Features of Emergency Medical System calls that facilitate or inhibit Emergency Medical Dispatcher recognition that a patient is in, or at imminent risk of, cardiac arrest: A systematic mixed studies review

Identifying patients at imminent risk of out-of-hospital cardiac arrest during the Emergency Medical call: The views of call-takers

Introduction

There is little research on the triage of patients who are not yet in cardiac arrest when the emergency call is initiated, but who deteriorate and suffer a cardiac arrest during the prehospital phase of care. The aim of this study was to investigate Emergency Operation Centre staff views on ways to improve the early identification of patients who are at imminent risk of cardiac arrest, and the barriers to achieving this.

Methods

A qualitative interview and focus group study was conducted in two large Emergency Medical Services in England, United Kingdom. Twelve semi-structured interviews and one focus group were completed with Emergency Operations Centre staff. Data were analysed using reflexive thematic analysis.

Results

Three main themes were identified: The dispatch protocol and call-taker audit; Identifying and responding to deteriorating patients; Education, knowledge and skills. Barriers to recognising patients at imminent risk of cardiac arrest include a restrictive dispatch protocol, limited opportunity to monitor a patient, compliance auditing and inadequate education. Clinician support is not always optimal, and a lack of patient outcome feedback restricts dispatcher learning and development. Suggested remedies include improvements in training and education (call-takers and the public), software, clinical support and patient outcome feedback.

Conclusions

Emergency Operation Centre staff identified a multitude of ways to improve the identification of patients who are at imminent risk of out-of-hospital cardiac arrest during the Emergency Medical Service call. Suggested areas for improvement include education, triage software, clinical support redesign and patient outcome feedback.

When the machine is wrong. Characteristics of true and false predictions of Out-of-Hospital Cardiac arrests in emergency calls using a machine-learning model

BACKGROUND

A machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA.

METHODS

All emergency calls were linked to the dispatch database and verified OHCAs were identified by linkage to the Danish Cardiac Arrest Registry. Calls with either false negative or false positive predictions of OHCA were evaluated by trained auditors. Descriptive analyses were performed with absolute numbers and percentages reported.

RESULTS

The machine-learning model processed 169,236 calls to Copenhagen EMS and suspected 5,811 (3.4%) of the calls as OHCA, resulting in 84.5% sensitivity and 97.1% specificity. Among OHCAs not recognised by machine-learning model, a condition completely different from OHCA was presented by caller in 31% of the cases. In 28% of unrecognised calls, patient was reported breathing normally, and language barriers were identified in 23% of the cases. Among falsely suspected OHCA, the patient was reported unconscious in 28% of the cases, and in 13% of the false positive cases the machine-learning model interpreted calls regarding dead patients with irreversible signs of death as OHCA.

CONCLUSION

Continuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.

Emotions in telephone calls to emergency medical services involving out-of-hospital cardiac arrest: A scoping review

Aims

The purpose of this scoping review was to identify and synthesise existing research evidence on emotions in the context of emergency phone calls to emergency medical services (EMS) involving out-of-hospital cardiac arrest (OHCA). The specific objectives were to identify studies that (1) described emotions during emergency OHCA calls; (2) specified an instrument or method for measuring/assessing emotions; and (3) examined the relationship between emotions and call outcomes or patient outcomes.

Methods/Data sources

Five databases were searched on 18 November 2021: Medline, Embase, PsycInfo, CINAHL, and the Cochrane Review Database. Included studies required the following three concepts to be addressed: emotions in the context of EMS calls that involved OHCA. Calls also needed to be made by a ‘second-party’ caller; and each study needed to address at least one of the three specific objectives, as outlined above.

The review was conducted in accordance with the Joanna Briggs Institute guidelines for evidence synthesis for scoping reviews.

Results

Thirteen eligible studies were included for synthesis. All studies met Objective 1; six studies met Objective 2; and seven met Objective 3. One study reported patient fatality due to heightened emotions and ensuing ineffective communications between callers and call-takers.

Conclusion

The review highlights a significant gap in the evidence base of emotions in emergency OHCA-related calls, and the need for a more comprehensive and effective method in assessing and measuring emotions in this context. Relationships between emotions (their expressions and perceptions) and call outcomes (including patient outcomes) also need more rigorous investigation.