged at the scene. EMS was able to safely adjust its performance during the first pandemic wave to avoid ED overcrowding.

Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions

&#x0D; Emergency department (ED) overcrowding occurs when the demand for health services in the ED exceeds the capacity of the ED, hospital, or community to deliver quality care in a reasonable amount of time. Overcrowding is worsening in jurisdictions across Canada and there is a need to address its many causes and identify potential solutions.&#x0D; This report uses a modified version of a conceptual model developed by Asplin et al. (2003) that organizes the emergency care system into 3 interdependent parts: input (arrival to the ED), throughput (flowing through the ED), and output (leaving the ED). We also examined an additional fourth part related to contextual factors and systems that affect overcrowding but lay outside of input, throughout, and output.&#x0D; &#x0D; Examples of factors include, but are not limited to, increased complexity of needs (input), diagnostic testing and procedures (throughput), boarding (output), and limited resources for mental health and substance use (outside the ED).&#x0D; Examples of interventions that were effective in some settings include, but are not limited to, prehospital decision-making by first responders, which reduced ED visits (input); short stay crisis units for people experiencing mental health challenges, which improved emergency department length of stay, wait times, boarding, and patient safety (throughput); ED-based discharge planning, which reduced ED return visits (output); and time-based policy reforms, which reduced ED length of stay (outside the ED).&#x0D; &#x0D; &#x0D; Most of the factors we identified in the published literature existed either outside of the ED or at the interface of the ED and other health care services (input and output), whereas most of the interventions we identified existed within the ED (throughput).&#x0D; We heard from participants (during multistakeholder dialogue sessions) and content experts that ED overcrowding is a complex health system issue for which the causes, impacts, and solutions extend beyond the ED. Specifically, the novel insights we heard included:&#x0D; &#x0D; ED overcrowding is better viewed as a problem of hospital overcrowding and strained resources in the broader social and health care systems. Contributing factors both within and outside the ED influence and interact with each other and are affected by economic, cultural, and institutional realities.&#x0D; Solving the issue requires addressing accountability and implementing multifaceted solutions in which several systems and voices work collaboratively.&#x0D; Existing technologies and data use and collection are not being used to their full potential; they can be better leveraged to alleviate this issue.&#x0D; &#x0D; &#x0D; In the identified literature, there was a lack of explicit reporting around equity and ethical considerations for factors contributing to, and interventions to alleviate, ED overcrowding. Future work should strive to deliberately and explicitly include ethical considerations inherent in research, planning, and policy-making; considerations of equity-deserving groups; and dedicate the time needed to consider the various facets of this issue.&#x0D; This CADTH report and our series of reports on ED overcrowding are a starting point to bridge the literature, stakeholder discussion, and expert opinion to help decision-makers understand the various parts of the issue and consult the relevant updated evidence to inform their work.&#x0D;

Medical experience as an influencing parameter in emergency medical care for psychiatric emergencies: retrospective analysis of a multicenter survey

BackgroundPrehospital care of psychiatric patients often relies on the medical experience of prehospital emergency physicians (PHEPs). The psychiatrists (PSs) involved in the further treatment of psychiatric patients also often rely on their experience. Furthermore, the interaction between PHEPs and PSs is characterized by interaction problems and different approaches in the prehospital care of the psychiatric emergency.ObjectivesTo analyze the phenomenon of “medical experience” as a cause of possible interaction-related problems and assess its impact on the prehospital decision-making process between prehospital emergency physicians and psychiatrists.MethodsThe retrospective data analysis was conducted between November 2022 and March 2023. Medical experience was defined as follows, based on the demographic information collected in the questionnaires: For PHEPs, the period since obtaining the additional qualification in emergency medicine was defined as a surrogate marker of medical experience: (i) inexperienced: < 1 year, (ii) experienced: 1–5 years, (iii) very experienced: > 5 years. For PSs, age in years was used as a surrogate parameter of medical experience: (i) inexperienced: 25–35 years, (ii) experienced: 35–45 years, (iii) very experienced: > 45 years.ResultsInexperienced PSs most frequently expressed anxiety about the psychiatric emergency referred by a PHEP (27.9%). Experienced PHEPs most frequently reported a lack of qualifications in handling the care of psychiatric emergencies (p = 0.002). Very experienced PHEPs were significantly more likely to have a referral refused by the acute psychiatric hospital if an inexperienced PS was on duty (p = 0.01). Experienced PHEPs apply an intravenous hypnotic significantly more often (almost 15%) than PSs of all experience levels (p = 0.001). In addition, very experienced PHEPs sought prehospital phone contact with acute psychiatry significantly more often (p = 0.01).ConclusionPHEPs should be aware that the PS on duty may be inexperienced and that treating emergency patients may cause him/her anxiety. On the other hand, PHEPs should be receptive to feedback from PS who have identified a qualification deficiency in them. Jointly developed, individualized emergency plans could lead to better prehospital care for psychiatric emergency patients. Further training in the prehospital management of psychiatric disorders is needed to minimize the existing skills gap among PHEPs in the management of psychiatric disorders.

FTP1.7 Critical decision analysis of pre-hospital major haemorrhage protocol activation

Abstract Aims Major haemorrhage protocol activation (MHPA) facilitates immediate blood product availability and institutional capacity readiness for bleeding trauma patients. MHPA is a difficult decision pre-hospital, where two thirds of MHPAs occur. We aimed to identify the determinants and influences of the pre-hospital MHPA decision. Methods This prospective qualitative study had Health Research Authority approval (REC 22/PR/0089). Physicians from one helicopter emergency medical service (HEMS) were purposively sampled. Written informed consent was obtained. Cases representing difficult MHPA decisions within the previous six months were included. Semi-structured interviews were conducted via Microsoft Teams, following the critical decision analysis method. Interviews were transcribed and analysed using a 6-step inductive thematic analysis method on NVivo. Results Twelve interviews were conducted with nine participants: median age 37 years (range 34-45), median post-graduate experience 12 years (IQR 10.5-14.8), and median HEMS experience 1 year (IQR 0.5-4.9). Cues for (and against) the MHPA decision included: hard evidence of bleeding (or bleeding judged to be non-severe); initial information fit their mental model of a bleeding patient (or did not); patient deterioration (or response to resuscitation); acknowledgement that injuries and bleeding can be missed (or uncertainty of clinical findings); and alternative diagnoses were considered less likely (or more likely). Other influences of the MHPA decision were heuristics (especially anchoring bias), situational awareness, previous experience, colleagues on-scene, time- and task-pressure, and the desire for diagnostic accuracy and professional reputation. Conclusions Multiple determinants and influences affect the pre-hospital MHPA decision. Acknowledging these cognitive processes will inform efforts to improve decision-making in trauma.

Comparison of helicopter and ground transportation in pediatric trauma patients.

Decision making regarding transportation mode after a traumatic injury may have a significant impact on outcomes, due to differences in time to definitive care. The objective of this study was to determine if transport mode had an impact on in-hospital mortality and discharge disposition in pediatric trauma patients. Data were abstracted from the National Trauma Data Bank from 2007 to 2016 comparing helicopter and ground transportation modes effects on mortality and discharge outcomes. The primary outcome was in-hospital death, while the secondary outcome was discharge home without services (DCHWOS). Analyses included logistic regression modeling and propensity score matching. Significant variables from univariate analysis were included in the multivariate, propensity-matched regression model. Pediatric trauma patients transported by helicopter had lower odds of mortality (OR 0.69 [0.64,0.75]) and higher odds of DCHWOS (1.29 [1.20,1.39]). There were no differences in overall mechanism, but individual injury patterns showed higher odds of mortality. Critical decisions regarding triage of patients by different modes of transport occur every day. This study supports the current literature on the topic and shows a potential additional benefit of a meaningful discharge outcome for those transported by helicopter. This study may impact prehospital triage decision making process for pediatric trauma patients on mortality. Prehospital transport mode may contribute to pediatric trauma discharge outcomes. Highlights the need for future research regarding non-clinical data that is unable to be abstracted from national databases (e.g., family dynamics, insurance status, weather, access to post-discharge resources).

Impact of Point-of-Care Ultrasound on Prehospital Decision Making by HEMS Physicians in Critically Ill and Injured Patients: A Prospective Cohort Study.

Several studies have shown the additional benefit of point-of-care ultrasound (POCUS) by prehospital Emergency Medical Services (EMS). Since organization of EMS may vary significantly across countries, the value of POCUS likely depends on the prehospital system in which it is used. In order to be able to optimally implement POCUS and develop a tailored training curriculum, it is important to know how often POCUS is currently used, for which indications it is used, and how it affects decision making. The aims of this study were: (1) to determine the percentage of patients in whom POCUS was used by Dutch Helicopter Emergency Medical Services (HEMS) crews; (2) to determine how often POCUS findings led to changes in on-scene management; and (3) what these changes were. Patients who received prehospital care from December 1, 2020 through March 31, 2021 by a single HEMS crew were included in this prospective cohort study. Clinical data and specific data on POCUS examination, findings, and therapeutic consequences were collected and analyzed. During the study period, on-scene HEMS care was provided to 612 patients, of which 211 (34.5%) patients underwent POCUS. Of these, 209 (34.2%) patients with a median age of 45 years were included. There were 131 (62.7%) trauma patients, and 70 (33.7%) of the included patients underwent cardiopulmonary resuscitation (CPR). The median reported time of POCUS examination was three (P25-P75 2-5) minutes. Median prolongation of on-scene time was zero (P25-P75 0-1) minutes. In 85 (40.7%) patients, POCUS examination had therapeutic consequence: POCUS was found to impact treatment decisions in 34 (26.0%) trauma patients and 51 (65.4%) non-trauma patients. In patients with cardiac arrest, POCUS was most often used to aid decision making with regard to terminating or continuing resuscitation (28 patients; 13.4%). During the study period, POCUS examination was used in 34.5% of all prehospital HEMS patients and had a therapeutic consequence in 40.7% of patients. In trauma patients, POCUS seems to be most effective for patient triage and evaluation of treatment effectiveness. Moreover, POCUS can be of significant value in patients undergoing CPR. A tailored HEMS POCUS training curriculum should include ultrasound techniques for trauma and cardiac arrest.

Prehospital seizures: Short-term outcomes and risk stratification based in point-of-care testing.

Information for treatment or hospital derivation of prehospital seizures is limited, impairing patient condition and hindering patients risk assessment by the emergency medical services (EMS). This study aimed to determine the associated factors to clinical impairment, and secondarily, to determine risk factors associated to cumulative in-hospital mortality at 2, 7 and 30 days, in patients presenting prehospital seizures. Prospective, multicentre, EMS-delivery study involving adult subjects with prehospital seizures, including five advanced life support units, 27 basic life support units and four emergency departments in Spain. All bedside variables: including demographic, standard vital signs, prehospital laboratory tests and presence of intoxication or traumatic brain injury (TBI), were analysed to construct a risk model using binary logistic regression and internal validation methods. A total of 517 patients were considered. Clinical impairment was present in 14.9%, and cumulative in-hospital mortality at 2, 7 and 30-days was 3.4%, 4.6% and 7.7%, respectively. The model for the clinical impairment indicated that respiratory rate, partial pressure of carbon dioxide, blood urea nitrogen, associated TBI or stroke were risk factors; higher Glasgow Coma Scale (GCS) scores mean a lower risk of impairment. Age, potassium, glucose, prehospital use of mechanical ventilation and concomitant stroke were risk factors associated to mortality; and oxygen saturation, a high score in GCS and haemoglobin were protective factors. Our study shows that prehospital variables could reflect the clinical impairment and mortality of patients suffering from seizures. The incorporation of such variables in the prehospital decision-making process could improve patient outcomes.

Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

IntroductionStroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there...

Using natural language processing in facilitating pre-hospital telephone triage of emergency calls.

Natural language processing (NLP) is an area of computer science that involves the use of computers to understand human language and semantics (meaning) and to offer consistent and reliable responses. There is good evidence of significant advancement in the use of NLP technology in dealing with acutely ill patients in hospital (such as differential diagnosis assistance, clinical decision-making and treatment options). Further technical development and research into the use of NLP could enable further improvements in the quality of pre-hospital emergency care. The aim of this literature review was to explore the opportunities and potential obstacles in implementing NLP during this phase of emergency care and to question if NLP could contribute towards improving the process of nature of call screening (NoCS) to enable earlier recognition of life-threatening situations during telephone triage of emergency calls. A systematic search strategy using two electronic databases (CINAHL and MEDLINE) was conducted in December 2021. The PRISMA systematic approach was used to conduct a review of the literature, and selected studies were identified and used to support a critical review of the actual and potential use of NLP for the call-taking phase of emergency care. An initial search offered 204 records: 23 remained after eliminating duplicates and a consideration of title and abstracts. A further 16 full-text articles were deemed ineligible (not related to the subject under investigation), leaving seven included studies. Following a thematic review of these studies two themes emerged, that are considered individually and together: (i) use of NLP for dealing with out-of-hospital cardiac arrest and (ii) responding to increased accuracy of NLP. NLP has the potential to reduce or eliminate human bias during the emergency triage assessment process and contribute towards improving triage accuracy in pre-hospital decision-making and an early identification and categorisation of life-threatening conditions. Evidence to date is mostly linked to cardiac arrest identification; this review proposes that during the call-taking phase NLP should be extended to include further medical emergencies (including fracture/trauma, stroke and ketoacidosis). Further research is indicated to test the reliability of these findings and a proportionate introduction of NLP simultaneous with increased quality and reliability.

The FAST VAN for Field Identification of Large Vessel Occlusion in Acute Stroke.

There is definitive evidence for effectiveness of thrombectomy for acute stroke with large vessel occlusion (LVO). A clinical tool to identify patients with LVO is therefore required for effective triage and prehospital decision making. We developed the FAST VAN tool, which follows from the Heart and Stroke Foundation FAST stroke screen, with the addition of cortical features of vision, aphasia, and neglect, to differentiate from lacunar syndromes. Consecutive acute stroke alerts initiated by emergency medical services (EMS) were prospectively analyzed from April 2017 to Jan 2021. FAST VAN signs were recorded by first responders who had received online education about the tool. These findings were compared to the presence or absence of LVO on CT angiography. Analysis was also performed by appropriateness for comprehensive stroke centers (CSC) transfer if no LVO was present. EMS providers were surveyed regarding ease of use in terms of learning the tool and using in real-world practice. Data from 1080 consecutive acute strokes included 440 patients considered to have VAN signs by EMS. Fifty-four percent of VAN-positive patients showed LVO on CTA. Sensitivity, specificity, and accuracy were 86%, 75%, and 77%, respectively. In 204 false-positive cases, 143 (70%) were considered appropriate for evaluation at the CSC. EMS providers reported high satisfaction with learning and using the tool. The FAST VAN tool for identification of LVO meets desired characteristics of an effective screening tool in ease of use, efficiency, and accuracy. Aphasia remains the most challenging cortical feature to identify accurately.

Job Burnout Is Associated With Prehospital Decision Delay: An Internet-Based Survey in China.

BackgroundPrehospital delay is associated with non-modifiable factors such as age, residential region, and disease severity. However, the impact of psychosocial factors especially for job burnout on prehospital decision delay is still little understood.MethodThis internet-based survey was conducted between 14 February 2021 and 5 March 2021 in China through the Wechat platform and web page. Self-designed questionnaires about the expected and actual length of prehospital decision time and the Chinese version of Maslach Burnout Inventory-General Survey, Type D Personality Scale-14, and Social Support Rating Scale were applied. A total of 1,039 general participants with a history of perceptible but tolerable body discomfort were included.ResultsThe top six reasons for prehospital decision delay were: (1) endure until self-healing (50.7%), (2) too busy to ask for leave (40.3%), (3) process for seeing a doctor too complicated (35.8%), (4) too tired after work (26.2%), (5) worry about the expenditure (16.6%), and (6) fear of being identified as with serious problem (14.5%). The univariate analyses revealed that older age (p = 0.001), type D personality (p = 0.025), job burnout (p = 0.055), and worrying about expenditure (p = 0.004) were associated with prolonged prehospital decision time, while engaged in medical-related job (p = 0.028) and with more social support (p = 0.066) would shorten the delay. The multivariate analysis using logistic regression model with forward selecting method showed that age [per 10 years, odds ratio (OR) 1.19 (1.09–1.31), p < 0.001], job burnout [per 10 points in Maslach Burnout Inventory-General Survey (MBI-GS), OR 1.17 (1.04–1.31), p = 0.007], and worrying about expenditure [OR 1.75 (1.25–2.47), p = 0.001] were the three determinants for prehospital decision delay (>7 days). Mediating effects were analyzed by using bias-corrected percentile bootstrap methods (N = 10,000). Social support was found partially mediated the relationship between the determinants and prehospital decision time. The partial mediating effect of social support accounted for 24.0% of the total effect for job burnout and 11.6% for worrying about expenditure.ConclusionPsychosocial factors have a non-negligible impact on prehospital decision delay. The crucial part of prehospital decision delay may be the lack of motivation inside. Job burnout and lack of social support, as two commonly seen features in the modern world, should be given enough consideration in disease prevention and treatment.

Performance of Prehospital Use of Chest Pain Risk Stratification Tools: The RESCUE Study

Background Emergency medical services (EMS) assesses millions of patients with chest pain each year. However, tools validated to risk stratify patients for acute coronary syndrome (ACS) and pulmonary embolism (PE) have not been translated to the prehospital setting. The objective of this study is to assess the prehospital performance of risk stratification scores for 30-day major adverse cardiac events (MACE) and PE. Methods A prospective observational cohort study of patients ≥21 years of age with acute chest pain who were transported by EMS in two North Carolina (NC) counties was conducted from 18 April 2018–2 January 2019. In this convenience sample, paramedics completed HEAR (history, electrocardiogram, age, risk factor), ED Assessment of Chest Pain Score (EDACS), Revised Geneva Score (RGS), and pulmonary embolism rule-out criteria (PERC) assessments on each patient. MACE (all-cause death, myocardial infarction, and revascularization) and PE at 30 days were determined by hospital records and NC Death Index. The positive (+LR) and negative likelihood ratios (−LR) of the risk scores for 30-day MACE and PE were calculated. Results During the study period, 82.1% (687/837) patients had all four risk score assessments. The cohort was 51.1% (351/687) female, 49.5% (340/687) African American, and had a mean age of 55.0 years (SD 16.0). At 30 days, MACE occurred in 7.4% (51/687), PE occurred in 0.9% (6/687), and the combined outcome occurred in 8.2% (56/687). The HEAR score had a − LR of 0.46 (95% CI 0.27–0.78) and + LR of 1.48 (95% CI 1.26–1.74) for 30-day MACE. EDACS had a − LR of 0.61 (95% CI 0.46–0.81) and + LR of 2.53 (95% CI 1.86–3.46) for 30-day MACE. The PERC score had a − LR of 0 (95% CI 0.0–1.4) and a + LR of 1.38 (95% CI 1.32–1.45) for 30-day PE. The RGS score had a − LR of 0 (95% CI 0.0–0.65) and a + LR of 2.36 (95% CI 2.16–2.57) for 30-day PE. The combination of a low-risk HEAR score and negative PERC evaluation had a − LR of 0.25 (95% CI 0.08–0.76) and a + LR of 1.21 (95% CI 1.21–1.30) for 30-day MACE or PE. Conclusion The combination of a paramedic-obtained HEAR score and PERC evaluation performed best to exclude 30-day MACE and PE but was not sufficient for directing prehospital decision making.

A model to explain the challenges of emergency medical technicians' decision making process in emergency situations: a grounded theory.

:Background:To manage life-threatening conditions and reduce morbidity and mortality, pre-hospital’s on-scene decision making is an influential factor. Since pre-hospital’s decision making is a challenging process, it is necessary to be identified this process. This study was conducted to explore the model of Iranian emergency medical technicians’ decision making in emergency situations.Methods:This study was applied through grounded theory method using direct field observations and semi-structured interviews. Purposeful sampling with 26 participants including 17 emergency medical technicians including dispatchers, physicians of medical directions, managers and 1 representative for court affairs was performed. Interviews were lasted from October 2018 to July 2019. Corbin and Strauss approach, 2015 (open, axial and selective coding) were used to analyze data.Results:A paradigm model was developed to explain the relationships among the main categories. Decision making in the context of fear and concern was emerged as the core category. Unclear duties, insufficient authorities and competencies as well as lack of enough decision making’s protocols and guidelines were categorized as casual conditions. Other important categories linked to the core category were interactions, feelings and “customer focus approach”. Action–interaction strategies were taken by Emergency Medical technicians lead to some negative consequences that can threaten clinical outcome and patient safety.Conclusions:Based on the finding of this study, Emergency Medical technicians’ decision making in the context of fear and concern, as the core concept of this model, lead to decrease in quality of the pre-hospital services, stakeholders’ dissatisfaction, hospital emergency units’ overload, decrease in reputation of the Emergency Medical Technicians, threat to patient clinical outcome and patient safety. To prevent of these negative consequences, facilitation of the Emergency Medical Technicians’ on-scene decision making is recommended.

Pre-hospital suPAR, lactate and CRP measurements for decision-making: a prospective, observational study of patients presenting non-specific complaints

BackgroundIn the pre-hospital setting, non-urgent patients with non-specific chief complaints pose assessment challenges for the emergency medical systems (EMS). Severely ill patients should be identified among these patients, and unnecessary transport to the emergency department (ED) should be avoided. Unnecessary admissions burden EDs, deplete EMS resources and can even be harmful to patients, especially elderly patients. Therefore, tools for facilitating pre-hospital decision-making are needed. They could be based on vital signs or point-of-care laboratory biomarkers. In this study, we examined whether the biomarker soluble urokinase plasminogen activator receptor (suPAR), either alone or combined with C-reactive protein (CRP) and/or lactate, could predict discharge from the ED and act as a pre-hospital support tool for non-conveyance decision-making.MethodsThis was a prospective, observational study of adult patients with normal or near-normal vital signs transported by an EMS to an ED with a code referring to deteriorated general condition. The levels of suPAR, CRP and lactate in the patients’ pre-hospital blood samples were analysed. The values of hospitalized patients were compared to those of discharged patients to determine whether these biomarkers could predict direct discharge from the ED.ResultsA total of 109 patients (median age: 81 years) were included in the study. Of those, 52% were hospitalized and 48% were discharged from the ED. No statistically significant association was found between suPAR and the ED discharge vs hospitalization outcome (OR: 1.04, 95% CI 0.97–1.13, AUROC: 0.58, 95% CI 0.47–0.69). Adding CRP (AUROC: 0.64, 95% CI 0.54–0.75) or lactate (AUROC: 0.60, 95% CI 0.49–0.71) to the regression models did not improve their diagnostic accuracy. None of the patients with a suPAR value of less than 2 ng/ml were admitted to hospital, while 64% of the patients with a suPAR value of more than 6 ng/ml were hospitalized.ConclusionPre-hospital suPAR measurements alone or combined with CRP and/or lactate measurements could not predict the ED discharge or hospital admission of 109 non-urgent EMS patients with non-specific chief complaints and normal or near-normal vital signs.

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.