An analysis of the descriptors of acute myocardial infarction used by South Africans when calling for an ambulance from a private emergency call centre

Buma, Chloe; Saunders, Colleen; Watermeyer, Jennifer; Stassen, Willem

doi:10.1016/j.afjem.2020.06.012

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…A random sample of 50 cases were chosen per language, based on previous, similar studies in this field. 15 , 16 , 17 However, owing to the limited number of isiXhosa and Afrikaans calls, all occurrences in these languages were sampled.…”

Section: Methodsmentioning

confidence: 99%

“…The research team has prior experience in the use of consensus judgement for similar studies. 15 The contact centre call taker is considered a real-world expert in this data's content, due to the nature of their daily work. They are regularly exposed to keywords and phrases spoken by callers with various first languages and dispatch resources based on what they have interpreted the call's nature.…”

Section: Methodsmentioning

confidence: 99%

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The lay descriptors of out-of-hospital cardiac arrest in the Western Cape province, South Africa

et al. 2021

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Introduction Out-of-Hospital Cardiac Arrest (OHCA) is a time-sensitive emergency requiring prompt identification and emergency care to reduce morbidity and mortality. The first step in managing OHCA is rapid identification by the emergency dispatch centre. Identification of these patients remains challenging in South Africa due to multiple languages and widely differing levels of education. This study aimed to identify the key descriptors (words and phrases) of OHCA used by callers in the Western Cape when contacting the provincial Emergency Medical Services' emergency call centre. Methodology Computer-aided dispatch data with a corresponding “ patient unresponsive ” incident type were drawn for a 12-month period (January–December 2018). Corresponding patient care records were extracted to verify OHCA. The original voice recordings between the caller and emergency call taker at the time of the emergency were extracted and transcribed verbatim. Transcriptions were subjected to inductive, qualitative content analysis to the manifest level. Descriptors of OHCA in isiXhosa, English and Afrikaans calls were coded, categorised, and quantified. Results A total of 729 confirmed OHCA cases were identified, of which 38 (5.2%) Afrikaans, 24 (3.3%) isiXhosa and a random sample of 50 (6.8%) English calls were transcribed. Following content analysis, five distinct categories were identified. The most prevalent categories were descriptors related to ill health (medical history and suspected diagnosis; 35.5%), level of consciousness (unresponsive; 18.6%) and cardiac activity (pulselessness and suspected death; 17.2%). Conclusion The vast majority of callers within the Western Cape province of South Africa use consistent descriptors across languages when requesting an ambulance for OHCA. Future studies should focus on the development and validation of OHCA recognition algorithms, based on these findings.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The lay descriptors of out-of-hospital cardiac arrest in the Western Cape province, South Africa

et al. 2021

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“…The transcriptions were originally collected in various South African languages, namely: English, Afrikaans, Zulu and Sesotho. These anonymised transcripts were obtained from previous studies on the telephonic descriptors of myocardial infarction [ 3 ], sepsis [ 7 ] and out-of-hospital cardiac arrest [ 12 ]. The telephonic conversations were transcribed and subsequently verified as either a sepsis, cardiac arrest or myocardial infarction diagnosis.…”

Section: Methodsmentioning

confidence: 99%

“…The features selected were based on previous works [ 3 , 7 , 12 ], where keywords used to describe sepsis, cardiac arrest, and acute myocardial infarction patients were identified from calls to EMS centres in South Africa. The papers quantified the frequency of keywords and from this, the ranking of descriptor categories could be realised per diagnosis.…”

Section: Methodsmentioning

confidence: 99%

“…Cases categorised as a “red priority” [ 1 ] in South Africa (patients in-need of immediate resuscitation) are dispatched immediately with national response targets stating that emergency assistance should take no longer than 15 min to get to the scene [ 2 ]. This is, however, dependant on the availability of EMS resources since South Africa, like most other African nations, has a shortage of emergency vehicles and advanced life-support providers [ 3 ] who are equipped with the skills and experience in advanced decision-making. It is therefore crucial, in an African context, that the call-takers accurately identify high-acuity cases so that resources are allocated appropriately to those who require emergency medical assistance.…”

Section: Introductionmentioning

confidence: 99%

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The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres According to Prehospital Diagnosis, by Utilising Caller Descriptions of the Incident

et al. 2021

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This paper presents the application of machine learning for classifying time-critical conditions namely sepsis, myocardial infarction and cardiac arrest, based off transcriptions of emergency calls from emergency services dispatch centers in South Africa. In this study we present results from the application of four multi-class classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest and K-Nearest Neighbor (kNN). The application of machine learning for classifying time-critical diseases may allow for earlier identification, adequate telephonic triage, and quicker response times of the appropriate cadre of emergency care personnel. The data set consisted of an original data set of 93 examples which was further expanded through the use of data augmentation. Two feature extraction techniques were investigated namely; TF-IDF and handcrafted features. The results were further improved using hyper-parameter tuning and feature selection. In our work, within the limitations of a limited data set, classification results yielded an accuracy of up to 100% when training with 10-fold cross validation, and 95% accuracy when predicted on unseen data. The results are encouraging and show that automated diagnosis based on emergency dispatch centre transcriptions is feasible. When implemented in real time, this can have multiple utilities, e.g. enabling the call-takers to take the right action with the right priority.

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The variables predictive of ambulance non-conveyance of patients in the Western Cape, South Africa

Binks,

Hardy,

Wallis

et al. 2023

African Journal of Emergency Medicine

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An analysis of the descriptors of acute myocardial infarction used by South Africans when calling for an ambulance from a private emergency call centre

Cited by 5 publications

References 32 publications

The lay descriptors of out-of-hospital cardiac arrest in the Western Cape province, South Africa

The lay descriptors of out-of-hospital cardiac arrest in the Western Cape province, South Africa

The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres According to Prehospital Diagnosis, by Utilising Caller Descriptions of the Incident

The variables predictive of ambulance non-conveyance of patients in the Western Cape, South Africa

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