Medical Priority Dispatch System Research Articles

Dispatch Categories as Indicators of Out-of-Hospital Time Critical Interventions and Associated Emergency Department Outcomes

Objectives Emergency medical services (EMS) systems increasingly grapple with rising call volumes and workforce shortages, forcing systems to decide which responses may be delayed. Limited research has linked dispatch codes, on-scene findings, and emergency department (ED) outcomes. This study evaluated the association between dispatch categorizations and time-critical EMS responses defined by prehospital interventions and ED outcomes. Secondarily, we proposed a framework for identifying dispatch categorizations that are safe or unsafe to hold in queue. Methods This retrospective, multi-center analysis encompassed all 9-1-1 responses from 8 accredited EMS systems between 1/1/2021 and 06/30/2023, utilizing the Medical Priority Dispatch System (MPDS). Independent variables included MPDS Protocol numbers and Determinant levels. EMS treatments and ED diagnoses/dispositions were categorized as time-critical using a multi-round consensus survey. The primary outcome was the proportion of EMS responses categorized as time-critical. A non-parametric test for trend was used to assess the proportion of time-critical responses Determinant levels. Based on group consensus, Protocol/Determinant level combinations with at least 120 responses (∼1 per week) were further categorized as safe to hold in queue (<1% time-critical intervention by EMS and <5% time-critical ED outcome) or unsafe to hold in queue (>10% time-critical intervention by EMS or >10% time-critical ED outcome). Results Of 1,715,612 EMS incidents, 6% (109,250) involved a time-critical EMS intervention. Among EMS transports with linked outcome data (543,883), 12% had time-critical ED outcomes. The proportion of time-critical EMS interventions increased with Determinant level (OMEGA: 1%, ECHO: 38%, p-trend < 0.01) as did time-critical ED outcomes (OMEGA: 3%, ECHO: 31%, p-trend < 0.01). Of 162 unique Protocols/Determinants with at least 120 uses, 30 met criteria for safe to hold in queue, accounting for 8% (142,067) of incidents. Meanwhile, 72 Protocols/Determinants met criteria for unsafe to hold, accounting for 52% (883,683) of incidents. Seven of 32 ALPHA level Protocols and 3/17 OMEGA level Protocols met the proposed criteria for unsafe to hold in queue. Conclusions In general, Determinant levels aligned with time-critical responses; however, a notable minority of lower acuity Determinant level Protocols met criteria for unsafe to hold. This suggests a more nuanced approach to dispatch prioritization, considering both Protocol and Determinant level factors.

Performance of the Medical Priority Dispatch System® in Identifying Patients Requiring Chest Compressions at Overdose Prevention Services: A Retrospective Cohort Study

Background and Aims The Medical Priority Dispatch System (MPDS)® is used to triage 9-1-1 calls according to acuity, with certain coding receiving telecommunicator cardiopulmonary resuscitation (T-CPR) for suspected out-of-hospital cardiac arrest (OHCA). However, this may be challenging for those with drug poisoning emergencies, who may resemble OHCA. We sought to examine the performance of the system to correctly identify cases requiring T-CPR, specifically at overdose prevention services (OPS). Methods This retrospective cohort study included patients attended by the provincial emergency medical system (EMS) (May 1, 2019–January 31, 2023). We calculated the diagnostic performance of MPDS® assessment of whether the case required T-CPR instructions against the gold standard of whether the patient was found pulseless on EMS clinician arrival. We compared performance among subgroups, specifically OPS vs other locations and drug poisoning-classified cases vs other case classifications. Results Comparing OPS to other locations, the sensitivity of MPDS® was similar (66.7% vs 62.4%, p = 0.4), with lower specificity (87.3% vs 98.1%, p < 0.01) and positive predictive value (0.3% vs 35.7%, p < 0.01) and higher negative predictive value (99.9% vs 99.4%, p < 0.01). The negative likelihood ratio of MPDS® was 0.381 at OPS locations, compared with 0.383 at other locations, while the positive likelihood ratio was 5.24, compared with 32.36. In patients with drug poisoning emergencies, compared with other 9-1-1 events, MPDS® had higher sensitivity (83.6% vs 60.6%, p < 0.01) but lower specificity (77.6% vs 98.9%, p < 0.01) and positive predictive value (10.5% vs 48.5%, p < 0.01), and similar negative predictive value (99.33% vs 99.35%, p = 0.03). The negative likelihood ratio of MPDS® was 0.212 in drug poisoning emergencies compared with 0.398 for all other presentations, and the positive likelihood ratio was 3.73 compared with 57.88. Discussion and Conclusions The ability of MPDS® to correctly identify patients needing telecommunicator cardiopulmonary resuscitation instructions differed between OPS settings and other locations, frequently recommending T-CPR for patients not suffering OHCA at an OPS. Different strategies developed in collaboration with people who use substances are required to better tailor dispatch instructions prior to EMS arrival to avoid delays in life-saving interventions.

Emergency medical dispatchers’ experiences of using the Medical Priority Dispatch System telephone triage to identify maternity emergencies: a qualitative focus group study

ObjectivesThe ambulance service plays a pivotal role in the provision of care in out-of-hospital maternity emergencies. Telephone triage of this patient group is complex and must be sensitive to an...

Abstract 300: An Opportunity for Awareness: The International Academy of Emergency Medical Dispatch's Stroke Diagnostic Tool is Predictive, but Underutilized, for Hospital Confirmed Large Vessel Occlusion Strokes

Background: Accurate identification of ischemic stroke from large vessel occlusion (LVO) by 9-1-1 professionals can facilitate prompt patient transfer to stroke centers that perform endovascular thrombectomy. We sought to determine the proportion of LVO stroke patients that were evaluated using the Medical Priority Dispatch System's (MPDS) Stroke Diagnostic Tool (SDxT). Methods: Structured, retrospective review of our institution’s stroke registry, EMS Records, and dispatch codes between January 1, 2019 and November 30, 2022. Inclusion: Adult (age &gt;17), accessed the 9-1-1 system and transported by a ground EMS agency for an acute stroke. Exclusion: age &lt;18, pregnant, incarcerated, cases with missing 9-1-1 data, and intracranial hemorrhage. Two trained/monitored abstractors, blinded to the study purpose, used a standardized data collection tool/dictionary to extract data. Data included MPDS codes, SDxT outcome, EMS care, demographics, hospital NIHSS score, advanced imaging, tPA administration, and mechanical thrombectomy. Primary outcome: the proportion of LVO cases that had an acute stroke (28 protocol) dispatch code. Secondary outcome: the proportion of LVO cases that that a SDxT score indicating “strong or clear evidence of a stroke”. We report proportions and 95% CI's as appropriate. Results: 60 patients met inclusion/exclusion criteria. 11/60, 18.3 % (95%CI= 10.4, 30) of the patients had complete data and a LVO. 54% were male, the median (IQR) age was 69.5 (61.3-77.5), and the median (IQR) NIHSS was 16 (5-19.75). Primary outcome: 45.5 % (95% CI= 21.3.72.0) of the LVO’s were coded as an Acute Stroke by the 9-1-1 center. Of those, 3/5 utilized the stroke diagnostic tool, all 3 of these were determined to have "Clear Evidence of Stroke". Limitations: Retrospective, unlinked data, small number of LVO's. Conclusion: When utilized, the SDxT correctly identified all LVO’s. There is an opportunity to increase awareness and utilization of the SDxT by 9-1-1 centers. Future research should focus on identifying barriers to stroke recognition and provide additional tools to address the real-life challenges our 9-1-1 communications specialists face when rapidly assessing for an acute stroke over the phone.

Nine golden codes: improving the accuracy of Helicopter Emergency Medical Services (HEMS) dispatch—a retrospective, multi-organisational study in the East of England

BackgroundHelicopter Emergency Medical Services (HEMS) are a limited and expensive resource, and should be intelligently tasked. HEMS dispatch was identified as a key research priority in 2011, with a call to identify a ‘general set of criteria with the highest discriminating potential’. However, there have been no published data analyses in the past decade that specifically address this priority, and this priority has been reaffirmed in 2023. The objective of this study was to define the dispatch criteria available at the time of the initial emergency call with the greatest HEMS utility using a large, regional, multi-organizational dataset in the UK.MethodsThis retrospective observational study utilized dispatch data from a regional emergency medical service (EMS) and three HEMS organisations in the East of England, 2016–2019. In a logistic regression model, Advanced Medical Priority Dispatch System (AMPDS) codes with ≥ 50 HEMS dispatches in the study period were compared with the remainder to identify codes with high-levels of HEMS patient contact and HEMS-level intervention/drug/diagnostic (HLIDD). The primary outcome was to identify AMPDS codes with a > 10% HEMS dispatch rate of all EMS taskings that would result in 10–20 high-utility HEMS dispatches per 24-h period in the East of England. Data were analysed in R, and are reported as number (percentage); significance was p < 0.05.ResultsThere were n = 25,491 HEMS dispatches (6400 per year), of which n = 23,030 (90.3%) had an associated AMPDS code. n = 13,778 (59.8%) of HEMS dispatches resulted in patient contact, and n = 8437 (36.6%) had an HLIDD. 43 AMPDS codes had significantly greater rates of patient contact and/or HLIDD compared to the reference group. In an exploratory analysis, a cut-off of ≥ 70% patient contact rate and/or ≥ 70% HLIDD (with a > 10% HEMS dispatch of all EMS taskings) resulted in 17 taskings per 24-h period. This definition derived nine AMPDS codes with high HEMS utility.ConclusionWe have identified nine ‘golden’ AMPDS codes, available at the time of initial emergency call, that are associated with high-levels of whole-system and HEMS utility in the East of England. We propose that UK EMS should consider immediate HEMS dispatch to these codes.

International Comparison of Ambulance Times Terminology and Definitions: A Benchmarking Study

Introduction:Ambulance times are internationally recognized Key Performance Indicators (KPI) for prehospital care. International benchmarking by comparing ambulance times between countries is a valuable method to help to identify strengths and weaknesses across healthcare systems. However, ambulance times are not standardized across or sometimes even within countries. Thus, this benchmarking study aims to compare terminology and definitions of ambulance times from the ambulance services of a range of countries to facilitate international benchmarking.Method:A 23-point questionnaire was developed and pilot-tested on members of international emergency care organizations. The final questionnaire was administered to domestic and international Ambulance Services, who use the Advanced Medical Priority Dispatch System, asking for the terminology and definitions for times from “call received” to “arrival at hospital”. This included “clock start” and “clock stop” times. We asked for the ambulance terms and related variable names in the computer aided dispatch/reporting system. We engaged with clinical stakeholders and Patient and Public Involvement Contributors throughout the process.Results:We gathered information from 10 international ambulance services, representing nine countries, and three continents. Some services in the United Kingdom have standardized ambulance times terminology and definitions. However, in the majority of cases terminology differed greatly between countries, and at times within countries and between reports. Definitions of ambulance times varied between countries and regions, with some having different clock start and stop times and others not collecting data on the same time periods.Conclusion:The current level of variation in international ambulance times terminology and definitions poses a challenge for international benchmarking and research. International consensus or harmonization of language and definitions would result in more efficient and accurate global comparison. On a smaller scale, defining terms in publications and reports would begin facilitating this process.

Abstract WMP47: Low Sensitivity Of Widely Used Emergency Dispatch Algorithm For Thrombectomy Patients - Implications For Mobile Stroke Units

Introduction: Widely used emergency dispatch algorithms such as the Advanced Medical Priority Dispatch System (AMPDS) have limited diagnostic accuracy for prehospital diagnosis of stroke. With advent of mobile stroke units (MSU), this inaccuracy prevents optimal dispatch to patients who may benefit. Expedited endovascular thrombectomy (EVT) is a major contributor to net benefit of MSUs. We assessed the accuracy of AMPDS for recognizing stroke in thrombectomy patients in the Australian state of Victoria. Methods: We included consecutive patients accepted for EVT (direct and secondary transfer) to The Royal Melbourne Hospital from 2007-2021 in whom linked AMPDS dispatch codes could be obtained from Ambulance Victoria. The primary outcome was the proportion of cases dispatched as stroke vs non-stroke with subgroup analyses of the effect of baseline clinical severity, metropolitan vs rural dispatch and time to thrombectomy. Chi square and Mann Whitney tests were used as appropriate. Results: A total of n=618 patients were included with baseline NIHSS 16 (IQR 10-20). Of these, only 62% (95% CI 58-66) were initially dispatched as suspected stroke, with the most common non-stroke diagnoses being “Unconscious/Fainting” (19.2%) and “Falls” (6.9%). Those with a higher baseline severity (NIHSS ≥10) were less likely to be classified as stroke than those with lower severity (59% vs 76%, p&lt;0.001), while no difference was found between metropolitan and rural patients (p=0.066). Overall, no significant time differences were found between stroke and non-stroke dispatches for ambulance dispatch to arterial access (median 208 vs 216 min, p=0.593) or hospital arrival to arterial access (median 42 vs 42 min, p=0.851). However, only 32 patients were treated on the MSU, which commenced operation November 2017. Conclusions: Almost 40% of thrombectomy patients did not receive an initial AMPDS dispatch of suspected stroke and those with higher baseline severity were more likely to be misclassified. Although time to thrombectomy was not significantly different between stroke vs non-stroke dispatches, MSU treatment was under-represented. Our findings have implications for emergency medical services and particularly mobile stroke units which rely on accurate stroke dispatch.

Urban-sub-urban-rural variation in the supply and demand of emergency medical services.

Emergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS supply matches the demand after the 2016 healthcare reform in China. Our objective was to comprehensively investigate EMS supply-demand matching, particularly among urban vs. sub-urban vs. rural areas. Data were extracted from the Tianjin Medical Priority Dispatch System (2017-2021). From supply and demand perspectives, EMS resources and patient characteristics were analyzed. First, we performed a descriptive analysis of characteristics, used Moran's I to explore the spatial layout, and used the Gini coefficient to evaluate the equity of EMS supply and demand. Second, we analyzed urban-sub-urban-rural variation in the characteristics of EMS supply and demand by using the chi-square test. Finally, we examined the association between the EMS health resource density index and the number of patients by using the Spearman correlation and divided supply-demand matching types into four types. In 2021, the numbers of medical emergency stations and ambulances were 1.602 and 3.270 per 100,000 population in Tianjin, respectively. There were gradients in the health resource density index of the number of emergency stations (0.260 vs. 0.059 vs. 0.036; P = 0.000) in urban, sub-urban, and rural areas. There was no spatial autocorrelation among medical emergency stations, of which the G values by population, geographical distribution, and the health resource density index were 0.132, 0.649, and 0.473, respectively. EMS demand was the highest in urban areas, followed by sub-urban and rural areas (24.671 vs. 15.081 vs. 3.210 per 1,000 population and per year; P = 0.000). The EMS supply met the demand in most districts (r = 0.701, P = 0.003). The high supply-high demand types with stationary demand trends were distributed in urban areas; the low supply-high demand types with significant demand growth trends were distributed in sub-urban areas; and the low supply-low demand types with the highest speed of demand growth were distributed in rural areas. EMS supply quantity and quality were promoted, and the supply met the demand after the 2016 healthcare reform in Tianjin. There was urban-sub-urban-rural variation in EMS supply and demand patterns.

Association between initial presenting level of consciousness and patient acuity – A potential application for secondary triage in emergency ambulance calls

IntroductionConscious state assessment is important for the triage of emergency patients. In this study, we measured the association between ambulance patients’ conscious state and high versus lower acuity, with a view to informing telephone triage assessment of conscious state. MethodsData were analysed from one year of emergency ambulance incidents in Perth, Western Australia. Patient conscious state at the time of paramedic arrival was compared to acuity (based on paramedic assessment and management). We determined the proportion of high-acuity patients across six levels of consciousness (Alert, Confused, Drowsy, Voice Response, Pain Response, Unresponsive) overall, and within individual protocols of the Medical Priority Dispatch System (MPDS). ResultsThe proportion of high acuity patients increased with each step across the consciousness scale. Applying conscious state as a binary predictor of acuity, the largest increases occurred moving the threshold from Alert to Confused (22.0–48.6% high acuity) and Drowsy to Voice Response (61.9–89.5% high acuity). The Area Under the Curve (AUC) of the Receiver Operating Characteristic was 0.65. Within individual protocols, the highest AUC was in Cardiac Arrest (0.89), Overdose/Poisoning (0.81), Unknown Problem (0.76), Diabetic Problem, (0.74) and Convulsions/Fitting (0.73); and lowest in Heart problems (0.55), Abdominal Pain (0.55), Breathing Problems (0.55), Back Pain (0.53), and Chest Pain (0.52). ConclusionBased on these proportions of high acuity patients, it is reasonable to consider patients with any altered conscious state a high priority. The value of conscious state assessment for predicting acuity varies markedly between MPDS protocols. These findings could help inform secondary triage of ambulance patients during the emergency call.

Optimizing an Emergency Medical Dispatch System to Improve Prehospital Diagnosis and Treatment of Acute Coronary Syndrome: Nationwide Retrospective Study in China.

Acute coronary syndrome (ACS) is the most time-sensitive acute cardiac event that requires rapid dispatching and response. The medical priority dispatch system (MPDS), one of the most extensively used types of emergency dispatch systems, is hypothesized to provide better-quality prehospital emergency treatment. However, few studies have revealed the impact of MPDS use on the process of ACS care. This study aimed to investigate whether the use of MPDS was associated with higher prehospital diagnosis accuracy and shorter prehospital delay for patients with ACS transferred by an emergency medical service (EMS), using a national database in China. This retrospective analysis was based on an integrated database of China's MPDS and hospital registry. From January 1, 2016, to December 31, 2020, EMS-treated ACS cases were divided into before MPDS and after MPDS groups in accordance with the MPDS launch time at each EMS center. The primary outcomes included diagnosis consistency between hospital admission and discharge, and prehospital delay. Multivariable logistic regression and propensity score-matching analysis were performed to compare outcomes between the 2 groups for total ACS and subtypes. A total of 9806 ACS cases (3561 before MPDS and 6245 after MPDS) treated by 43 EMS centers were included. The overall diagnosis consistency of the after MPDS group (Cohen κ=0.918, P<.001) was higher than that of the before MPDS group (Cohen κ=0.889, P<.001). After the use of the MPDS, the call-to-EMS arrival time was shortened in the matched ACS cases (20.0 vs 16.0 min, P<.001; adjusted difference: -1.67, 95% CI -2.33 to -1.02; P<.001) and in the subtype of ST-elevation myocardial infarction (adjusted difference: -3.81, 95% CI -4.63 to -2.98, P<.001), while the EMS arrival-to-door time (20.0 vs 20.0 min, P=.31) was not significantly different in all ACS cases and subtypes. The optimized use of MPDS in China was associated with increased diagnosis consistency and a reduced call-to-EMS arrival time among EMS-treated patients with ACS. An emergency medical dispatch system should be designed specifically to fit into different prehospital modes in the EMS system on a regional basis.

Performance of the medical priority dispatch system in correctly classifying out-of-hospital cardiac arrests as appropriate for resuscitation

BackgroundEmergency dispatch centres receive emergency calls and assign resources. Out-of-hospital cardiac arrests (OHCA) can be classified as appropriate (requiring emergent response) or inappropriate (requiring non-emergent response) for resuscitation. We sought to determine system accuracy in emergency medical services (EMS) OHCA response allocation. MethodsWe analyzed EMS-assessed non-traumatic OHCA records from the British Columbia (BC) Cardiac Arrest registry (January 1, 2019-June 1, 2021), excluding EMS-witnessed cases. In BC the “Medical Priority Dispatch System” is used. We classified EMS dispatch as “emergent” or “non-emergent” and compared to the gold standard of whether EMS personnel decided treatment was appropriate upon scene arrival. We calculated sensitivity, specificity, and positive and negative predictive values (PPV, NPV), with 95% CI’s. ResultsOf 15,371 non-traumatic OHCAs, the median age was 65 (inter quartile range 51–78), and 4834 (31%) were women; 7152 (47%) were EMS-treated, of whom 651 (9.1%) survived). Among EMS-treated cases 6923/7152 had an emergent response (sensitivity = 97%, 95% CI 96–97) and among EMS-untreated cases 3951/8219 had a non-emergent response (specificity = 48%, 95% CI, 47 to 49). Among cases with emergent dispatch, 6923/11191 were EMS-treated (PPV = 62%, 95% CI 61–62), and among those with non-emergent dispatch, 3951/4180 were EMS-untreated (NPV = 95%, 95% CI 94–95); 229/4180 (5.5%) with a non-emergent dispatch were treated by EMS. ConclusionThe dispatch system in BC has a high sensitivity and moderate specificity in sending the appropriate responses for OHCAs deemed appropriate for treatment by paramedics. Future research may address strategies to increase system specificity, and decrease the incidence of non-emergent dispatch to EMS-treated cases.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

IntroductionDemand for emergency ambulances is increasing, therefore it is important that ambulance dispatch is prioritised appropriately. This means accurately identifying which incidents require a lights and sirens (L&S) response and those that do not. For traffic crashes, it can be difficult to identify the needs of patients based on bystander reports during the emergency phone call; as traffic crashes are complex events, often with multiple patients at the same crash with varying medical needs. This study aims to determine how well the text sent to paramedics en-route to the traffic crash scene by the emergency medical dispatcher (EMD), in combination with dispatch codes, can predict the need for a L&S ambulance response to traffic crashes. Methods: A retrospective cohort study was conducted using data from 2014 to 2016 traffic crashes attended by emergency ambulances in Perth, Western Australia. Machine learning algorithms were used to predict the need for a L&S response or not. The features were the Medical Priority Dispatch System (MPDS) determinant codes and EMD text. EMD text was converted for computation using natural language processing (Bag of Words approach). Machine learning algorithms were used to predict the need for a L&S response, defined as where one or more patients (a) died before hospital admission, (b) received L&S transport to hospital, or (c) had one or more high-acuity indicators (based on an a priori list of medications, interventions or observations. Results: There were 11,971 traffic crashes attended by ambulances during the study period, of which 22.3 % were retrospectively determined to have required a L&S response. The model with the highest accuracy was using an Ensemble machine learning algorithm with a score of 0.980 (95 % CI 0.976–0.984). This model predicted the need for an L&S response using both MPDS determinant codes and EMD text. Discussion: We found that a combination of EMD text and MPDS determinate codes can predict which traffic crashes do and do not require a lights and sirens ambulance response to the scene with a high degree of accuracy. Emergency medical services could deploy machine learning algorithms to improve the accuracy of dispatch to traffic crashes, which has the potential to result in improved system efficiency.

A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates.

BackgroundCalls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response. In this context, over triage is defined as dispatching an ambulance with lights and sirens (L&S) to a low acuity MVC and under triage is not dispatching an ambulance with L&S to those who require urgent medical care. We explored the potential for crash characteristics to be used during emergency ambulance calls to identify those MVCs that required a L&S response.MethodsWe conducted a retrospective cohort study using ambulance and police data from 2014 to 2016. The predictor variables were crash characteristics (e.g. road surface), and Medical Priority Dispatch System (MPDS) dispatch codes. The outcome variable was the need for a L&S ambulance response. A Chi-square Automatic Interaction Detector technique was used to develop decision trees, with over/under triage rates determined for each tree. The model with an under/over triage rate closest to that prescribed by the American College of Surgeons Committee on Trauma (ACS COT) will be deemed to be the best model (under triage rate of ≤ 5% and over triage rate of between 25–35%.ResultsThe decision tree with a 2.7% under triage rate was closest to that specified by the ACS COT, had as predictors—MPDS codes, trapped, vulnerable road user, anyone aged 75 + , day of the week, single versus multiple vehicles, airbag deployment, atmosphere, surface, lighting and accident type. This model had an over triage rate of 84.8%.ConclusionsWe were able to derive a model with a reasonable under triage rate, however this model also had a high over triage rate. Individual EMS may apply the findings here to their own jurisdictions when dispatching to the scene of a MVC.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12873-022-00609-5.

Epidemiology of emergency ambulance service calls related to COVID-19 in Scotland: a national record linkage study

BackgroundCOVID-19 has overwhelmed health services across the world; its global death toll has exceeded 5.3 million and continues to grow. There have been almost 15 million cases of COVID-19 in the UK. The need for rapid accurate identification, appropriate clinical care and decision making, remains a priority for UK ambulance service. To support identification and conveyance decisions of patients presenting with COVID-19 symptoms the Scottish Ambulance Service introduced the revised Medical Priority Dispatch System Protocol 36, enhanced physician led decision support and prehospital clinical guidelines. This study aimed to characterise the impact of these changes on the pathways and outcomes of people attended by the SAS) with potential COVID-19.MethodsA retrospective record linkage cohort study using National Data collected from NHS Scotland over a 5 month period (April–August 2020).ResultsThe SAS responded to 214,082 emergency calls during the study time period. The positive predictive value of the Protocol 36 to identify potentially COVID-19 positive patients was low (17%). Approximately 60% of those identified by Protocol 36 as potentially COVID-19 positive were conveyed. The relationship between conveyance and mortality differed between Protocol 36 Covid-19 positive calls and those that were not. In those identified by Protocol 36 as Covid-19 negative, 30 day mortality was higher in those not conveyed (not conveyed 9.2%; conveyed 6.6%) but in the Protocol 36 Covid-19 positive calls, mortality was higher in those conveyed (not conveyed 4.3% conveyed 8.8%). Thirty-day mortality rates of those with COVID-19 diagnosed through virology was between 28.8 and 30.2%.ConclusionThe low positive predictive value (17%) of Protocol 36 in identifying potential COVID-19 in patients emphasises the importance of ambulance clinicians approaching each call as involving COVID-19, reinforcing the importance of adhering to existing policy and continued use of PPE at all calls. The non-conveyance rate of people that were categorised as COVID-19 negative was higher than in the preceding year in the same service. The reasons for the higher rates of non-conveyance and the relationship between non conveyance rates and death at 3 and 30 days post index call are unknown and would benefit from further study.

Abstract 13202: The Accuracy Of EMS Dispatch For Stroke Following The Introduction Of The Stroke Diagnostic Tool

Introduction: Early identification of stroke in the emergency call is key to timely reperfusion therapy. The Medical Priority Dispatch System (MPDS) is widely used and recent versions include a Stroke Diagnostic Tool (SDxT) using the FAST (Face, Arm, Speech) assessments. We conducted a retrospective observational study to examine whether introduction of the SDxT improved the identification of stroke/TIA in the emergency call in Victoria, Australia. Methods: We included all stroke dispatches (MPDS Code 28) for adults (aged ≥20 years) between 2008 and 2015. We linked this data to diagnoses from Ambulance Victoria, and the Victorian Department of Health and Human Services’ databases: the Victorian Emergency Minimum Dataset and the Victorian Admitted Episode Database. We examined the use of SDxT codes, and compared the proportion of stroke/TIA diagnoses before and after the introduction of the SDxT in September 2013. Results: Over the study period there were 108,917 Code 28 cases (average 13,600/year), 80% (n=86,906) were transported to an ED and 55% (n=60,334) were admitted to hospital. There were 3,141 diagnoses among the Code 28 cases, with 30% diagnosed with stroke/TIA. No changes was seen in the proportion of stroke/TIAs after the introduction of the SDxT (28.5% vs. 28.2%, p=0.23). The SDxT was used and complete in 63% (n=20,070) of calls. Patients with evidence of stroke on the SDxT were more likely to have stroke/TIA (partial evidence=29%, strong evidence=38% and clear evidence=40%) compared to those with no evidence (20%) and those with no/incomplete SDT (25%). Conclusions: Our results suggest a large proportion of stroke dispatches are not stroke/TIA, but evidence of stroke on the SDxT does identify marginally higher proportions. Further research is required to understand use of the tool, and public education on stroke assessments may be needed to realise its full potential.

Comparison of Emergency Medical Dispatch Systems for Performance of Telecommunicator-Assisted Cardiopulmonary Resuscitation Among 9-1-1 Callers With Limited English Proficiency

Increasing bystander cardiopulmonary resuscitation (CPR) among racial/ethnic minority groups and culturally underserved populations is a key strategy in improving health care disparities in out-of-hospital cardiac arrest. To ascertain whether implementation of the Los Angeles Tiered Dispatch System (LA-TDS) was associated with improved performance of telecommunicator-assisted CPR (T-CPR) among 9-1-1 callers with limited English proficiency in the City of Los Angeles. This cohort study compared emergency medical services-treated, nontraumatic out-of-hospital cardiac arrest calls using the Medical Priority Dispatch System (MPDS) from January 1 to March 31, 2014, with calls using LA-TDS from January 1 to March 31, 2015. Trained data abstractors evaluated all 9-1-1 audio recordings for the initiation of T-CPR and the elapsed time to predefined events. Data were analyzed between January and December 2017. The primary outcome was the prevalence of T-CPR among 9-1-1 callers with limited English proficiency for field-confirmed nontraumatic cardiac arrests. Additional outcomes included T-CPR among callers with English proficiency and the elapsed time until key events in the call. Of the 1027 emergency medical services calls during the study periods, 597 met the inclusion criteria. A total of 289 calls (48%) were made using MPDS (263 callers with English proficiency, and 26 callers with limited English proficiency), and 308 calls (52%) were made using LA-TDS (273 callers with English proficiency, and 35 callers with limited English proficiency). No differences between MPDS and LA-TDS cohorts were found in age, sex, known comorbidities, arrest location (private vs public), or witnessed status. The prevalence of T-CPR among callers with limited English proficiency was significantly greater using LA-TDS (69%) vs MPDS (28%) (odds ratio [OR], 5.66; 95% CI, 1.79-17.85; P = .003). For callers with English proficiency, the prevalence of T-CPR improved from 55% using MPDS to 67% using LA-TDS (OR, 1.66; 95% CI, 1.15-2.41; P = .007). With LA-TDS, callers with limited English proficiency had a significant decrease in time to recognition of cardiac arrest (OR, 0.59; 95% CI, 0.41-0.84; P = .005) and dispatch of resources (OR, 0.71; 95% CI, 0.54-0.94; P = .02). The LA-TDS compared with MPDS was associated with increased performance of T-CPR for out-of-hospital cardiac arrests involving 9-1-1 callers with limited English proficiency. Further studies are needed in communities with a predominance of people with limited English proficiency to characterize bystander response, promote activation of the chain of survival, and clarify the precise elements of LA-TDS that can improve T-CPR performance.

Prioritising the mobilisation of emergency medical services: patient making at the healthcare gateway

PurposeThe purpose of this paper is to analyse the socio-material practices through which organisational understanding of patients is accomplished in order to prioritise calls and mobilise emergency medical services at the gateway of the healthcare system.Design/methodology/approachThe methodology of this paper is an ethnographic study of the co-ordination of collective action in an emergency services control room in the Welsh NHS, with data generation and analysis informed by Translational Mobilisation Theory.FindingsMobilisation of emergency medical services entails the translation of callers' undifferentiated problems into response priority categories, which are used by dispatch operators to mobilise crews. A central actor in these processes is the computerised Medical Priority Dispatch System. While designed to enable non-clinically qualified call handlers to triage calls in a standardised way, the system constrains caller–call handler interaction, which negatively impacts the categorisation process. Analysis of these interactional difficulties and associated mitigation strategies highlights opportunities for intervening to support co-ordination at this healthcare boundary.Originality/valueOrthodox approaches to improving interface management are founded on a conceptualisation of “patients” as immutable actors in care transfer processes. Translational Mobilisation Theory brings into view the multiple versions of the “patient” produced by healthcare systems and offers a framework for analysing the mechanisms of action necessary to create organisational understandings of patients at boundary crossings. While the ambulance control centre is a singular case, the paper illustrates the value of attending to these processes in interface organisation.

‘Is the Patient Completely Alert?’ – Accuracy of Emergency Medical Dispatcher Determination of Patient Conscious State

Introduction During emergency ambulance calls, one of the key issues assessed is the patient's level of consciousness. An altered conscious state can be indicative of a need for a high priority response; however, the reliability of the resulting triage depends on how accurately alertness can be ascertained over the phone. This study investigated the accuracy of emergency medical dispatcher (EMD) determination of conscious state in emergency ambulance calls in Perth, Western Australia. Methods The study compared EMD determination of patient alertness based on the Medical Priority Dispatch System (MPDS), with conscious state as recorded by paramedics on arrival, for all emergency ambulance calls in a 1-year period in metropolitan Perth. Diagnostic accuracy was reported across the whole system and stratified by MPDS chief complaint. Results There were 109,678 calls included for analysis. In terms of identifying patients as not alert, the overall positive predictive value was 6.62% and negative predictive value was 99.93%, with 10 times as many patients dispatched as not alert than found to be not alert at scene. Sensitivity was only 69.94%. There was significant variation in accuracy between chief complaints. Conclusion The study found high levels of inaccuracy between dispatch identification of not-alert patients, and what paramedics found on scene. While not-alert dispatch was 10 times more common than patients being determined not-alert on scene, only 70% of not-alert patients on scene were classified as such during dispatch. Further research is suggested into the factors that affect the accuracy of EMD determination of patient conscious state.

Implementation of the Los Angeles Tiered Dispatch System is associated with an increase in telecommunicator-assisted CPR

BackgroundIn 2015, the Los Angeles Fire Department (LAFD) replaced the Medical Priority Dispatch System® (MPDS) with a new, homegrown Los Angeles Tiered Dispatch System (LA-TDS). The objective of this study was to assess the impact of this new dispatch system on telecommunicator-assisted CPR (T-CPR). MethodsThis was an interrupted time series study of out-of-hospital cardiac arrest (OHCA) cases where resuscitation was attempted with LAFD 9-1-1 telecommunicators using either MPDS® (January 1, 2014 to March 31, 2014) or LA-TDS (January 1, 2015 to March 31, 2015). Trained non-LAFD abstractors listened to all 9-1-1 calls and recorded if T-CPR was initiated, and the elapsed time from the start of the call until key events. The primary outcome was prevalence of T-CPR. ResultsOf 1027 calls during the study period, 597 9-1-1 calls met study inclusion/exclusion criteria (including 289 in MPDS cohort, 308 in LA-TDS cohort). The prevalence of T-CPR was significantly greater using LA-TDS (57%) vs. MPDS (43%) (OR 1.86, 95% CI 1.3–2.6). The LA-TDS cohort demonstrated a significant decrease (p < 0.001) in time to recognition of cardiac arrest, time to dispatch, and time to first T-CPR chest compression while there was no significant difference in the elapsed time to first description of agonal breathing. For cases where the telecommunicator had the opportunity to assess consciousness and breathing, there was a significant improvement in cardiac arrest recognition in < 1 min, prevalence of T-CPR (p < 0.001), and T-CPR started in <2 min (p < 0.001). ConclusionImplementation of the new Los Angeles Tiered Dispatch System was associated with decreased time to recognition of cardiac arrest and an increased rate of T-CPR compared to the previously used Medical Priority Dispatch System®.

Enhancing predictions of patient conveyance using emergency call handler free text notes for unconscious and fainting incidents reported to the London Ambulance Service

ObjectivePre-hospital emergency medical services use clinical decision support systems (CDSS) to triage calls. Call handlers often supplement this by making free text notes covering key incident information. We investigate whether machine learning approaches using features from such free text notes can improve prediction of unconscious patients who require conveyance. Materials and methodsWe analysed a subset of all London Ambulance Service calls that were triaged through the Medical Priority Dispatch System (MPDS) as involving an unconscious or fainting patient in 2018. We use and compare two machine learning algorithms: random forest (RF) and gradient boosting machine (GBM). For each incident, we predict whether the patient will be conveyed to a hospital emergency department or equivalent using as features 1) the MPDS code, 2) the free text notes and 3) the two together. We evaluate model performance using the area under the curve (AUC) metric. Given the imbalance of outcomes (patient conveyed 71 %, not conveyed 29 %), we also consider sensitivity and specificity. ResultsUsing only the MPDS code resulted in an AUC of 0.57. Using the text notes gave an improved AUC score of 0.63 and combining the two gave an AUC score of 0.64 (scores were similar for RF and GBM). GBM models scored better on sensitivity (0.93 vs 0.62 for RF in the combined model), but specificity was lower (0.17 vs. 0.56 for RF in the combined model). ConclusionsUsing information contained in the free text notes made by call handlers in combination with MPDS improves prediction of unconscious and fainting patients requiring conveyance to a hospital emergency department (or equivalent) when compared with machine learning models using MPDS codes only. This suggests there is some useful information in unstructured data captured by emergency call handlers that complements MPDS codes. Quantifying this gain can help inform emergency medical service policy when evaluating the decision to expand or augment existing CDSS.