Emergency Medical Dispatch Research Articles

Monkeypox 2022: A Primer and Identify-Isolate-Inform (3I) Tool for Emergency Medical Services Professionals.

Monkeypox 2022 exhibits unprecedented human-to-human transmission and presents with different clinical features than those observed in prior outbreaks. Previously endemic only to West and Central Africa, the monkeypox virus spread rapidly world-wide following confirmation of a case in the United Kingdom on May 7, 2022 of an individual that had traveled to Nigeria. Detection of cases with no travel history confirms on-going community spread. Emergency Medical Services (EMS) professionals will likely encounter patients suspected or confirmed to have monkeypox, previously a rare disease and therefore unfamiliar to most clinicians. Consequently, it is critical for EMS medical directors to immediately implement policies and procedures for EMS teams - including emergency medical dispatchers - to identify potential monkeypox cases. These must include direction on actions EMS professionals should take to protect themselves and others from virus transmission. Monkeypox 2022 may manifest more subtly than it has historically. Presentations include a subclinical prodrome and less dramatic skin lesions - potentially limited to genital or anal body regions - which can be easily confused with dermatologic manifestations of common sexually transmitted infections (STIs). While most readily spread by close contact with infectious skin lesions on a patient, it is also transmissible from fomites, such as bed sheets. Additionally, droplet transmission can occur, and the virus can be spread by aerosolization under certain conditions. The long incubation period could have profound negative consequences on EMS staffing if clinicians are exposed to monkeypox. This report summarizes crucial information needed for EMS professionals to understand and manage the monkeypox 2022 outbreak. It presents an innovative Identify-Isolate-Inform (3I) Tool for use by EMS policymakers, educators, and clinicians on the frontlines who may encounter monkeypox patients. Patients are identified as potentially exposed or infected after an initial assessment of risk factors with associated signs and symptoms. Prehospital workers must immediately don personal protective equipment (PPE) and isolate infectious patients. Also, EMS professionals must report exposures to their agency infection control officer and alert health authorities for non-transported patients. Prehospital professionals play a crucial role in emerging and re-emerging infectious disease mitigation. The monkeypox 2022 3I Tool includes knowledge essential for all clinicians, plus specific information to guide critical actions in the prehospital environment.

9-1-1 Caller-Described Heart Attack Symptoms

Heart attacks (HAs) present clinically with varying symptoms, which are not always described by patients as chest pain (CP) or chest discomfort (CD). Emergency Medical Dispatchers (EMDs) select the CP/CD dispatch protocol for non-chest pain HA symptoms or classic HA complaint of CP/CD. Nevertheless, it is still unknown how often callers report HA symptoms other than CP/CD. The objective of this study was to characterize the caller's descriptions of the primary HA symptoms, descriptions of the other HA symptoms, and the use of a case entry (CE) question clarifier. A retrospective descriptive study analyzed randomly selected EMD audios (where CD/CD protocol was used) from five accredited emergency communication centers in the United States. Several Quality Performance Review (QPR) experts reviewed the audios and recorded callers' initial problem descriptions, the use of and responses to the CE question clarifier, including the EMD-assigned final determinant code. A total of 1,261 audios were reviewed. The clarifier was used only 8.5% of the time. The CP/CD symptoms were mentioned alone or with other problems 87.0% of the time. Overall, CP symptom was mentioned alone 70.8%, HA alone 4.0%, and CD symptom alone 1.4% of the time. 9-1-1 callers report potential HA cases using a variety of terms and descriptions-most commonly CP. Other less-common symptoms associated with a HA may be mentioned. Therefore, EMDs must be well-trained to be prepared to probe the caller with a clarifying query to elicit more specific information when "having a heart attack" is the only complaint initially mentioned.

Factors affecting false calls to prehospital Emergency medical services and analyzing The recorded false calls in the dispatch center

INTRODUCTION: False calls to the prehospital emergency medical service (EMS) can divert EMS concentration from people with life-threatening conditions to a false situation and also affect the lives of the at-risk people. MATERIAL AND METHODS: This cross-sectional study, was conducted in 2019. The population consisted of 28 Dispatchers personnel and 130 emergency medical technicians located in 24 urban bases, and the recorded messages of 450 false calls to the prehospital EMS were also analyzed. Both personnel perspectives on the false calls questionnaire and false call content evaluation checklist were used to collect data. RESULTS: The most influential factors in the incidence of false calls were the dimensions of public education (4.38 ± 0.5) and instructions and guidelines (4.28 ± 0.51), respectively. “Insufficient knowledge of people about the sensitivity of EMS work” from the public education dimension (4.60 ± 0.54) and “Lack of codified rules for dealing with false calls to the EMS” from the instructions and guidelines dimension (4.48 ± 0.71) were the most important factors. type of requests from EMS were mostly unnecessary conversations (52.4%) and dispatching an ambulance (21.3%). CONCLUSIONS: Given the importance of emergency, it is required to promote the general public knowledge and culture, as well as stricter rules and training programs for all ages, should be considered to reduce false calls. On the other hand, prehospital emergency medical dispatcher personnel should be trained on how to deal with false calls.

Machine Learning–Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

BackgroundEarly recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies.ObjectiveIn this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident.MethodsAudio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.ResultsAlthough the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments.ConclusionsA machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

Association between patient age and pediatric cardiac arrest recognition by emergency medical dispatchers

BackgroundDispatcher-assisted cardiopulmonary resuscitation (DA-CPR) is an important prognostic factor in pediatric out-of-hospital cardiac arrest (OHCA). The recognition of cardiac arrest by dispatcher is a key factor for successful DA-CPR. In this study, we evaluated the association between pediatric age and dispatcher recognition. MethodsA retrospective observational study was designed using a nationwide OHCA registry. Patients under 19 years of age were enrolled. Patients were categorized into four groups according to age (<1 year, 1–6 years, 7–13 years, and 14–18 years). The primary outcome was cardiac arrest recognition by dispatcher. A multivariable logistic regression analysis was performed. ResultsA total of 2754 pediatric OHCA patients were enrolled. A negative trend was observed between age and dispatcher performance (p < 0.01). The rate of cardiac arrest recognition was highest in patients under one year of age (61.5%) and lowest in patients ages 14–18 years old (47.1%). Patients in the 7–13 years and 14–18 years age groups were both associated with a decreased rate of recognition (adjusted odds ratio with 95% confidence interval: 0.55 (0.41–0.74) and 0.44 (0.34–0.57), respectively). In the interaction analysis, the association between age and outcomes was more prominent in patients with non-medical causes. ConclusionPatients ages 7–18 years old were negatively associated with cardiac arrest recognition and DA-CPR instruction provision within optimal timeframes compared to those younger than one year old. Development of a tailored protocol could be considered according to age and cause of arrest for better dispatcher performance in pediatric OHCA patients.

P106 Emergency medical dispatcher perceive alerting community first responders to out-of-hospital cardiac arrests as no or minor additional burden

P106 Emergency medical dispatcher perceive alerting community first responders to out-of-hospital cardiac arrests as no or minor additional burden

Resuscitation guideline highlights.

The purpose of this review was to give an overview of the most significant updates in resuscitation guidelines and provide some insights into the new topics being considered in upcoming reviews. Recent updates to resuscitation guidelines have highlighted the importance of the earlier links in the chain-of-survival aimed to improve early recognition, early cardiopulmonary resuscitation (CPR) and defibrillation. Empowering lay rescuers with the support of emergency medical dispatchers or telecommunicators and engaging the community through dispatching volunteers and Automated External Defibrillators, are considered key in improving cardiac arrest outcomes. Novel CPR strategies such as passive insufflation and head-up CPR are being explored, but lack high-certainty evidence. Increased focus on survivorship also highlights the need for more evidence based guidance on how to facilitate the necessary follow-up and rehabilitation after cardiac arrest. Many of the systematic and scoping reviews performed within cardiac arrest resuscitation domains identifies significant knowledge gaps on key elements of our resuscitation practices. There is an urgent need to address these gaps to further improve survival from cardiac arrest in all settings. A continuous evidence evaluation process for resuscitation after cardiac arrest is triggered by new evidence or request by the resuscitation community, and provides more current and relevant guidance for clinicians.

Discovering Key Topics in Emergency Medical Dispatch from Free Text Dispatcher Observations.

The objective of this work was to discover key topics latent in free text dispatcher observations registered during emergency medical calls. We used a total of 1374931 independent retrospective cases from the Valencian emergency medical dispatch service in Spain, from 2014 to 2019. Text fields were preprocessed to reduce vocabulary size and filter noise, removing accent and punctuation marks, along with uninformative and infrequent words. Key topics were inferred from the multinomial probabilities over words conditioned on each topic from a Latent Dirichlet Allocation model, trained following an online mini-batch variational approach. The optimal number of topics was set analyzing the values of a topic coherence measure, based on the normalized pointwise mutual information, across multiple validation K-folds. Our results support the presence of 15 key topics latent in free text dispatcher observations, related with: ambulance request; chest pain and heart attack; respiratory distress; head falls and blows; fever, chills, vomiting and diarrhea; heart failure; syncope; limb injuries; public service body request; thoracic and abdominal pain; stroke and blood pressure abnormalities; pill intake; diabetes; bleeding; consciousness. The discovery of these topics implies the automatic characterization of a huge volume of complex unstructured data containing relevant information linked to emergency medical call incidents. Hence, results from this work could lead to the update of structured emergency triage algorithms to directly include this latent information in the triage process, resulting in a positive impact in patient wellbeing and health services sustainability.

Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

Background and purposeStroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment.MethodsStroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI.ResultsThe Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted.ConclusionsAn ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends.Trial registrationThis study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).

The potential of new prediction models for emergency medical dispatch prioritisation of patients with chest pain: a cohort study

ObjectivesTo develop emergency medical dispatch (EMD) centre prediction models with high sensitivity and satisfying specificity to identify high-priority patients and patients suitable for non-emergency care respectively, when assessing patients with chest pain.MethodsObservational cohort study of 2917 unselected patients with chest pain who contacted an EMD centre in Sweden due to chest pain during 2018. Multivariate logistic regression was applied to develop models predicting low-risk or high-risk condition, that is, occurrence of time-sensitive diagnosis on hospital discharge.ResultsPrediction models were developed for the identification of patients suitable for high- and low-priority dispatch, using 11 and 10 variables respectively. The area under the receiver-operating characteristic curve (AUROC) for the high-risk prediction model was 0.79 and for the low-risk model it was 0.74. When applying the high-risk prediction model, 56% of the EMS missions were given highest priority, compared with 65% with the current standard. When applying the low-risk model, 7% were given the lowest priority compared to 1% for the current standard. The new prediction models outperformed today’s dispatch priority accuracy in terms of sensitivity as well as positive and negative predictive value in both high- and low-risk prediction. The low-risk model predicted almost six times as many patients as having low-risk conditions compared with today’s standard. This was done without increasing the number of high-risk patients wrongly assessed as low-risk.ConclusionsBy introducing prediction models, based on logistic regression analyses, using variables obtained by standard EMD-questions on age, sex, medical history and symptomology, EMD prioritisation can be improved compared with using current criteria index-based ones. This will allow a more efficient emergency medical services resource allocation.

A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates.

BackgroundCalls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response. In this context, over triage is defined as dispatching an ambulance with lights and sirens (L&S) to a low acuity MVC and under triage is not dispatching an ambulance with L&S to those who require urgent medical care. We explored the potential for crash characteristics to be used during emergency ambulance calls to identify those MVCs that required a L&S response.MethodsWe conducted a retrospective cohort study using ambulance and police data from 2014 to 2016. The predictor variables were crash characteristics (e.g. road surface), and Medical Priority Dispatch System (MPDS) dispatch codes. The outcome variable was the need for a L&S ambulance response. A Chi-square Automatic Interaction Detector technique was used to develop decision trees, with over/under triage rates determined for each tree. The model with an under/over triage rate closest to that prescribed by the American College of Surgeons Committee on Trauma (ACS COT) will be deemed to be the best model (under triage rate of ≤ 5% and over triage rate of between 25–35%.ResultsThe decision tree with a 2.7% under triage rate was closest to that specified by the ACS COT, had as predictors—MPDS codes, trapped, vulnerable road user, anyone aged 75 + , day of the week, single versus multiple vehicles, airbag deployment, atmosphere, surface, lighting and accident type. This model had an over triage rate of 84.8%.ConclusionsWe were able to derive a model with a reasonable under triage rate, however this model also had a high over triage rate. Individual EMS may apply the findings here to their own jurisdictions when dispatching to the scene of a MVC.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12873-022-00609-5.

The association of indoor heat exposure with diabetes and respiratory 9-1-1 calls through emergency medical dispatch and services documentation

BackgroundPeople with pre-existing medical conditions, who spend a large proportion of their time indoors, are at risk of emergent morbidities from elevated indoor heat exposures. In this study, indoor heat of structures wherein exposed people received Grady Emergency Services based care in Atlanta, GA, U.S., was measured from May to September 2016. Methodology: In this case-control study, analyses were conducted to investigate the effect of indoor heat on the odds of 9-1-1 calls for diabetic (n = 90 cases) and separately, for respiratory (n = 126 cases), conditions versus heat-insensitive emergencies (n = 698 controls). Generalized Additive Models considered both linear and non-linear indoor heat and health outcome associations using thin-plate regression splines. ResultsHotter and more humid indoor conditions were non-linearly associated with an increasing likelihood of receiving emergency care for complications of diabetes and severe respiratory distress. Higher heat indices were associated with increased odds of a diabetes (odds ratio for change from 30 to 31 °C: 1.12, 95% CI: 1.08–1.16) or respiratory 9-1-1 medical call versus control (odds ratio for change from 34 to 35 °C: 1.18, 95% CI: 1.09–1.28) call. Both diabetic and respiratory distress patients were more likely to be African-American and/or have comorbidities. ConclusionsIn this study, the statistical association of indoor heat exposure with emergency morbidities (diabetic, respiratory) was demonstrated. The study also showcased the value and utility of data gathered by emergency medical dispatch and services from inaccessible private indoor sources (i.e., domiciles) for environmental health.

Emergency medical dispatchers’ experiences of managing emergency calls: a qualitative interview study

ObjectivesTo explore the emergency medical dispatchers (EMDs) experiences of managing emergency medical calls.DesignA qualitative interview study with an inductive approach. EMDs were interviewed individually using a semistructured interview guide. The...

Unrecognized cardiac arrests: A one-year review of audio from emergency medical dispatch calls.

Immediate recognition of out-of-hospital cardiac arrest (OHCA) by Emergency Medical Dispatch (EMD) operators is crucial to facilitate timely initiation of telephone cardiopulmonary resuscitation (T-CPR) and to enable the appropriate level of Emergency Medical Services (EMS) response. The goal of this study was to identify patterns that can increase EMD-level recognition of cardiac arrests prior to EMS arrival. The Combined Communications Center in Alachua County, Florida provided audio recordings of all emergency calls from January 1, 2018 to November 16, 2018 dispatched as a chief complaint other than OHCA, but later identified as cardiac arrest. A multi-disciplinary medical team transcribed and analyzed the calls to determine common themes and trends. Out of an initial 81 calls meeting inclusion criteria, 69 were immediately recognized as OHCA by EMDs, leaving 12 calls of unrecognized OHCA. In 11 of 12 calls respiratory issues were described to EMD. In 10 of 12 calls the subject was described as unconscious, and in the other 2 calls, the subject lost consciousness during the call. Lack of recognition of OHCA by EMD occurred in most calls due to difficulty communicating the subject's respiratory status. Further emphasis should be placed on identifying non-viable respirations in unconscious patients in EMD training and algorithms to increase recognition of OHCA and initiation of T-CPR. A multi-year review of a comparable dataset from geographically and socioeconomically diverse regions in the United States can validate and expand these preliminary trends.

Temporal Trend of Transient Ischemic Attack Management over a 10-Year Period: Data from the Rhône County, France

Background: Timely recognition and management of transient ischemic attack (TIA) offer the greatest opportunity to prevent subsequent stroke. But variability of TIA management quality exists across hospitals. Under the impetus of national plans, measures were adopted to improve TIA management, including a structured local pathway. Our objective was to compare TIA management between two periods over 10 years, before and after the implementation of these measures. Methods: A before-and-after study was conducted with two identical population-based cohort studies in 2006–2007 (AVC69) and 2015–2016 (STROKE69) including all patients with TIA diagnosis over a 7-month period in six public and private hospitals in the Rhône county in France. The primary outcome was the adequate TIA management defined as brain and vessel imaging within 24 h of admission and the prescription of antithrombotic treatment at discharge. Results: We identified 109 patients TIA patients in 2006–2007, and 458 over the same period in 2015–2016. A higher proportion of patients were adequately managed in 2015–2016 compared to 2006–2007 (14/96 [15%] in 2006–2007 vs. 306/452 [68%] in 2015–2016, p < 0.001). This difference was mainly driven by a marked increase of vessel imaging performed within 24 h of admission, most often by computed tomography angiography. Furthermore, patients called more often emergency medical dispatch before admission, were admitted with a shorter delay after symptom onset, and were more likely discharged to home in 2015–2016 compared to 2006–2007. Conclusion: Our study demonstrated an increasing rate of adequate TIA management, mainly driven by a marked increase of vessel imaging within 24 h of admission, over a 10-year period in the Rhône county in France.

The \u201cunclear problem\u201d category: an analysis of its patient and dispatch characteristics and its trend over time

BackgroundAn effective emergency medical dispatch process is vital to provide appropriate prehospital care to patients. It increases patient safety and ensures the sustainable use of medical resources. Although Copenhagen has a sophisticated emergency medical services (EMS) system with a significant focus on public welfare, more than 10% of emergency cases are still being categorized as an "unclear problem category" (UPC) and are thus not categorized as "symptom-specific". Therefore, the objective of this research is to gain a better understanding of the patient and dispatch characteristics of emergency cases categorized as "unclear".MethodsThis register-based study based on medical emergency cases data describes patient and dispatch characteristics of emergency cases categorized as “unclear” through the use of numbers and proportions. Moreover, these cases were compared to non UPC cases. Use of UPC was stratified by month to determine the impact of alerting medical dispatchers to reduce its use.ResultsFrom 296,398 included cases UPC accounted for 11.4% of the cases. The median age of those triaged with the UPC was 66 years vs 58 years for individuals triaged with other symptom-specific categories.Moreover, after having been triaged with the UPC, 9,661 (34.7%) of the dispatched EMS vehicles ended up being cancelled. Sensitizing medical dispatchers about the use of the UPC likely contributed to the decreased use of the UPC over time.ConclusionThe UPC has different dispatch characteristics than the symptom-specific categories, with potential negative effects on the medical dispatch process. Moreover, the median age of individuals triaged with the UPC is higher than those triaged with symptom-specific categories. Nonetheless, the use of the UPC decreased throughout the study period after the medical dispatchers were alerted about the implications of its use.

Effects of an ergonomic intervention program based on the PRECEDE-PROCEED model for reducing work-related health problems and exposure risks among emergency medical dispatchers.

To evaluate the effectiveness of an ergonomic intervention program based on the PRECEDE-PROCEED model in terms of improving exposure risks and work-related health problems in emergency medical dispatchers. This quasi-experimental study used an interrupted time series design. Participants were 55 employees working in an Emergency Medical Communications Center in Iran. The intervention program was based on the PRECEDE-PROCEED model and included five face-to-face training sessions and installing auxiliary equipment according to best ergonomic principles. Direct observations of the emergency medical dispatchers' working postures using the Rapid Office Strain Assessment and a survey which included a modified Nordic Questionnaire, Work Ability Score, Visual Fatigue Questionnaire, and a Behavioral Factors Questionnaire were used at three time points: baseline, 1 month post-intervention, and 3 months post-intervention. The modified Nordic Questionnaire showed significant reductions in pain intensity scores for neck, lower back, knee and ankle after the ergonomic intervention program. In addition, there were considerable post-training improvements in behavioral factors (knowledge and enabling factors) and working postures. No significant changes were observed in Work Ability Scores, or visual symptoms. An ergonomic intervention program based on a systematic framework such as the PRECEDE-PROCEED model and on-site interventions can be effective in improving and enhancing the working conditions of emergency medical dispatchers. Therefore, it is suggested that ergonomic interventions be implemented based on standard and valid behavioral change models such as PRECEDE-PROCEED model in other work environments in which musculoskeletal pain and digital eye strain are common.

A mixed methods analysis of caller-emergency medical dispatcher communication during 9–1–1 calls for out-of-hospital cardiac arrest

ObjectiveRecognition of out-of-hospital cardiac arrest (OHCA) during 9–1–1 calls is critically important, but little is known about how laypersons and emergency medical dispatchers (EMDs) communicate. We sought to describe 9–1–1 calls for OHCA. MethodsWe performed a mixed-methods, retrospective analysis of 9–1–1 calls for OHCA victims in a large urban emergency medical services (EMS) system using a random sampling of cases containing the term “cardiopulmonary resuscitation” (CPR) in the EMS electronic report. A constant comparison qualitative approach with four independent reviewers continued until thematic saturation was achieved. Quantitative analysis employed computational linguistics. Callers’ emotional states were rated using the emotional content and cooperation score (ECCS). ResultsThematic saturation was achieved after 46 calls. Three “OHCA recognition” themes emerged [ 1) disparate OHCA terms used, 2) OHCA mimics create challenges, 3) EMD questions influence recognition]. Three “CPR facilitation” themes emerged [ 1) directive language may facilitate CPR, 2) specific instructions assist CPR, 3) caller’s emotions affect CPR initiation]. Callers were generally “anxious but cooperative.” Callers saying “pulse” was associated with OHCA recognition. ConclusionCommunication characteristics appear to influence OHCA recognition and CPR facilitation. Practice implicationsDispatch protocols that acknowledge characteristics of callers’ communication may improve OHCA recognition and CPR facilitation.

The trajectory of repairs in the defibrillator sequence during emergency cardiac arrest calls – Balancing progressivity and intersubjectivity

Due to the urgent, time-sensitive nature of interactions in emergency ambulance phone calls, dealing with repairs (communication trouble) can be challenging. We investigate a critical medical emergency known as out-of-hospital cardiac arrest (OHCA) and focus on how ambulance call-takers handle repairs during an interactive sequence concerning the retrieval of automatic external defibrillators (AED). Clear communication about AEDs is vital, because the device can deliver a life-saving shock to an OHCA patient’s heart. We examined repair initiations, and their subsequent trajectories, during the defibrillator sequences in 58 OHCA emergency calls. We found evidence of competing influences in resolving such repairs: (1) providing a repair solution (including ensuring caller comprehension of what a defibrillator is) to achieve intersubjectivity that could resolve the question of defibrillator availability; or (2) progressing the call as swiftly as possible to an immediately applicable life-saving intervention such as cardiopulmonary resuscitation. The findings suggest that in certain institutional contexts, such as emergency medical service dispatch, the resolution of repairs in communication can take varying trajectories in order to achieve the most feasible goal in immediate time. We suggest that emergency medical services consider these trajectories in helping ambulance call-takers anticipate repairs in OHCA calls.

Maine approves plan to extend MH support to emergency responders

A Maine legislative committee has signed off on a proposal to try to help emergency responders better access mental health support, The Associated Press reported Feb. 7. The proposal establishes a presumption that firefighters, emergency medical technicians, police officers, emergency dispatchers and others who are diagnosed as having post‐traumatic stress disorder have the condition because of “extraordinary and unusual work stress,” a bill before the Maine Legislature states. The Legislature's Labor and Housing Committee unanimously approved the proposal on Feb. 7. Professional Firefighters of Maine backed the change. The organization said the bill would shift the burden of proof to the employer to prove the condition is not related to work. The full Maine Legislature must still vote on the proposal. The rule is currently on the books but is due to end in October. The bill before the Legislature would make it permanent.