Dataset Information

The linguistic and interactional factors impacting recognition and dispatch in emergency calls for out-of-hospital cardiac arrest: a mixed-method linguistic analysis study protocol.

ABSTRACT:

Introduction

Emergency telephone calls placed by bystanders are crucial to the recognition of out-of-hospital cardiac arrest (OHCA), fast ambulance dispatch and initiation of early basic life support. Clear and efficient communication between caller and call-taker is essential to this time-critical emergency, yet few studies have investigated the impact that linguistic factors may have on the nature of the interaction and the resulting trajectory of the call. This research aims to provide a better understanding of communication factors impacting on the accuracy and timeliness of ambulance dispatch.

Methods and analysis

A dataset of OHCA calls and their corresponding metadata will be analysed from an interdisciplinary perspective, combining linguistic analysis and health services research. The calls will be transcribed and coded for linguistic and interactional variables and then used to answer a series of research questions about the recognition of OHCA and the delivery of basic life-support instructions to bystanders. Linguistic analysis of calls will provide a deeper understanding of the interactional dynamics between caller and call-taker which may affect recognition and dispatch for OHCA. Findings from this research will translate into recommendations for modifications of the protocols for ambulance dispatch and provide directions for further research.

Ethics and dissemination

The study has been approved by the Curtin University Human Research Ethics Committee (HR128/2013) and the St John Ambulance Western Australia Research Advisory Group. Findings will be published in peer-reviewed journals and communicated to key audiences, including ambulance dispatch professionals.

SUBMITTER: Riou M

PROVIDER: S-EPMC5541602 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

The linguistic and interactional factors impacting recognition and dispatch in emergency calls for out-of-hospital cardiac arrest: a mixed-method linguistic analysis study protocol.

Riou Marine M Ball Stephen S Williams Teresa A TA Whiteside Austin A O'Halloran Kay L KL Bray Janet J Perkins Gavin D GD Cameron Peter P Fatovich Daniel M DM Inoue Madoka M Bailey Paul P Brink Deon D Smith Karen K Della Phillip P Finn Judith J

BMJ open 20170709 7

<h4>Introduction</h4>Emergency telephone calls placed by bystanders are crucial to the recognition of out-of-hospital cardiac arrest (OHCA), fast ambulance dispatch and initiation of early basic life support. Clear and efficient communication between caller and call-taker is essential to this time-critical emergency, yet few studies have investigated the impact that linguistic factors may have on the nature of the interaction and the resulting trajectory of the call. This research aims to provid ...[more]

PMID: 28694349

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Project description:BACKGROUND:Little is known regarding paediatric medical emergency calls to Danish Emergency Medical Dispatch Centres (EMDC). This study aimed to investigate these calls, specifically the medical issues leading to them and the pre-hospital units dispatched to the paediatric emergencies. METHODS:We performed a retrospective, observational study on paediatric medical emergency calls managed by the EMDC in the Region of Southern Denmark in February 2016. We reviewed audio recordings of emergency calls and ambulance records to identify calls concerning patients ≤ 15 years. We examined EMDC dispatch records to establish how the medical issues leading to these calls were classified and which pre-hospital units were dispatched to the paediatric emergencies. We analysed the data using descriptive statistics. RESULTS:Of a total of 7052 emergency calls in February 2016, 485 (6.9%) concerned patients ≤ 15 years. We excluded 19 and analysed the remaining 466. The reported medical issues were commonly classified as: "seizures" (22.1%), "sick child" (18.9%) and "unclear problem" (12.9%). The overall most common pre-hospital response was immediate dispatch of an ambulance with sirens and lights with a supporting physician-manned mobile emergency care unit (56.4%). The classification of medical issues and the dispatched pre-hospital units varied with patient age. DISCUSSION:We believe our results might help focus the paediatric training received by emergency medical dispatch staff on commonly encountered medical issues, such as the symptoms and conditions pertaining to the symptom categories "seizures" and "sick child". Furthermore, the results could prove useful in hypothesis generation for future studies examining paediatric medical emergency calls. CONCLUSION:Almost 7% of all calls concerned patients ≤ 15 years. Medical issues pertaining to the symptom categories "seizures", "sick child" and "unclear problem" were common and the calls commonly resulted in urgent pre-hospital responses.

Project description:ImportanceEmergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.ObjectiveTo examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.Design, setting, and participantsThis double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.InterventionDispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.Main outcomes and measuresThe primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.ResultsA total of 169?049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P?=?.15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P?<?.001) but lower specificity (97.4% vs 99.6%; P?<?.001) and positive predictive value (17.8% vs 55.8%; P?<?.001).Conclusions and relevanceThis randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.Trial registrationClinicalTrials.gov Identifier: NCT04219306.

Project description:BackgroundThe aim of this study was to identify key indicator symptoms and patient factors associated with correct out of hospital cardiac arrest (OHCA) dispatch allocation. In previous studies, from 3% to 62% of OHCAs are not recognised by Emergency Medical Service call handlers, resulting in delayed arrival at scene.MethodsRetrospective, mixed methods study including all suspected or confirmed OHCA patients transferred to one acute hospital from its associated regional Emergency Medical Service in England from 1/7/2013 to 30/6/2014. Emergency Medical Service and hospital data, including voice recordings of EMS calls, were analysed to identify predictors of recognition of OHCA by call handlers. Logistic regression was used to explore the role of the most frequently occurring (key) indicator symptoms and characteristics in predicting a correct dispatch for patients with OHCA.ResultsA total of 39,136 dispatches were made which resulted in transfer to the hospital within the study period, including 184 patients with OHCA. The use of the term 'Unconscious' plus one or more of symptoms 'Not breathing/Ineffective breathing/Noisy breathing' occurred in 79.8% of all OHCAs, but only 72.8% of OHCAs were correctly dispatched as such. 'Not breathing' was associated with recognition of OHCA by call handlers (Odds Ratio (OR) 3.76). The presence of key indicator symptoms 'Breathing' (OR 0.29), 'Reduced or fluctuating level of consciousness' (OR 0.24), abnormal pulse/heart rate (OR 0.26) and the characteristic 'Female patient' (OR 0.40) were associated with lack of recognition of OHCA by call handlers (p-values < 0.05).ConclusionsThere is a small proportion of calls in which cardiac arrest indicators are described but the call is not dispatched as such. Stricter adherence to dispatch protocols may improve call handlers' OHCA recognition. The existing dispatch protocol would not be improved by the addition of further terms as this would be at the expense of dispatch specificity.

Project description:BackgroundThe European resuscitation council have highlighted emergency medical dispatch centres as an important key player for early recognition of Out-of-Hospital Cardiac Arrest (OHCA) and in providing dispatcher assisted cardiopulmonary resuscitation (CPR) before arrival of emergency medical services. Early recognition is associated with increased bystander CPR and improved survival rates. The aim of this study is to describe OHCA call handling in emergency medical dispatch centres in Copenhagen (Denmark), Stockholm (Sweden) and Oslo (Norway) with focus on sensitivity of recognition of OHCA, provision of dispatcher-assisted CPR and time intervals when CPR is initiated during the emergency call (NO-CPRprior), and to describe OHCA call handling when CPR is initiated prior to the emergency call (CPRprior).MethodsBaseline data of consecutive OHCA eligible for inclusion starting January 1st 2016 were collected from respective cardiac arrest registries. A template based on the Cardiac Arrest Registry to Enhance Survival definition catalogue was used to extract data from respective cardiac arrest registries and from corresponding audio files from emergency medical dispatch centres. Cases were divided in two groups: NO-CPRprior and CPRprior and data collection continued until 200 cases were collected in the NO-CPRprior-group.ResultsNO-CPRprior OHCA was recognised in 71% of the calls in Copenhagen, 83% in Stockholm, and 96% in Oslo. Abnormal breathing was addressed in 34, 7 and 98% of cases and CPR instructions were started in 50, 60, and 80%, respectively. Median time (mm:ss) to first chest compression was 02:35 (Copenhagen), 03:50 (Stockholm) and 02:58 (Oslo). Assessment of CPR quality was performed in 80, 74, and 74% of the cases. CPRprior comprised 71 cases in Copenhagen, 9 in Stockholm, and 38 in Oslo. Dispatchers still started CPR instructions in 41, 22, and 40% of the calls, respectively and provided quality assessment in 71, 100, and 80% in these respective instances.ConclusionsWe observed variations in OHCA recognition in 71-96% and dispatcher assisted-CPR were provided in 50-80% in NO-CPRprior calls. In cases where CPR was initiated prior to emergency calls, dispatchers were less likely to start CPR instructions but provided quality assessments during instructions.

Project description:BackgroundShortness of breath is a common complaint among individuals contacting emergency communication center (EMCCs). In some prehospital system, emergency medical services include an advanced life support (ALS)-capable team. Whether such team should be dispatched during the phone call or delayed until the BLS-capable paramedic team reports from the scene is unclear. We aimed to evaluate the impact of delayed MMT dispatch until receiving the paramedic review compared to immediate dispatch at the time of the call on patient outcomes.MethodsA cross-sectional study conducted in Lyon, France, using data obtained from the departmental EMCC during the period from January to December 2019. We included consecutive calls related to adult patients experiencing acute respiratory distress. Patients from the two groups (immediate mobile medical team (MMT) dispatch or delayed MMT dispatch) were matched on a propensity score, and a conditional weighted logistic regression assessed the adjusted odds ratios (ORs) for each outcome (mortality on days 0, 7 and 30).ResultsA total of 870 calls (median age 72 [57-84], male 466 53.6%) were sought for analysis [614 (70.6%) "immediate MMT dispatch" and 256 (29.4%) "delayed MMT" groups]. The median time before MMT dispatch was 25.1 min longer in the delayed MMT group (30.7 [26.4-36.1] vs. 5.6 [3.9-8.8] min, p < 0.001). Patients subjected to a delayed MMT intervention were older (median age 78 [66-87] vs. 69 [53-83], p < 0.001) and more frequently highly dependent (16.3% vs. 8.6%, p < 0.001). A higher proportion of patients in the delayed MMT group required bag valve mask ventilation (47.3% vs. 39.1%, p = 0.03), noninvasive ventilation (24.6% vs. 20.0%, p = 0.13), endotracheal intubation (7.0% vs. 4.1%, p = 0.07) and catecholamine infusion (3.9% vs. 1.3%, p = 0.01). After propensity score matching, mortality at day 0 was higher in the delayed MMT group (9.8% vs. 4.2%, p = 0.002). Immediate MMT dispatch at the call was associated with a lower risk of mortality on day 0 (0.60 [0.38;0.82], p < 0.001) day 7 (0.50 [0.27;0.72], p < 0.001) and day 30 (0.56 [0.35;0.78], p < 0.001) CONCLUSIONS: This study suggests that the deployment of an MMT at call in patients in acute respiratory distress may result in decreased short to medium-term mortality compared to a delayed MMT following initial first aid assessment.

Project description:BackgroundAlthough studies have reported variation in out-of-hospital cardiac arrest (OHCA) survival by geographic location, little is known about variation in OHCA survival at the level of emergency medical service (EMS) agencies-which may have modifiable practices, unlike counties and regions. We quantified the variation in OHCA survival across EMS agencies and explored whether variation in 2 specific EMS resuscitation practices were associated with survival to hospital admission.MethodsWithin the Cardiac Arrest Registry to Enhance Survival, a prospective registry representing ≈51% of the US population, we identified 258 342 OHCAs from 764 EMS agencies with >10 OHCA cases annually during 2015 to 2019. Using hierarchical logistic regression, risk-standardized rates of survival to hospital admission were computed for each EMS agency. We quantified inter-agency variation in survival with median odds ratios and assessed the association of 2 resuscitation practices (EMS response time and the proportion of OHCAs with termination of resuscitation without meeting futility criteria) with EMS agency survival rates to hospital admission.ResultsAcross 764 EMS agencies comprising 258 342 OHCAs, the median risk-standardized rate of survival to hospital admission was 27.3% (interquartile range, 24.5%-30.1%; range: 16.0%-45.6%). The adjusted median odds ratio was 1.35 (95% CI, 1.32-1.39), denoting that the odds of survival of 2 patients with identical covariates varied by 35% at 2 randomly selected EMS agencies. EMS agencies in the lowest quartile of risk-standardized survival had longer EMS response times when compared with the highest quartile (12.0±3.4 versus 9.0±2.6 minutes; P<0.001), and a higher proportion of OHCAs with termination of resuscitation without meeting futility criteria (27.9±16.1% versus 18.9±11.4%; P<0.001).ConclusionsSurvival after OHCA varies widely across EMS agencies. EMS response times and termination of resuscitation practices were associated with agency-level rates of survival to hospital admission, suggesting potentially modifiable practices which can improve OHCA survival.

Dataset Information

The linguistic and interactional factors impacting recognition and dispatch in emergency calls for out-of-hospital cardiac arrest: a mixed-method linguistic analysis study protocol.

Introduction

Methods and analysis

Ethics and dissemination

Publications

The linguistic and interactional factors impacting recognition and dispatch in emergency calls for out-of-hospital cardiac arrest: a mixed-method linguistic analysis study protocol.

Similar Datasets

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