Emergency medical dispatch codes association with emergency department outcomes

Novel Methodology for Linking 9-1-1 Dispatch Categories with a Death Registry: Mortality Rates of Selected Dispatch Categories

OBJECTIVES

Medical Priority Dispatch System (MPDS) is a system used to assign medical 9-1-1 calls to one of 35 chief complaints that are further categorized in order of increasing priority, Alpha through Echo. In this descriptive study we demonstrate the methodology of matching MPDS codes to a county mortality registry. We also evaluated the ability of select MPDS codes (fall, respiratory, sick person, and abdominal pain) to predict mortality up to 30 d for all ages transported by Alameda County Emergency Medical Services (EMS).

METHODS

Using Alameda County EMS data, we conducted a retrospective review of all EMS encounters that occurred from November 1, 2011, to November 1, 2016. To describe mortality in this population, we identified unique patients and linked them to the Alameda County Public Health Death Registry. We identified mortality at 48 h, 7 d, and 30 d after an EMS encounter.

RESULTS

Approximately 99% of the EMS encounters were matched with unique patient identifiers, yielding a study sample of 202,431 (4% less than age 18, 53% between ages 18-65, and 43% over age 65). Patients with a respiratory chief complaint had the highest mortality percentage in each age group (0.23%, 2.7%, and 14.55% respectively). There was no correlation between the MPDS code and mortality for patients less than age 18. An increase in Alpha through Echo designation for respiratory complaints in patients 18-65 and older than 65 years corresponded with an increase in 30-day mortality.

CONCLUSIONS

This study demonstrates an upward trend in mortality with increasing acuity of Alpha through Echo designations for adult patients with respiratory complaints. Mortality increased with age in this cohort. Most of the deaths occurred after 7 days.

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.

Increasing emergency number utilisation is not driven by low-acuity calls: an observational study of 1.5 million emergency calls (2018-2021) from Berlin

BACKGROUND

The Emergency Medical Service (EMS) in Germany is increasingly challenged by strongly rising demand. Speculations about a greater utilisation for minor cases have led to intensive media coverage, but empirical evidence is lacking. We investigated the development of low-acuity calls from 2018 to 2021 in the federal state of Berlin and its correlations with sociodemographic characteristics.

METHODS

We analysed over 1.5 million call documentations including medical dispatch codes, age, location and time using descriptive and inferential statistics and multivariate binary logistic regression. We defined a code list to classify low-acuity calls and merged the dataset with sociodemographic indicators and data on population density.

RESULTS

The number of emergency calls (phone number 112 in Germany) increased by 9.1% from 2018 to 2021; however, the proportion of low-acuity calls did not increase. The regression model shows higher odds of low-acuity for young to medium age groups (especially for age 0-9, OR 1.50 [95% CI 1.45-1.55]; age 10-19, OR 1.77 [95% CI 1.71-1.83]; age 20-29, OR 1.64 [95% CI 1.59-1.68] and age 30-39, OR 1.40 [95% CI 1.37-1.44]; p < 0.001, reference group 80-89) and for females (OR 1.12 [95% CI 1.1-1.13], p < 0.001). Odds were slightly higher for calls from a neighbourhood with lower social status (OR 1.01 per index unit increase [95% CI 1.0-1.01], p < 0.05) and at the weekend (OR 1.02 [95% CI 1.0-1.04, p < 0.05]). No significant association of the call volume with population density was detected.

CONCLUSIONS

This analysis provides valuable new insights into pre-hospital emergency care. Low-acuity calls were not the primary driver of increased EMS utilisation in Berlin. Younger age is the strongest predictor for low-acuity calls in the model. The association with female gender is significant, while socially deprived neighbourhoods play a minor role. No statistically significant differences in call volume between densely and less densely populated regions were detected. The results can inform the EMS in future resource planning.

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

BACKGROUND

Emergency 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.

METHODS

We 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.

RESULTS

Of 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.

CONCLUSION

The 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.

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

Background

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.

Objective

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.

Methods

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.

Results

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.

Conclusions

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.

Improving ambulance dispatch triage to trauma: A scoping review using the framework of development and evaluation of clinical prediction rules

OBJECTIVE

Ambulance dispatch algorithms should function as clinical prediction rules, identifying high acuity patients for advanced life support, and low acuity patients for non-urgent transport. Systematic reviews of dispatch algorithms are rare and focus on study types specific to the final phases of rule development, such as impact studies, and may miss the complete value-added evidence chain. We sought to summarise the literature for studies seeking to improve dispatch in trauma by performing a scoping review according to standard frameworks for developing and evaluating clinical prediction rules.

METHODS

We performed a scoping review searching MEDLINE, EMBASE, CINAHL, the CENTRAL trials registry, and grey literature from January 2005 to October 2021. We included all study types investigating dispatch triage to injured patients in the English language. We reported the clinical prediction rule phase (derivation, validation, impact analysis, or user acceptance) and the performance and outcomes measured for high and low acuity trauma patients.

RESULTS

Of 2067 papers screened, we identified 12 low and 30 high acuity studies. Derivation studies were most common (52%) and rule-based computer-aided dispatch was the most frequently investigated (23 studies). Impact studies rarely reported a prior validation phase, and few validation studies had their impact investigated. Common outcome measures in each phase were infrequent (0 to 27%), making a comparison between protocols difficult. A series of papers for low acuity patients and another for pediatric trauma followed clinical prediction rule development. Some low acuity Medical Priority Dispatch System codes are associated with the infrequent requirement for advanced life support and clinician review of computer-aided dispatch may enhance dispatch triage accuracy in studies of helicopter emergency medical services.

CONCLUSIONS

Few derivation and validation studies were followed by an impact study, indicating important gaps in the value-added evidence chain. While impact studies suggest clinician oversight may enhance dispatch, the opportunity exists to standardize outcomes, identify trauma-specific low acuity codes, and develop intelligent dispatch systems.

Linking Emergency Medical Services and Emergency Department Data to Improve Overdose Surveillance in North Carolina

INTRODUCTION

Linking emergency medical services (EMS) data to emergency department (ED) data enables assessing the continuum of care and evaluating patient outcomes. We developed novel methods to enhance linkage performance and analysis of EMS and ED data for opioid overdose surveillance in North Carolina.

METHODS

We identified data on all EMS encounters in North Carolina during January 1-November 30, 2017, with documented naloxone administration and transportation to the ED. We linked these data with ED visit data in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. We manually reviewed a subset of data from 12 counties to create a gold standard that informed developing iterative linkage methods using demographic, time, and destination variables. We calculated the proportion of suspected opioid overdose EMS cases that received International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for opioid overdose in the ED.

RESULTS

We identified 12 088 EMS encounters of patients treated with naloxone and transported to the ED. The 12-county subset included 1781 linkage-eligible EMS encounters, with historical linkage of 65.4% (1165 of 1781) and 1.6% false linkages. Through iterative linkage methods, performance improved to 91.0% (1620 of 1781) with 0.1% false linkages. Among statewide EMS encounters with naloxone administration, the linkage improved from 47.1% to 91.1%. We found diagnosis codes for opioid overdose in the ED among 27.2% of statewide linked records.

PRACTICE IMPLICATIONS

Through an iterative linkage approach, EMS-ED data linkage performance improved greatly while reducing the number of false linkages. Improved EMS-ED data linkage quality can enhance surveillance activities, inform emergency response practices, and improve quality of care through evaluating initial patient presentations, field interventions, and ultimate diagnoses.

Medical dispatchers' perception of the interaction with the caller during emergency calls - a qualitative study

BACKGROUND

Medical dispatching is a highly complex procedure and has an impact upon patient outcome. It includes call-taking and triage, prioritization of resources and the provision of guidance and instructions to callers. Whilst emergency medical dispatchers play a key role in the process, their perception of the process is rarely reported. We explored medical dispatchers' perception of the interaction with the caller during emergency calls. Secondly, we aimed to develop a model for emergency call handling based on these findings.

METHODS

To provide an in-depth understanding of the dispatching process, an explorative qualitative interview study was designed. A grounded theory design and thematic analysis were applied.

RESULTS

A total of 5 paramedics and 6 registered nurses were interviewed. The emerging themes derived from dispatchers' perception of the emergency call process were related to both the callers and the medical dispatchers themselves, from which four and three themes were identified, respectively. Dispatchers reported that for callers, the motive for calling, the situation, the perception and presentation of the problem was influencing factors. For the dispatchers the expertise, teamwork and organization influenced the process. Based on the medical dispatchers´ perception, a model of the workflow and interaction between the caller and the dispatcher was developed based on themes related to the caller and the dispatcher.

CONCLUSIONS

According to medical dispatchers, the callers seem to lack knowledge about best utilization of the emergency number and the medical dispatching process, which can be improved by public awareness campaigns and incorporating information into first aid courses. For medical dispatchers the most potent modifiable factors were based upon the continuous professional development of the medical dispatchers and the system that supports them. The model of call handling underlines the complexity of medical dispatching that embraces the context of the call beyond clinical presentation of the problem.

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

OBJECTIVE

Pre-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 METHODS

We 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.

RESULTS

Using 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).

CONCLUSIONS

Using 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.

Short-Term Outcome of Transported Versus Not-Transported Seizure Patients by Tehran Emergency Medical Services; a Retrospective Cohort Study

Background: One of the most frequent complaints that emergency medical services (EMS) deal with is seizure. The missions of EMS on these cases may lead to transferring the patient to the emergency department (ED). Objectives: Therefore, the present study was conducted to compare the short-term outcome of transported versus not-transported cases. Methods: Our population sample was selected retrospectively from medical records in Tehran EMS center for 6 months in which the plan was transferring to a specific hospital. The cases were divided into transported or not-transported. W extracted and compared the cases’ demographic data, vital signs, conducted prehospital management, patient disposition, and their short-term outcome. Results: We evaluated 486 cases, 173 of whom were males (35.6%) and the mean age of patients was 34.8 ± 32.0 years old. 329 (67.7%) and 157 (32.3%) were in the not-transported and the transported group, respectively. Among all not-transported cases, we could follow 172 patients, 52 of whom had gone to hospital during the next 72 hours. 22 out of the 52 cases were discharged from ED the same day. Overall, the ED discharge rate was significantly more in the transported group (P < 0.001). Short-term mortality had no significant difference in the two study groups (P = 1.00). Conclusions: Most of the not-transported patients did not go to the hospital during further 72 hours, and some were hospitalized. Most of the patients transported to the hospital were discharged from the ED. The short-term mortality rate was not different.

Population-based analysis of the effect of a comprehensive, systematic change in an emergency medical services resource allocation plan on 24-hour mortality

BACKGROUND

Resource allocation planning for emergency medical services (EMS) systems determines appropriate resources including what paramedic qualification and how rapidly to respond to patients for optimal outcomes. The British Columbia Emergency Health Services implemented a revised response plan in 2013.

METHODS

A pre- and post-methodology was used to evaluate the effect of the resource allocation plan revision on 24-hour mortality. All adult cases with evaluable outcome data (obtained through linked provincial health administrative data) were analyzed. Multivariable logistic regression was used to adjust for variations in other significant associated factors. Interrupted time series analysis was used to estimate immediate changes in level or trend of outcome after the start of the revised resource allocation plan implementation, while simultaneously controlling for pre-existing trends.

RESULTS

The derived cohort comprised 562,546 cases (April 2012-March 2015). When adjusted for age, sex, urban/metro region, season, day, hour, and dispatch determinant, the probability of dying within 24 hours of an EMS call was 7% lower in the post-resource allocation plan-revision cohort (OR = 0.936; 95% CI: 0.886-0.989; p = 0.018). A subgroup analysis of immediately life-threatening cases demonstrated similar effect (OR = 0.890; 95% CI: 0.808-0.981; p = 0.019). Using time series analysis, the descending changes in overall 24-hour mortality trend and the 24-hour mortality trend in immediately life-threatening cases, were both statistically significant (p < 0.001).

CONCLUSION

Comprehensive, evidence-informed reconstruction of a provincial EMS resource allocation plan is feasible. Despite change in crew level response and resource allocation, there was significant decrease in 24-hour mortality in this pan-provincial population-based cohort.

Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department

OBJECTIVE

Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success. In this study, we use supervised machine learning to derive and validate an automated record linkage algorithm between EMS ePCRs and ED records.

MATERIALS AND METHODS

All consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number, and date of birth were extracted. Data were randomly split into 80% training and 20% test datasets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5-fold cross-validation, using label k-fold, L2 regularization, and class reweighting.

RESULTS

A total of 14 032 ePCRs were included in the study. Interrater reliability between the primary and secondary reviewer had a kappa of 0.9. The algorithm had a sensitivity of 99.4%, a positive predictive value of 99.9%, and an area under the receiver-operating characteristic curve of 0.99 in both the training and test datasets. Date-of-birth match had the highest odds ratio of 16.9, followed by last name match (10.6). Social security number match had an odds ratio of 3.8.

CONCLUSIONS

We were able to successfully derive and validate a record linkage algorithm from a single EMS ePCR provider to our hospital EMR.

A validation of machine learning-based risk scores in the prehospital setting

BACKGROUND

The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

METHODS

Dispatch, ambulance, and hospital data were collected in one Swedish region from 2016-2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.

RESULTS

A total of 38203 patients were included from 2016-2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51-0.66, while those for NEWS ranged from 0.66-0.85. Concordance ranged from 0.70-0.79 for risk scores based only on dispatch data, and 0.79-0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.

CONCLUSIONS

Machine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.

Differences between cases admitted to hospital and discharged from the emergency department after emergency medical services transport

OBJECTIVE

Rising emergency medical services (EMS) utilisation increases transport to hospital emergency departments (ED). However, some patients receive outpatient treatment (discharged) while others are hospitalised (admitted). The aims of this analysis were to compare admitted and discharged cases, to assess whether cases that were discharged from the ED could be identified using dispatch data and to compare dispatch keyword categories and hospital diagnoses.

DESIGN

Retrospective observational study using linked secondary data.

SETTING AND PARTICIPANTS

78 303 cases brought to 1 of 14 ED in the city of Munich, Germany, by EMS between 1 July 2013 and 30 June 2014.

MAIN OUTCOME MEASURES

Characteristics of admitted and discharged cases were assessed. Logistic regression was used to estimate the association between discharge and age, sex, time of day, ambulance type and dispatch keyword category. Keyword categories were compared to hospital diagnoses.

RESULTS

39.4% of cases were discharged. They were especially likely to be young (OR 10.53 (CI 9.31 to 11.92), comparing <15-year-olds to >70-year-olds) and to fall under the categories 'accidents/trauma' (OR 2.87 (CI 2.74 to 3.01)) or 'other emergencies (unspecified)' (OR 1.23 (CI 1.12 to 1.34) (compared with 'cardiovascular'). Most frequent diagnoses came from chapter 'injury and poisoning' (30.1%) of the 10th revision of the international statistical classification of disease and related health problems (ICD-10), yet these diagnoses were more frequent at discharge (42.7 vs 22.0%) whereas circulatory system disease was less frequent (2.6 vs 21.8%). Except for accidents/trauma and intoxication/poisoning many underlying diagnoses were observed for the same dispatch keyword.

CONCLUSION

Young age and dispatch for accidents or trauma were the strongest predictors of discharge. Even within the same dispatch keyword category the distribution of diagnoses differed between admitted and discharged cases. Discharge from the ED does not indicate that urgent response was unnecessary. However, these cases could be suitable for allocation to hospitals with low inpatient bed capacities and are of particular interest for future studies regarding the urgency of their condition.

Feasibility of data linkage in the PARAMEDIC trial: a cluster randomised trial of mechanical chest compression in out-of-hospital cardiac arrest

OBJECTIVES

There is considerable interest in reducing the cost of clinical trials. Linkage of trial data to administrative datasets and disease-specific registries may improve trial efficiency, but it has not been reported in resuscitation trials conducted in the UK. To assess the feasibility of using national administrative and clinical datasets to follow up patients transported to hospital following attempted resuscitation in a cluster randomised trial of a mechanical chest compression device in out-of-hospital cardiac arrest.

METHODS

Hospital data on trial participants were requested from Hospital Episode Statistics (HES), the Intensive Care National Audit and Research Centre, and Myocardial Ischaemia National Audit Project and National Audit of Percutaneous Coronary Interventions, using unique patient identifiers. Linked data were received between June 2014 and June 2015.

RESULTS

Of 4471 patients randomised in the pre-hospital randomised assessment of a mechanical compression device in cardiac arrest (PARAMEDIC) trial, 2398 (53.6%) were not known to be deceased at emergency department arrival and were eligible for linkage. We achieved an overall match rate of 86.7% in the combined HES accident and emergency, inpatient and critical care dataset, with variable match rates (4.2%-80.4%) in individual datasets. Patient demographics, cardiac arrest-related characteristics and major outcomes were predominantly similar between HES matched and unmatched groups, in the linkage apart from location, response time and return of spontaneous circulation (ROSC) at handover.

CONCLUSIONS

This study shows that it is feasible to track patients from the prehospital setting through to hospital admission using routinely available administrative datasets with a moderate to high degree of success. This approach has the potential to complement the trial data with the demographic and clinical management information about the studied cohort, as well as to improve the efficiency and reduce the costs of follow-up in cardiac arrest trials.

CLINICAL TRIAL REGISTRATION

ISRCTN08233942; Post-results.

Ambulance dispatch versus general practitioner home visit for highly urgent out-of-hours primary care

BACKGROUND

Patients with life-threatening conditions who contact out-of-hours primary care either receive a home visit from a GP of a GP cooperative (GPC) or are handed over to the ambulance service.

OBJECTIVE

The objective of this study was to determine whether highly urgent visits, after a call to the GPC, are delivered by the most appropriate healthcare provider: GPC or ambulance service.

METHODS

We performed a cross-sectional study using patient record data from a GPC and ambulance service in an urban district in The Netherlands. During a 21-month period, all calls triaged as life-threatening (U1) to the GPCs were included. The decision to send an ambulance or not was made by the triage nurse following a protocolized triage process. Retrospectively, the most appropriate care was judged by the patient's own GP, using a questionnaire.

RESULTS

Patient and care characteristics from 1081 patients were gathered: 401 GPC visits, 570 ambulance responses and 110 with both ambulance and GPC deployment. In 598 of 1081 (55.3%) cases, questionnaires were returned by the patients' own GP. About 40% of all visits could have been carried out with a lower urgency in retrospect, and almost half of all visits should have received a different type of care or different provider. In case of ambulance response, 60.7% concerned chest pain.

CONCLUSION

Research should be done on the process of triage and allocation of care to optimize labelling complaints with the appropriate urgency and to deploy the appropriate healthcare provider, especially for patients with chest pain.

A high proportion of prehospital emergency patients are not transported by ambulance: a retrospective cohort study in Northern Finland

BACKGROUND

The number of missions in the emergency medical services (EMS) has increased considerably in recent years. People are requesting ambulance for even minor illnesses and non-medical problems, which is placing financial and resource burdens on the EMS. The aim of this study was to determine the rate of non-transportation missions in Northern Finland and the reasons for these missions.

METHODS

All ambulance missions in two hospital districts in Northern Finland during the 6-month period of January 1 through June 30, 2014, were retrospectively evaluated from the EMS charts to identify missions in which the patients were not transported by the EMS. The non-transportation rates and reasons were calculated and expressed as percentages.

RESULTS

In 41.7% of the 13,354 missions, the patient was not transported from the scene by an ambulance. After a medical assessment and care was provided by the EMS, 48.2% of these non-transport patients were evaluated as not needing further treatment in the emergency department and were directed to contact the municipal health care center during office hours. There was no need for any medical care in 39.9% of non-transportation missions.

CONCLUSION

This study showed a high rate of EMS missions resulting in non-transportation in two hospital districts in Northern Finland. In the majority of these missions there was no need for emergency admission to an emergency department or for any medical care at all. These findings indicate that an improvement in the dispatch process and primary care resources might be of benefit.

Merger of two dispatch centres: does it improve quality and patient safety?

Background

Dispatch centres (DCs) are considered an essential but expensive component of many highly developed healthcare systems. The number of DCs in a country, region, or state is usually based on local history and often related to highly decentralised healthcare systems. Today, current technology (Global Positioning System or Internet access) abolishes the need for closeness between DCs and the population. Switzerland went from 22 DCs in 2006 to 17 today. This study describes from a quality and patient safety point of view the merger of two DCs.

Methods

The study analysed the performance (over and under-triage) of two medical DCs for 12 months prior to merging and for 12 months again after the merger in 2015. Performance was measured comparing the priority level chosen by dispatcher and the severity of cases assessed by paramedics on site using the National Advisory Committee for Aeronautics (NACA) score. We ruled that NACA score > 3 (injuries/diseases which can possibly lead to deterioration of vital signs) to 7 (lethal injuries/diseases) should require a priority dispatch with lights and siren (L&S). While NACA score < 4 should require a priority dispatch without L&S. Over-triage was defined as the proportion of L&S dispatches with a NACA score < 4, and under-triage as the proportion of dispatches without L&S with a NACA > 3.

Results

Prior to merging, Dispatch A had a sensitivity/specificity regarding the use of lights and sirens and severity of cases of 86%/48% with over- and under-triage rates of 78% and 5%, respectively. Dispatch B had sensitivity and specificity of 92%/20% and over- and under-triage rates of 84% and 7%, respectively. After they merged, global sensitivity/specificity reached 87%/67%, and over- and under-triage rates were 71% and 3%, respectively

Conclusions

A part the potential cost advantage achieved by the merger of two DCs, it can improve the quality of services to the population, reducing over- and under-triage and the use of lights and sirens and therefore, the risk of accidents. This is especially the case when a DC with poor triage performance merges with a high-performing DC.

The difficult medical emergency call: A register-based study of predictors and outcomes

BACKGROUND

Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories and the effect of categorization on mortality.

METHODS

Register-based study in a 2-year period based on emergency call data from the emergency medical dispatch center in Copenhagen combined with nationwide register data. Logistic regression analysis (N = 78,040 individuals) was used for identification of predictors of emergency call categorization as "unclear problem". Poisson regression analysis (N = 97,293 calls) was used for examining the effect of categorization as "unclear problem" on mortality.

RESULTS

"Unclear problem" was the registered category in 18% of calls. Significant predictors for "unclear problem" categorization were: age (odds ratio (OR) 1.34 for age group 76+ versus 18-30 years), ethnicity (OR 1.27 for non-Danish vs. Danish), day of week (OR 0.92 for weekend vs. weekday), and time of day (OR 0.79 for night vs. day). Emergency call categorization had no effect on mortality for emergency priority level A calls, incidence rate ratio (IRR) 0.99 (95% confidence interval (CI) 0.90-1.09). For emergency priority level B calls, an association was observed, IRR 1.26 (95% CI 1.18-1.36).

DISCUSSIONS

The results shed light on the complexity of emergency call handling, but also implicate a need for further improvement. Educational interventions at the dispatch centers may improve the call handling, but also the underlying supportive tools are modifiable. The higher mortality rate for patients with emergency priority level B calls with "unclear problem categorization" could imply lowering the threshold for dispatching a high level ambulance response when the call is considered unclear. On the other hand a "benefit of the doubt" approach could hinder the adequate response to other patients in need for an ambulance as there is an increasing demand and limited resources for ambulance services.

CONCLUSIONS

Age, ethnicity, day of week and time of day were significant predictors of emergency call categorization as "unclear problem". "Unclear problem" categorization was not associated with mortality for emergency priority level A calls, but a higher mortality was observed for emergency priority level B calls.

Prehospital triage accuracy in a criteria based dispatch centre

BACKGROUND

Priority dispatch accuracy is a key issue in optimizing the match between patients' medical needs and pre-hospital resources. This study measures the accuracy of a Criteria Based Dispatch (CBD) system, by evaluating discrepancies between dispatch priorities and ambulance crews' severity evaluations.

METHODS

This is a retrospective study conducted from January 2011 to December 2011. We ruled that a National Advisory Committee for Aeronautics (NACA) score > 3 (injuries/diseases which can possibly lead to deterioration of vital signs) to 7 (lethal injuries/ diseases) should require a priority dispatch with lights and siren (L&S), while NACA scores < 4 should require a priority dispatch without L&S. Over triage was defined as the proportion of L&S dispatches with a NACA score < 4, and under triage as the proportion of dispatches without L&S with a NACA score > 3.

RESULTS

There were 29,008 primary missions in 2011, 1122 were excluded. Of the 15,749 L&S missions, 12,333 patients had a NACA score < 4, leading to an over triage rate of 78 %; 561 missions out of 12,137 missions without L&S had a NACA score > 3, leading to an under triage rate of 4.6 %. Sensitivity was 86 % (95 % confidence interval: 85.6-86.4 %), specificity 48 % (47.4-48.6 %), positive predictive value 21.7 % (21.2-22.2 %), and negative predictive value 95.4 % (95.2-95.6 %).

CONCLUSION

The rates of over triage and under triage in our CBD are 78 and 4.6 % respectively. The lack of consistent or universal metrics is perhaps the most important limitation in dispatch accuracy research. This is mainly due to the large heterogeneity of dispatch systems and prehospital emergency system.

Non-specific complaints in the ambulance; predisposing structural factors

BACKGROUND

The pre-hospital assessment non-specific complaint (NSC) often applies to patients whose diagnosis does not match any other specific assessment correlating to particular symptoms or diseases, though some previous studies have found them to be related to serious underlying conditions. The aim was to identify whether the structural factors such as urgency according to the dispatch priority of the Emergency Medical Communication Centre (EMCC) or work load in the Emergency Medical Services (EMS) are predisposing factors for the assessment of NSC instead of a specific assessment.

METHODS

All patients with assessed condition NSCs by the EMS to Södersjukhuset during 2011 (n = 493) were compared with gender- and age-matched controls (n = 493), which were randomly drawn from all patients with specific conditions in the EMS, regarding day of week, time of day and priority set by EMCC with chi-squared tests and multivariate logistic regression models.

RESULTS

Among patients with NSCs, more were females (58 %) and the median age was 82. Almost all patients were categorized with NSCs during the daytime (8 a.m. to 9 p.m.), i.e. 450 (91 %) as compared to 373 (75 %) of those with specific conditions (p < 0.01). The risk of having an EMS dispatched as low priority by the EMCC was almost doubled among patients with NSCs compared to controls (OR 1.97, 95 % CI 1.38-2.79).

CONCLUSIONS

Since patients with NSCs appear most frequently during the hours with most transportations for the EMS, i.e. 10 a.m. to 2 p.m., and the risk of having the assessment NSC was doubled if the EMCC dispatched EMS as low priority, structural factors might be predisposing factors for the assessment.

Probabilistic Linkage of Prehospital and Outcomes Data in Out-of-hospital Cardiac Arrest

OBJECTIVE

Lack of longitudinal patient outcome data is an important barrier in emergency medical services (EMS) research. We aimed to demonstrate the feasibility of linking prehospital data from the California EMS Information Systems (CEMSIS) database to outcomes data from the California Office of Statewide Health Planning and Development (OSHPD) database for patients with out-of-hospital cardiac arrest (OHCA).

METHODS

We included patients age 18 years or older who sustained nontraumatic OHCA and were included in the 2010-2011 CEMSIS databases. The CEMSIS database is a unified EMS data collection system for California. The OSHPD database is a comprehensive data collection system for patient-level inpatient and emergency department encounters in California. OHCA patients were identified in the CEMSIS database using cardiac rhythm, procedures, medications, and provider impression. Probabilistic linkage blocks were created using in-hospital death or one of the following primary or secondary diagnoses (ICD-9-CM) in the OSHPD databases: cardiac arrest (427.5), sudden death (798), ventricular tachycardia (427.1), ventricular fibrillation (427.4), and acute myocardial infarction (410.xx). Blocking variables included incident date, gender, date of birth, age, and/or destination facility. Due to the volume of cases, match thresholds were established based on clerical record review for each block individually. Match variables included incident date, destination facility, date of birth, sex, race, and ethnicity.

RESULTS

Of the 14,603 cases of OHCA we identified in CEMSIS, 91 (0.6%) duplicate records were excluded. Overall, 46% of the data used in the linkage algorithm were missing in CEMSIS. We linked 4,961/14,512 (34.2%) records. Linkage rates varied significantly by local EMS agency, ranging from 1.4 to 61.1% (OR for linkage 0.009-0.76; p < 0.0001). After excluding the local EMS agency with the outlying low linkage rate, we linked 4,934/12,596 (39.2%) records.

CONCLUSION

Probabilistic linkage of CEMSIS prehospital data with OSHPD outcomes data was severely limited by the completeness of the EMS data. States and EMS agencies should aim to overcome data limitations so that more effective linkages are possible.

Intentional drug poisoning care in a physician-manned emergency medical service

OBJECTIVES

Severely poisoned patients can benefit from intensive and specific treatments. Emergency medical services (EMS) may therefore play a crucial role by matching prehospital care and hospital referral to the severity of poisoned patients. Our aim was to investigate EMS accuracy in this condition.

METHODS

A 3-year retrospective study was conducted in a university hospital. Emergency telephone calls about adult patients with intentional drug poisoning (IDP) were included. In daily practice, an emergency physician answers such telephone calls and dispatches either first responders or a mobile intensive care unit (MICU). According to on-scene evaluation, patients are referred to the emergency department (ED) or to an intensive care unit (ICU). We therefore calculated global EMS accuracy according to patients' actual medical needs. We further evaluated the performance of dispatch and hospital referral decision. We also performed a regression analysis to identify factors of inappropriate dispatch.

RESULTS

A total of 2,227 patients were studied. Median age was 41 years old (range 30-49) and 63% were women. Dispatch was appropriate for 1,937 (87%) patients. Sensitivity and specificity of dispatch decision were 0.43 and 0.93, respectively. Decision of patients' referral to an appropriate hospital facility had a sensitivity of 0.67 and a specificity of 0.98. Toxicological data, age, and Glasgow coma scale were significantly associated with inappropriate EMS decisions.

CONCLUSIONS

A physician-operated EMS is an accurate system to provide prehospital care to IDP patients. However, dispatch physicians should pay attention, especially to toxicological anamnesis, to anticipate proper patient care.

Which emergency medical dispatch codes predict high prehospital nontransport rates in an urban community?

BACKGROUND

The Medical Priority Dispatch System (MPDS) is a commonly used computer-based emergency medical dispatch (EMD) system that is widely used to prioritize 9-1-1 calls and optimize resource allocation. There are five major priority classes used to dispatch 9-1-1 calls in the San Francisco System; Alpha codes are the lowest priority (lowest expected acuity) and Echo are the highest priority.

OBJECTIVE

We sought to determine which MPDS dispatch codes are associated with high prehospital nontransport rates (NTRs).

METHODS

All unique MPDS call categories from 2009 in a highly urbanized, two-tier advanced life support (ALS) system were sorted according to highest NTRs. There are many reasons for nontransport, such as "gone on arrival," and "patient denied transport." Those categories with greater than 100 annual calls were further evaluated. MPDS groups that included multiple categories with NTRs exceeding 25% were then identified and each category was analyzed. Results. EMS responded to a total of 81,437 calls in 2009, of which 18,851 were not transported by EMS. The majority of the NTRs were found among "cardiac/ respiratory arrest/death," "assault/sexual assaults," "unknown problem/man down," "traffic/transportation accidents," and "unconscious/fainting." "Cardiac or respiratory arrest/death -obvious death" (9B1) had the highest overall nontransport rate, 99.25% (1/134), most likely due to declaration of death. "Unknown problem -man down -medical alert notification" had the second highest NTR, 67.22% (138/421). However, Echo priority codes had the highest overall nontransport rates (45.45%) and Charlie had the lowest (13.84%).

CONCLUSIONS

The nontransport rates of individual MPDS categories vary considerably and should be considered in any system design. We identified 52 unique call categories to have a 25% or greater NTR, 18 of which exceeded 40%. The majority of NTRs occurred among the "cardiac/respiratory arrest/death," "assault/sexual assaults," "unknown problem/man down," "traffic/transportation accidents," and "unconscious/fainting" categories. The higher the priority code within each subset (AB vs. CDE), the less likely the patient was to be transported. Charlie priority codes had a lower NTR than Delta, and Delta was lower than Echo. Charlie codes were therefore the strongest predictors of hospital transport, while Echo codes (highest priority) were those with the highest nontransport rates and were the worst predictors of hospital transport in the emergent subset.