FirstAED emergency dispatch, global positioning of community first responders with distinct roles - a solution to reduce the response times and ensuring an AED to early defibrillation in the rural area Langeland

Henriksen, Finn Lund; Schorling, Per; Hansen, Bruno; Schakow, Henrik; Larsen, Mogens Lytken

doi:10.1504/ijnvo.2016.075131

Cited by 24 publications

(18 citation statements)

References 14 publications

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“…As the majority of OHCAs occur in the home, bystander characteristics are more important than victim factors training communities to perform CPR can increase bystander CPR rates and overall survival [7,8]. Ventricular fibrillation (VF) and ventricular tachycardia without a pulse (VT), frequently, are the rhythms found in persons with witnessed CA, as a result of which it is extremely important that both CPR and defibrillation should be undertaken at an early stage [9,10].…”

Section: Discussionmentioning

confidence: 99%

Out of hospital cardiac arrest: when to resuscitate

Zelfani¹,

Manai²,

Riahi³

et al. 2019

Pan Afr Med J

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IntroductionThis study explores why resuscitation is withheld when mobile emergency medical team arrive at the scene of a cardiac arrest.MethodsWe conducted a prospective, observational study in pre hospital emergency services. We included adults' patients, with a suspicion of non-traumatic cardiac arrest (CA) in an out of hospital environment, who received or not cardiopulmonary resuscitation (CPR) by our mobile emergency medical service teams. An analytic study was conducted in order to identify independent factors that could influence the decision to resuscitate OHCA.ResultsDuring study, 228 patients were enrolled, the mean age was 64 +/- 14 years and 59% were men. Eighteen patients (8%) received bystander CPR by witnesses. The median time elapsed to arrive at the scene was 13 [8-25] min. The median “noflow” was 22 [10-34] min. The resuscitation decision was taken by the mobile EMS staff for 106 patients (46.5%). For other patients, the decision not to resuscitate was motivated solely by the finding of a confirmed state of death in an elderly patient (p = 0.045). The predictive decision factor for resuscitation was the no flow time less than 18.5 min, Odds Ratio adjusted with 95% confidence interval to: 1.38 (1.24 - 3.55) (p <0.001). Overall out of hospital survival rate was 17% of resuscitated patients.ConclusionThe decision to resuscitate a cardiac arrest outside of the hospital depends more on the “no flow” time than on the presumed etiologies.

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Section: Discussionmentioning

confidence: 99%

Out of hospital cardiac arrest: when to resuscitate

Zelfani¹,

Manai²,

Riahi³

et al. 2019

Pan Afr Med J

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“…This in turn ensures that patients have shorter waiting times through location planning and increased ambulance availability when the need arises. This is an important goal for pre-hospital emergency medical services [1][2][3][4] and especially necessary for patients in critical conditions [5,6]. Furthermore, it helps in the efficient staffing for shifts in the hospitals, as well as early identification of any surges in ambulance demands.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Lin

Cheong

et al. 2020

IJERPH

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The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

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“…A Community First Responder (CFR) “ is a member of the public who receives basic emergency care training and volunteers to help their community by responding to appropriate medical emergencies while an ambulance is en route ” [ 1 ]. CFRs were introduced to help improve response to emergencies such as out-of-hospital cardiac arrests and more recently to help ambulance services reduce response times to incidents more generally, especially in rural areas [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…There is evidence that in rural communities in particular, having CFRs can reassure patients [ 4 , 5 ] and reduce time to defibrillation [ 3 ]. Intervening in the chain of survival by reducing the time to defibrillation improves survival rates [ 2 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Perceptions and experiences of community first responders on their role and relationships: qualitative interview study

Phung

Trueman

Togher

et al. 2018

Scand J Trauma Resusc Emerg Med

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BackgroundCommunity First Responders (CFRs) are lay volunteers who respond to medical emergencies. We aimed to explore perceptions and experiences of CFRs in one scheme about their role.MethodsWe conducted semi-structured interviews with a purposive sample of CFRs during June and July 2016 in a predominantly rural UK county. Interviews were transcribed verbatim and analysed using the Framework method, supported by NVivo 10.ResultsWe interviewed four female and 12 male adult CFRs aged 18–65+ years with different levels of expertise and tenures. Five main themes were identified: motivation and ongoing commitment; learning to be a CFR; the reality of being a CFR; relationships with statutory ambulance services and the public; and the way forward for CFRs and the scheme. Participants became CFRs mainly for altruistic reasons, to help others and put something back into their community, which contributed to personal satisfaction and helped maintain their involvement over time. CFRs valued scenario-based training and while some were keen to access additional training to enable them to attend a greater variety of incidents, others stressed the importance of maintaining existing abilities and improving their communication skills. They were often first on scene, which they recognised could take an emotional toll but for which they found informal support mechanisms helpful. Participants felt a lack of public recognition and sometimes were undervalued by ambulance staff, which they thought arose from a lack of clarity over their purpose and responsibilities. Although CFRs perceived their role to be changing, some were fearful of extending the scope of their responsibilities. They welcomed support for volunteers, greater publicity and help with fundraising to enable schemes to remain charities, while complementing the role of ambulance services.DiscussionCFR schemes should consider the varying training, development and support needs of staff. CFRs wanted schemes to be complementary but distinct from ambulance services. Further information on outcomes and costs of the CFR contribution to prehospital care is needed.ConclusionOur findings provide insight into the experiences of CFRs, which can inform how the role might be better supported. Because CFR schemes are voluntary and serve defined localities, decisions about levels of training, priority areas and targets should be locally driven. Further research is required on the effectiveness, outcomes, and costs of CFR schemes and a wider understanding of stakeholder perceptions of CFR and CFR schemes is also needed.

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FirstAED emergency dispatch, global positioning of community first responders with distinct roles - a solution to reduce the response times and ensuring an AED to early defibrillation in the rural area Langeland

Cited by 24 publications

References 14 publications

Out of hospital cardiac arrest: when to resuscitate

Out of hospital cardiac arrest: when to resuscitate

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Perceptions and experiences of community first responders on their role and relationships: qualitative interview study

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