Differences between the dispatch priority assessments of emergency medical dispatchers and emergency medical services: a prospective register-based study in Finland

Pain Assessment and Its Effect on Pain Management During Emergency Medical Services-A Descriptive Study in the Tampere University Hospital Area of Finland

BACKGROUND

Pain management is an important part of prehospital care. Pain assessment and recognition are inseparable in adequate pain treatment. However, relatively scarce literature is available from Nordic and European countries. We investigated the current practices of pain assessment and management in the Emergency Medical Service (EMS) of one Finnish hospital area.

METHODS

The cohort data were originally collected prospectively to assess various quality processes in EMS. This study was designed as a descriptive, retrospective, register-based cohort study, analysing data from 7245 patients encountered by EMS over a one-month period in the Tampere University Hospital area of Finland in 2021. Pain levels were primarily assessed using the numeric rating scale (NRS). Records were combined with auxiliary data from the National Emergency Response Centre Agency. We grouped patients into six age groups and recorded dispatch categories in seven groups to clarify the impact of age and dispatch category on pain assessment and intensity. We used crosstabulation and Pearson's chi-square test for statistical analyses; we also applied a linear mixed model to analyse the effect of pain medication on pain intensity.

RESULTS

Pain was assessed once in 2586 (36%) patients and then reassessed a second time for 707 (27%) of those patients. Age and dispatch category affected pain intensity (p < 0.001): Patients under 66 reported higher pain intensity than patients over 66 years. In dispatch categories, "oxygen deficiency" and "non-mechanical accident or exposure," reported pain intensity was minimal compared to other categories. Of the patients with a pain assessment, 611 (24%) experienced pain equal to or greater than 4 on the NRS scale. We found that assessment with a high NRS level was associated with a higher likelihood of administering potent pain medication.

CONCLUSION

The prevalence of pain assessment is relatively low. The diverse nature of EMS interventions must be considered when improving pain management strategies.

Exploring care pathways of patients conveyed by emergency medical services (EMS) through electronic health records

BACKGROUND

Emergency Medical Services (EMS) and Emergency Departments (ED) have reported increased patient volumes in the last decades. Despite high rates of non-conveyance decisions, unnecessary conveyances by EMS still occur. The aim of this study was to explore care pathways of conveyed patients by EMS through registry data.

METHODS

This was a retrospective cohort study of EMS patients in Finland. The primary outcomes were EMS recontacts and visits to a primary health care facility or ED within seven days. The secondary outcome was mortality within one week. Univariate and multivariable associations between the outcome variables and categorical variables were analysed with logistic regression. Results are presented with odds ratios (ORs) together with 95% confidence intervals (CIs) and p-values.

RESULTS

The conveyed patients' visits to health care facilities were mainly brief. EMS arrival during night-time (20:00-08:00) (OR 1.69; 95% CI 1.59 to 1.80), in urban area (OR 1.21; 95% CI 1.13 to 1.29) and alcohol use (OR 2.55; 95% CI 2.26 to 2.86) predicted short ED visits (< 24 h). 77% of the patients were discharged from primary health care within one hour (median 22 min, IQR 18-60). After EMS conveyance and visit to the ED or primary health care facility, 10.5% of the patients were readmitted within one week. Non-urgent patients (OR 1.26; 95% CI 1.14 to 1.39), an EMS mission at night (OR 1.36; 95% CI 1.24 to 1.50), and based on univariate analyses, the usage of alcohol (OR 1.26; 95% CI 1.09 to 1.45) increased the likelihood of a readmission. 449 patients of all conveyed EMS patients (n = 20376) died within one week (2.2%).

CONCLUSIONS

EMS non-conveyance reduces patient flow in EDs, but there is a possibility that more could be done related to unnecessary conveyances to health care facilities, especially in urban areas and at night. The pathway analyses of post conveyance re-contacts show that a small number of patients burden the system. Further in-depth studies are needed to understand of unnecessary conveyances, find solutions, and provide repeated users the appropriate care.

Understanding EMS response times: a machine learning-based analysis

BACKGROUND

Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning (ML) techniques to identify key factors such as urgency levels, environmental conditions, and geographic variables. The findings aim to inform strategies for enhancing resource allocation and operational efficiency in EMS systems.

METHODS

A retrospective analysis was conducted using over one million EMS missions from Stockholm, Sweden, between 2017 and 2022. Advanced ML techniques, including Gradient Boosting models, were applied to evaluate the influence of diverse variables such as call handling times, travel times, weather patterns, and resource availability. Feature engineering was employed to extract meaningful insights, and statistical models were used to validate the relationships between key predictors and response times.

RESULTS

The study revealed a complex interplay of factors influencing EMS response times, aligning with the study's aim to deepen the understanding of these determinants. Key drivers of response time variability included weather conditions, call priority, and resource constraints. ML models, particularly Gradient Boosting, proved effective in quantifying these impacts and provided robust predictions of response times across scenarios. By providing a comprehensive evaluation of these influences, the results support the development of adaptive resource allocation models and evidence-based policies aimed at enhancing EMS efficiency and equity across all call priorities.

CONCLUSIONS

This study underscores the potential of ML-driven insights to revolutionize EMS resource allocation strategies. By integrating real-time data on weather, call types, and workload, EMS systems can transition to adaptive deployment models, reducing response times and enhancing equity across priority levels. The research provides a blueprint for implementing predictive analytics in EMS operations, paving the way for evidence-based policies that improve emergency care efficiency and outcomes.

CLINICAL TRIAL NUMBER

Not applicable.

Emergency call utilization over a 10-years period: an observational study in Region Zealand, Denmark, 2013-2022

BACKGROUND

Improving prehospital emergency care requires a comprehensive understanding of the efficiency of emergency medical services and demand fluctuations. The medical emergency call is the primary contact between citizens and the emergency medical dispatch center, serving as the gateway to accessing emergency assistance. This study aimed to characterize the emergency call population and analyze the development of emergency call utilization in Region Zealand in Denmark during a 10-years period.

METHODS

This was an observational register-based study of administrative data from the emergency medical dispatch center in Region Zealand. Data was collected from 1 January 2013 to 31 December 2022. All unique emergency calls from residents to the emergency number "1-1-2" were included. Descriptive analyses were used to characterize the study population. Poisson regression models were used to calculate ratio estimates for the association between years and hospital catchment areas, using the incidence rate of emergency calls as outcome measure.

RESULTS

A total of 641,457 emergency calls were included. A significant increase in the total number of emergency calls was found, with an increase from 58,454 annual calls to 80,819 calls over the study period. The incidence rate per 1000 residents per year increased from 71.1 to 95.2, a 35% increase. The southern part of the region had significantly more emergency calls per 1000 residents per year during the study period compared to the eastern part of the region (IRR 1.70). Demographically, males comprised 52.3% of cases, and patients aged 65 and older represented 48.2% of calls. Emergency calls were "Emergency level A" in 45.5% and "Emergency level B" in 39.1%. In 22.3% of cases, the emergency call was categorized as "Unclear problem." The most frequent categories were "chest pain" (12.7%), "impaired consciousness" (9.6%), "breathing difficulties" (8.8%), "accidents" (7.9%), and "minor injuries" (7.6%).

CONCLUSIONS

The study revealed a significant increase in emergency calls, both in absolute numbers and per 1000 residents per year, indicating growing demand for emergency care, along with a surge in activity at the region's dispatch center. Regional disparities underscores the potential necessity for tailored developmental approaches over time.

Correlation between the accuracy of the emergency response centre's urgency assessment and emergency medical services non-conveyance: a retrospective register-based study in Finland

BACKGROUND

In modern emergency medical services (EMS), ambulances increasingly focus on examining and treating the patient at the scene. This has led to increased levels of non-conveyance. In Finland, for instance, approximately 40% of EMS dispatches end up in non-conveyance. As EMS systems evolve, the proportion of non-conveyance could serve as a cost-effective measure to assess the quality of the dispatch criteria, if a link to the performance of urgency assessment would be established. The purpose of this study was to investigate whether the proportion of non-conveyance is associated with the test performance levels of the urgency assessment. This investigation was done separately within each dispatch category.

METHODS

A retrospective evaluation of the data was conducted on all EMS dispatches in the Pirkanmaa Hospital District from 1 August 2021 through 31 August 2021. There were a total of 7,245 EMS dispatches during the study period of which 829 were excluded. This study was conducted by comparing the existing test performance levels (sensitivity, specificity and under- or overestimation) of the emergency response centre's urgency assessment with the non-conveyance rate (%) of each dispatch category. The relationships between the variables were measured using Spearman's rank correlation coefficient.

RESULTS

The proportion of over-triage was the only urgency assessment's test performance variable that had a statistically significant correlation with the proportion of non-conveyance (r = 0.568; p = 0.003). Other test performance variables of the urgency assessment had no or little correlation to the proportion of non-conveyance. Of the 6,416 EMS dispatches in the study period, 42% (2,672) resulted in non-conveyance of the patient. In nine dispatch categories, at least half (51-69%) of the dispatches ended in non-conveyance.

CONCLUSIONS

Based on this study, it seems that the percentage of non-conveyance in the dispatch category could be used, with certain limitations, to assess the proportion of over-triage in the dispatch category. The method is particularly applicable in scenarios where the dispatch criteria have undergone modifications and there is a need to monitor the effect of the changes on the level of over-triage.

Exploring stress management strategies among emergency medical service providers in Iran: a qualitative content analysis

BACKGROUND

Emergency medical service providers are frequently exposed to a variety of stressors as a result of their work environment. These stressors can have detrimental effects on both the physical and mental well-being of individuals. This study was conducted with the aim of exploring stress management strategies in emergency medical service providers.

METHODS

This study was conducted in 2023 using a qualitative approach and content analysis method. A purposive sampling method was used to include 16 emergency medical system providers from Hamadan city. Semi-structured interviews, with a duration of 45-60 min, were conducted for data collection. The Data were analyzed using Graneheim and Lundman's conventional content analysis approach.

RESULTS

The analysis of the interview data revealed three themes: readiness for the worst conditions, assistance based on supportive partnerships, and striving for balance. The six categories within these three themes were mental preparation, risk management, collaborations in emergency response, supportive communication, adaptive behaviors, and maladaptive responses.

CONCLUSIONS

The results of this study shed light on the various stress management strategies employed by emergency medical service providers. Understanding and implementing effective stress management strategies can not only enhance the well-being of emergency medical service providers but also improve the quality of patient care. Further research and action are essential to promote the resilience and mental health of these professionals, ensuring their overall well-being and job satisfaction.

112 What's your emergency? Overview of mental health and sleep disorders among emergency medical dispatchers in a French 112 call center

BACKGROUND

Emergency medical dispatchers (EMD) experience significant occupational stress. Medical dispatching includes call-taking, triage, dispatch, and providing callers gesture guidance to the victims. Every decision has a major impact on the patient's outcome. Chronic exposure to stress and potentially traumatic situations, combined with night shifts can impact the stress response and physical health of staff.

OBJECTIVES

To evaluate the prevalence of mental health and sleep disorders among EMD personnel working in a 112-call center, prior to an evidence-based prevention intervention (primary outcome); and to assess the relationship between health outcomes and DM (secondary outcome).

METHODS

We conducted a descriptive, monocentric study with 109 EMD. HAD Anxiety (HAD-A) and Depression (HAD-D) scores, and the PTSD checklist for DSM-5 (PCL-5) were used to explore mental health disorders. The Epworth Sleepiness Scale, and other analog scales were used to explore sleep disorders. DM resources were assessed using the Freiburg Mindfulness Inventory (FMI), and its Presence and Acceptance subscales.

RESULTS

A total of 72% of the EMD working in the call center were included. Of these, 16.6% had moderate anxiety disorder, and 6.4% had an anxiety disorder (Mean HAD-A: 6.05 ± 2.88). Furthermore, 16.6% had a moderate depression disorder, and 6.4% had a depression disorder (Mean HAD-D: 4.28 ± 3.28), and 16% had symptoms of PTSD (Mean PCL-5: 17.57 ± 13.67). Turning to sleep, 39% may suffer from excessive daytime sleepiness (EDS), and 10% had confirmed EDS (Mean Epworth score 10.47 ± 4.41). Finally, 39% had moderate insomnia, and 59% had severe insomnia (Mean insomnia: 13.84 ± 5.77.). Medium-strength negative correlations were found between mental health and DM (FMI scores and sub-scores: -0.48 < r < - 0.29; 0.001 < p < 0.004); and a positive correlation was found between DM and daytime awareness (0.22 < r < 0.26; 0.01 < p < 0.03).

CONCLUSION

The prevalence of depression, symptoms of PTSD, and sleep disorders in our sample of EMD is significant, and confirms findings reported in the literature. The EMD population may benefit from specific, multi-level interventions that target mindfulness, sleep, and ergonomics to improve their mental and physical health.

Palliative care patients in the emergency medical service: a retrospective cohort study from Finland

BACKGROUND

Paramedics are often involved in treating palliative care patients with difficulties regarding symptom control. They report minimal training in palliative care and find decision-making difficult. This often leads to overtreatment and unnecessary transportation to the emergency department. The study's objective is to determine how much palliative patients use emergency services, how well are they recognized by paramedics and how paramedics choose care in terms of treatment and transportation.

METHODS

This study is a retrospective cohort study based in the Finnish Tampere University Hospital area. We included patients with a palliative care decision setting the goal of therapy as palliative intent between 1 August 2021 and 31 December 2021 and who died before 1 April 2022. From these patients, records of nurse paramedic visits were retrieved. Descriptive statistics were used to describe the data.

RESULTS

Paramedics visited 69 patients in 97 callouts. These callouts comprised 0.26% of the total dispatches in the study area. The most common reasons for callouts were general weakness, breathing difficulty and pain. The paramedics provided treatment in 40% of the missions. 55% of the patients were transported to the emergency department. A palliative care plan was recognized by the paramedics in 42 of the 97 callouts. A total of 38 patients were recognized as palliative care patients by the paramedics while in the cases of 31 patients, palliative care was not recognized in any dispatch.

CONCLUSION

Patients in palliative care cause only a minimal load on the emergency medical services, but the paramedics do not necessarily recognize them as such. This leads to the risk of overtreatment and a high transportation rate to the emergency department, which is not an ethical choice. Recognition and treatment provided to palliative care patients by the paramedics could be improved with additional training and greater availability of patient records.

The SEE-IT Trial: emergency medical services Streaming Enabled Evaluation In Trauma: a feasibility randomised controlled trial

BACKGROUND

Use of bystander video livestreaming from scene to Emergency Medical Services (EMS) is becoming increasingly common to aid decision making about the resources required. Possible benefits include earlier, more appropriate dispatch and clinical and financial gains, but evidence is sparse.

METHODS

A feasibility randomised controlled trial with an embedded process evaluation and exploratory economic evaluation where working shifts during six trial weeks were randomised 1:1 to use video livestreaming during eligible trauma incidents (using GoodSAM Instant-On-Scene) or standard care only. Pre-defined progression criteria were: (1) ≥ 70% callers (bystanders) with smartphones agreeing and able to activate live stream; (2) ≥ 50% requests to activate resulting in footage being viewed; (3) Helicopter Emergency Medical Services (HEMS) stand-down rate reducing by ≥ 10% as a result of live footage; (4) no evidence of psychological harm in callers or staff/dispatchers. Observational sub-studies included (i) an inner-city EMS who routinely use video livestreaming to explore acceptability in a diverse population; and (ii) staff wellbeing in an EMS not using video livestreaming for comparison to the trial site.

RESULTS

Sixty-two shifts were randomised, including 240 incidents (132 control; 108 intervention). Livestreaming was successful in 53 incidents in the intervention arm. Patient recruitment (to determine appropriateness of dispatch), and caller recruitment (to measure potential harm) were low (58/269, 22% of patients; 4/244, 2% of callers). Two progression criteria were met: (1) 86% of callers with smartphones agreed and were able to activate livestreaming; (2) 85% of requests to activate livestreaming resulted in footage being obtained; and two were indeterminate due to insufficient data: (3) 2/6 (33%) HEMS stand down due to livestreaming; (4) no evidence of psychological harm from survey, observations or interviews, but insufficient survey data from callers or comparison EMS site to be confident. Language barriers and older age were reported in interviews as potential challenges to video livestreaming by dispatchers in the inner-city EMS.

CONCLUSIONS

Progression to a definitive RCT is supported by these findings. Bystander video livestreaming from scene is feasible to implement, acceptable to both 999 callers and dispatchers, and may aid dispatch decision-making. Further assessment of unintended consequences, benefits and harm is required.

TRIAL REGISTRATION

ISRCTN 11449333 (22 March 2022). https://www.isrctn.com/ISRCTN11449333.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.