Can public health registry data improve Emergency Medical Dispatch?

Andersen, Mikkel S; Christensen, Erika Frischknecht; Jepsen, S; Nørtved, J.; Hansen, Jesper Bjerring

doi:10.1111/aas.12654

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Comparison of ICD-10 diagnosis with dispatch keyword categories implies some degree of imprecision, since dispatch keywords often describe emergency situations or medical conditions rather than suspected diagnoses. We could not study patient factors which are likely to be associated with the outcome or other variables, like socioeconomic status or access to care and could not capture comorbid conditions, which are known to increase the risk of short-term adverse outcomes for time-critical 112 callers with the same complaint 14. The study area is a metropolitan area and results might be different in rural regions or even in metropolitan areas with different prehospital treatment or admission practices.…”

Section: Discussionmentioning

confidence: 99%

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

2019

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ObjectiveRising 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.DesignRetrospective observational study using linked secondary data.Setting and participants78 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 measuresCharacteristics 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.Results39.4% 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.ConclusionYoung 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.

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Section: Discussionmentioning

confidence: 99%

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

2019

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“…Results are presented as cause-specific hazard ratios (HR) with corresponding 95% confidence intervals (CI). We used sex, age, and socioeconomic status (described previously) as exposures, and Charlson comorbidity index was dichotomized to fit the model as 0 versus > 0 because most patients were assumed to be young and healthy as previously shown [19]. Each exposure was adjusted for the other three.…”

Section: Statistical Analysesmentioning

confidence: 99%

Mortality and return to work in patients transported by emergency ambulance after involvement in a traffic accident

et al. 2020

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Introduction/background Traffic accidents constitute a common reason for injury. Little is known about long-term outcomes for patients following a traffic accident. Therefore, in this present paper, we examine 1-day, 30-day and 1-year mortality, and return to work (RTW) during a 1-year period. Methods Patients (between 18 and 65 years of age) who had an ambulance dispatched to them following a traffic accident and who were employed prior to the accident were identified from the Electronic Prehospital Emergency Patient (amPHI™) database in the North Denmark Region (catchment population ≈600,000) during 2006–2014. Outcomes of 1- and 30- and 365-day mortality and 1-year return to work (RTW), with mortality as competing risk. We stratified by intensive care unit (ICU) admission; and the anatomical region of injury (head/neck, thorax, abdomen, extremities and multiple injuries) is reported. Results Of 6072 patients in our study population, 59 (1%) died within 1 day and 76 (1.3%) within 30 days; 88 (1.5%) were dead within a year. Thirty-day mortality was 1.7% for the 290 patients admitted to the ICU, and 1.2% for the remaining 5782 patients. Within the study population, RTW rate was 92.7% (N = 5984). RTW was 84.8% among 290 ICU-admitted patients versus 93.1% for the remaining 5782 patients. RTW rate was 94.6% for the 1793 patients discharged with a diagnosis of head/neck injury. Of 671 patients with a discharge diagnosis for the thoracic region, 92.6% returned to work. Of 402 patients with abdominal injury diagnoses, 90.8% returned to work. Of 1603 patients discharged with a diagnosis of extremity injury, the RTW rate was 93.6%. Of 192 patients with a discharge diagnosis of injury in multiple regions, 91.7% returned to work. Conclusion Overall, mortality rates were low and RTW rates high in patients who had an ambulance dispatched due to a traffic accident. Those admitted to the ICU had the lowest RTW rate, yet still around 80% returned to work.

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“…Ambulance response times may impact morbidity and mortality, especially among patients with time-critical conditions, such as 'the first-hour quintet': cardiac arrest, chest pain, stroke, severe respiratory failure and severe trauma [8]. Based on a study among patients with cardiac arrest [9], a response time of eight minutes or less has been widely accepted as the gold standard [10,11].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to assess the extent of busy ambulances and its impact on ambulance response times: A retrospective observational study

Næss,

Krüger,

Uleberg

et al. 2024

PLoS ONE

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Background Ambulance response times are considered important. Busy ambulances are common, but little is known about their effect on response times. Objective To assess the extent of busy ambulances in Central Norway and their impact on ambulance response times. Design This was a retrospective observational study. We used machine learning on data from nearby incidents to assess the probability of up to five different ambulances being candidates to respond to a medical emergency incident. For each incident, the probability of a busy ambulance was estimated by summing the probabilities of candidate ambulances being busy at the time of the incident. The difference in response time that may be attributable to busy ambulances was estimated by comparing groups of nearby incidents with different estimated busy probabilities. Setting Medical emergency incidents with ambulance response in Central Norway from 2013 to 2022. Main outcome measures Prevalence of busy ambulances and differences in response times associated with busy ambulances. Results The estimated probability of busy ambulances for all 216,787 acute incidents with ambulance response was 26.7% (95% confidence interval (CI) 26.6 to 26.9). Comparing nearby incidents, each 10-percentage point increase in the probability of a busy ambulance was associated with a delay of 0.60 minutes (95% CI 0.58 to 0.62). For incidents in rural and urban areas, the probability of a busy ambulance was 21.6% (95% CI 21.5 to 21.8) and 35.0% (95% CI 34.8 to 35.2), respectively. The delay associated with a 10-percentage point increase in busy probability was 0.81 minutes (95% CI 0.78 to 0.84) and 0.30 minutes (95% CI 0.28 to 0.32), respectively. Conclusion Ambulances were often busy, which was associated with delayed ambulance response times. In rural areas, the probability of busy ambulances was lower, although the potentially longer delays when ambulances were busy made these areas more vulnerable.

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Can public health registry data improve Emergency Medical Dispatch?

Abstract: Increasing age, male sex, and hospitalization history was associated with increased risk of death on day 1 for FHQ 112 callers. Additional efforts are warranted to clarify the role for risk prediction tools in emergency medical dispatch.

Cited by 6 publications

References 23 publications

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

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

Mortality and return to work in patients transported by emergency ambulance after involvement in a traffic accident

Using machine learning to assess the extent of busy ambulances and its impact on ambulance response times: A retrospective observational study

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