Emergency Medical Services (EMS) are designed to respond to and manage patients experiencing life-threatening emergencies; however, not all emergency calls are necessarily emergent and of high acuity. Emergency responses to low-acuity patients affect not only EMS, but other areas of the health care system. However, definitions of low-acuity calls are vague and subjective; therefore, it was necessary to provide a clear description of the low-acuity patient in EMS.

The goal of this study was to develop descriptors for “low-acuity EMS patients” through expert consensus within the EMS environment.

A Modified Delphi survey was used to develop call-out categories and descriptors of low acuity through expert opinion of practitioners within EMS. Purposive, snowball sampling was used to recruit 60 participants, of which 29 completed all three rounds. An online survey tool was used and offered both binary and free-text options to participants. Consensus of 75% was accepted on the binary options while free text offered further proposals for consideration during the survey.

On completion of round two, consensus was obtained on 45% (70/155) of the descriptors, and a further 30% (46/155) consensus was obtained in round three. Experts felt that respiratory distress, unconsciousness, chest pain, and severe hemorrhage cannot be considered low acuity. For other emergency response categories, specific descriptors were offered to denote a case as low acuity.

Descriptors of low acuity in EMS are provided in both medical and trauma cases. These descriptors may not only assist in the reduction of unnecessary response and transport of patients, but also assist in identifying the most appropriate response of EMS resources to call-outs. Further development and validation are required of these descriptors in order to improve accuracy and effectiveness within the EMS dispatch environment.

Triage - the sorting of patients according to urgency of need for clinical care - is an essential part of delivering effective and efficient emergency care. But when frequent over- or under-triaging occurs, finite time and resources are diverted away from those in greatest need of care and the entire Emergency Medical Services (EMS) system is strained. In resource-constrained settings, such as South Africa, poor triage in EMS only serves to compound other contextual challenges. This study examined the accuracy of dispatcher triage over a one-year period in the Western Cape Government (WCG) EMS system in South Africa.

A retrospective analysis of existing dispatch and EMS data to assess the accuracy of dispatch-assigned priorities was conducted. The mismatch between dispatcher-assigned call priority and triage levels determined by EMS personnel was analyzed via over- and under-triage rates, sensitivity and specificity, and positive and negative predictive values (PPVs and NPVs, respectively).

A total of 185,166 records from December 2016 through November 2017 were analyzed. Across all dispatch complaints, the over-triage rate was 67.6% (95% CI, 66.34-68.76) and the under-triage rate was 16.2% (95% CI, 15.44-16.90). Dispatch triage sensitivity for all included records was 49.2% (95% CI, 48.10-50.38), specificity 71.9% (95% CI, 71.00-72.92), PPV 32.5% (95% CI, 30.02-34.88), and NPV 83.8% (95% CI, 81.93-85.73).

This study provides the first evaluation of dispatch triage accuracy in the WCG EMS system, identifying that the system is suffering from both under- and over-triage. Despite variance across dispatch complaints, both under- and over-triage remained higher than widely accepted norms, and all rates were significantly above acceptable target metrics described in similar studies. Results of this study will be used to motivate the development of more rigorous training programs and resources for WCG EMS dispatchers, including improved dispatch protocols for conditions suffering from high over- and under-triage.

In 2010, South Africa (SA) hosted the Fédération Internationale de Football Association (FIFA) World Cup (soccer). Emergency Medical Services (EMS) used the SA mass gathering medicine (MGM) resource model to predict resource allocation. This study analyzed data from the World Cup and compared them with the resource allocation predicted by the SA mass gathering model.

Prospectively, data were collected from patient contacts at 9 venues across the Western Cape province of South Africa. Required resources were based on the number of patients seeking basic life support (BLS), intermediate life support (ILS), and advanced life support (ALS). Overall patient presentation rates (PPRs) and transport to hospital rates (TTHRs) were also calculated.

BLS services were required for 78.4% (n = 1279) of patients and were consistently overestimated using the SA mass gathering model. ILS services were required for 14.0% (n = 228), and ALS services were required for 3.1% (n = 51) of patients. Both ILS and ALS services, and TTHR were underestimated at smaller venues.

The MGM predictive model overestimated BLS requirements and inconsistently predicted ILS and ALS requirements. MGM resource models, which are heavily based on predicted attendance levels, have inherent limitations, which may be improved by using research-based outcomes.

Historically, the quality and performance of prehospital emergency care (PEC) has been assessed largely based on surrogate, non-clinical endpoints such as response time intervals or other crude measures of care (eg, stakeholder satisfaction). However, advances in Emergency Medical Services (EMS) systems and services world-wide have seen their scope and reach continue to expand. This has dictated that novel measures of performance be implemented to compliment this growth. Significant progress has been made in this area, largely in the form of the development of evidence-informed quality indicators (QIs) of PEC.

Quality indicators represent an increasingly popular component of health care quality and performance measurement. However, little is known about the development of QIs in the PEC environment. The purpose of this study was to assess the development and characteristics of PEC-specific QIs in the literature.

A scoping review was conducted through a search of PubMed (National Center for Biotechnology Information, National Institutes of Health; Bethesda, Maryland USA); EMBase (Elsevier; Amsterdam, Netherlands); CINAHL (EBSCO Information Services; Ipswich, Massachusetts USA); Web of Science (Thomson Reuters; New York, New York USA); and the Cochrane Library (The Cochrane Collaboration; Oxford, United Kingdom). To increase the sensitivity of the literature, a search of the grey literature and review of select websites was additionally conducted. Articles were selected that proposed at least one PEC QI and whose aim was to discuss, analyze, or promote quality measurement in the PEC environment.

The majority of research (n=25 articles) was published within the last decade (68.0%) and largely originated within the USA (68.0%). Delphi and observational methodologies were the most commonly employed for QI development (28.0%). A total of 331 QIs were identified via the article review, with an additional 15 QIs identified via the website review. Of all, 42.8% were categorized as primarily Clinical, with Out-of-Hospital Cardiac Arrest contributing the highest number within this domain (30.4%). Of the QIs categorized as Non-Clinical (57.2%), Time-Based Intervals contributed the greatest number (28.8%). Population on Whom the Data Collection was Constructed made up the most commonly reported QI component (79.8%), followed by a Descriptive Statement (63.6%). Least reported were Timing of Data Collection (12.1%) and Timing of Reporting (12.1%). Pilot testing of the QIs was reported on 34.7% of QIs identified in the review.

Overall, there is considerable interest in the understanding and development of PEC quality measurement. However, closer attention to the details and reporting of QIs is required for research of this type to be more easily extrapolated and generalized.

HowardI, CameronP, WallisL, CastrenM, LindstromV. Quality Indicators for Evaluating Prehospital Emergency Care: A Scoping Review. Prehosp Disaster Med. 2018;33(1):43–52.

Little is known about the existence, distribution, and characteristics of Emergency Medical Services (EMS) systems in Africa, or the corresponding epidemiology of prehospital illness and injury.

A survey was conducted between 2013 and 2014 by distributing a detailed EMS system questionnaire to experts in paper and electronic versions. The questionnaire ascertained EMS systems’ jurisdiction, operations, finance, clinical care, resources, and regulatory environment. The discovery of respondents with requisite expertise occurred in multiple phases, including snowball sampling, a review of published scientific literature, and a rigorous search of the Internet.

The survey response rate was 46%, and data represented 49 of 54 (91%) African countries. Twenty-five EMS systems were identified and distributed among 16 countries (30% of African countries). There was no evidence of EMS systems in 33 (61%) countries. A total of 98,574,731 (8.7%) of the African population were serviced by at least one EMS system in 2012. The leading causes of EMS transport were (in order of decreasing frequency): injury, obstetric, respiratory, cardiovascular, and gastrointestinal complaints. Nineteen percent of African countries had government-financed EMS systems and 26% had a toll-free public access telephone number. Basic emergency medical technicians (EMTs) and Basic Life Support (BLS)-equipped ambulances were the most common cadre of provider and ambulance level, respectively (84% each).

Emergency Medical Services systems exist in one-third of African countries. Injury and obstetric complaints are the leading African prehospital conditions. Only a minority (<9.0%) of Africans have coverage by an EMS system. Most systems were predominantly BLS, government operated, and fee-for-service.

Mould-MillmanNK, DixonJM, SefaN, YanceyA, HollongBG, HagahmedM, GindeAA, WallisLA. The State of Emergency Medical Services (EMS) Systems in Africa. Prehosp Disaster Med. 2017;32(3):273–283.

Vital signs remain important clinical indicators in the management of trauma. Tissue injury and ischemia cause tachycardia and hypertension, which are mediated via the sympathetic nervous system (SNS). Spinal immobilization is known to cause discomfort, and it is not known how this might influence the SNS and contribute to abnormal vital signs.

This study aimed to establish whether the pain and discomfort associated with spinal immobilization and the maneuvers commonly used in injured patients (eg, log roll) affect the Heart rate (HR), Systolic Blood Pressure (SBP) and Respiratory rate (RR). The null hypothesis was that there are no effects.

A prospective, unblinded, repeated-measure study of 53 healthy subjects was used to test the null hypothesis. Heart rate, BP and RR were measured at rest (five minutes), after spinal immobilization (10 minutes), following log roll, with partial immobilization (10 minutes) and again at rest (five minutes). A visual analog scale (VAS) for both pain and discomfort were also collected at each stage. Results were statistically compared.

Pain VAS increased significantly during spinal immobilization (3.8 mm, P < .01). Discomfort VAS increased significantly during spinal immobilization, after log roll and during partial immobilization (17.7 mm, 5.8 mm and 8.9 mm, respectively; P < .001). Vital signs however, showed no clinically relevant changes.

Spinal immobilization does not cause a change in vital signs despite a significant increase in pain and discomfort. Since no relationship appears to exist between immobilization and abnormal vital signs, abnormal vital signs in a clinical situation should not be considered to be the result of immobilization. Likewise, pain and discomfort in immobilized patients should not be disregarded due to lack of changes in vital signs.

BruijnsS, GulyH, WallisL. Effect of Spinal Immobilization on Heart Rate, Blood Pressure and Respiratory Rate. Prehosp Disaster Med. 2013;28(2):1-5.

A modified Medical Resource Model to predict the medical resources required at mass gatherings based on the risk profile of events has been developed. This study was undertaken to validate this tool using data from events held in both a developed and a developing country.

A retrospective study was conducted utilizing prospectively gathered data from individual events at Old Trafford Stadium in Manchester, United Kingdom, and Ellis Park Stadium, Johannesburg, South Africa. Both stadia are similar in design and spectator capacity. Data for Professional Football as well as Rugby League and Rugby Union (respectively) matches were used for the study. The medical resources predicted for the events were determined by entering the risk profile of each of the events into the Medical Resource Model. A recently developed South African tool was used to predetermine medical staffing for mass gatherings. For the study, the medical resources actually required to deal with the patient load for events within the control sample from the two stadia were compared with the number of needed resources predicted by the Medical Resource Model when that tool was applied retrospectively to the study events. The comparison was used to determine if the newly developed tool was either over- or under-predicting the resource requirements.

In the case of Ellis Park, the model under-predicted the basic life support (BLS) requirement for 1.5% of the events in the data set. Mean over-prediction was 209.1 minutes for BLS availability. Old Trafford displayed no events for which the Medical Resource Model would have under-predicted. The mean over-prediction of BLS availability for Old Trafford was 671.6 minutes. The intermediate life support (ILS) requirement for Ellis Park was under-predicted for seven of the total 66 events (10.6% of the events), all of which had one factor in common, that being relatively low spectator attendance numbers. Modelling for ILS at Old Trafford did not under-predict for any events. The ILS requirements showed a mean over-prediction of 161.4 minutes ILS availability for Ellis Park compared with 425.2 minutes for Old Trafford. Of the events held at Ellis Park, the Medical Resource Model under-predicted the ambulance requirement in 4.5% of the events. For Old Trafford events, the under-prediction was higher: 7.5% of cases.

The medical resources that are deployed at a mass gathering should best match the requirement for patient care at a particular event. An important consideration for any model is that it does not continually under-predict the resources required in relation to the actual requirement. With the exception of a specific subset of events at Ellis Park, the rate of under-prediction for this model was acceptable.

SmithWP, TuffinH, StrattonSJ, WallisLA. Validation of a Modified Medical Resource Model for Mass Gatherings. Prehosp Disaster Med.2013;28(1):1-7.

Africa's first postgraduate training program in emergency medicine (EM) was established at the University of Cape Town/Stellenbosch University (UCT/SUN) in 2004. This study of the UCT/SUN EM program investigated the backgrounds, perceptions, and experiences of its graduates.

This was a cross-sectional descriptive study. The study population was the 30 graduates from the first four classes in the UCT/SUN EM program (2007–2010). We employed a scripted interview with a combination of closed and open-ended questions. Data were analyzed using the thematic method of qualitative analysis.

Twenty-seven (90%) graduates were interviewed. Initial career goals were primarily (78%) to practice EM in a nonacademic clinical capacity. At the time of the interview, 52% held academic positions, 15% had nonacademic clinical positions, and 33% had temporary positions and were looking for other posts. The three most commonly cited strengths of their program were diversity of clinical rotations (85%), autonomy and procedural experience (63%), and importance of being pioneers within Africa (52%). The three most commonly cited weaknesses were lack of bedside teaching in the ED (96%), lack of career options after graduation (74%), and lack of preparation for academic careers (70%).

The lessons identified from structured interviews with graduates from Africa’s first EM training include the importance of strong clinical training, difficulty of ensuring bedside teaching in a new program, the necessity of ensuring postgraduation positions, and the need for academic training. These findings may be useful for other developing countries looking to start EM training programs.