The Prevalence and Characteristics of Non-Transports in a Provincial Emergency Medical Services System: A Population-Based Study

Carrigan, Steven; Goldstein, Judah; Carter, Alix; Asada, Yukiko; Travers, Andrew H.

doi:10.1016/j.jemermed.2021.12.009

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…It could also mitigate the risks associated with high-priority driving, with increased risk of road traffic accidents and under-triage associated with not transporting a patient [30][31][32][33]. It is crucial to highlight that numerous recent studies have accentuated the significant risks of failing to transport patients who have called for pre-hospital emergency assistance, thereby depriving them of advanced in-hospital care [34,35].…”

Section: Plos Onementioning

confidence: 99%

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

Farhat,

Makhlouf,

Gangaram

et al. 2024

PLoS ONE

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Background The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study’s objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. Methods ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. Results All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients’ transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified “Transported” cases (False Positive). Conclusion The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.

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Section: Plos Onementioning

confidence: 99%

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

Farhat,

Makhlouf,

Gangaram

et al. 2024

PLoS ONE

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“…As a consequence of the growing proportion of low acuity conditions attended to by ambulance services, there is a varying percentage of patients that are deemed to not require transport to hospital [ 3 , 6 , 10 , 11 , 12 , 13 , 14 ]. Whilst many of these patients can safely remain in the community, there is a cohort who still require some form of health or medical care [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Paramedic Education to Support the Use of Low-Acuity Care Pathways: A Scoping Review Protocol

2023

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Ambulance services worldwide have transformed over time into health care services that not only attend to life-threatening emergencies, but are also increasingly being utilised for patients with low-acuity or non-urgent illness and injury. As a result, there has been a need to adapt and include mechanisms to assist paramedics in the assessment and management of such patients, including alternative pathways of care. However, it has been identified that education and training for paramedics in the care of low-acuity patients is limited. This study aims to identify potential gaps in the literature and inform further research, paramedic education and training, patient care guidelines, and policy. A scoping review will be conducted utilising the Joanna Briggs Institutes methodology. A range of relevant electronic databases will be searched along with the grey literature, using search terms related to paramedic education for low-acuity patient care pathways. The search results will be screened by two authors and presented in the PRISMA-ScR format, with articles presented in tabular format and analysed thematically. The results of this scoping review will inform further research exploring paramedic education, clinical guidelines, policy and experiences in the management of low-acuity patients.

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“…Next, ED visit research may underestimate the burden of ACSCs on 911/999 because 16%–38% of patients are not transported to ED 17–19 and these non-transports may be primarily for low-acuity conditions. 18 19 Finally, self-report data may suffer from bias in who can be contacted, self-selection bias, recall bias, social desirability bias and errors due to incorrect responses (eg, poor health literacy). Therefore, being able to identify ACSCs from paramedic impression codes in administrative datasets would be valuable for health system research; for example, studies on the differential rates in 911/999 non-transports for ACSC and non-ACSC calls, and potential primary care interventions.…”

Section: Introductionmentioning

confidence: 99%

“…Increasingly, prehospital research studies are using clinical impressions for this reason. 1 17 Next, ED visit research may underestimate the burden of ACSCs on 911/999 because 16%-38% of patients are not transported to ED [17][18][19] and these non-transports may be primarily for low-acuity conditions. 18 19 Finally, selfreport data may suffer from bias in who can be contacted, self-selection bias, recall bias, social desirability bias and errors due to incorrect responses (eg, poor health literacy).…”

mentioning

confidence: 99%

Development of indicators for avoidable emergency medical service calls by mapping paramedic clinical impression codes to ambulatory care sensitive conditions and mental health conditions in the UK and Canada

Agarwal,

Siriwardena,

McLeod

et al. 2023

BMJ Open

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ObjectiveParamedic assessment data have not been used for research on avoidable calls. Paramedic impression codes are designated by paramedics on responding to a 911/999 medical emergency after an assessment of the presenting condition. Ambulatory care sensitive conditions (ACSCs) are non-acute health conditions not needing hospital admission when properly managed. This study aimed to map the paramedic impression codes to ACSCs and mental health conditions for use in future research on avoidable 911/999 calls.DesignMapping paramedic impression codes to existing definitions of ACSCs and mental health conditions.SettingEast Midlands Region, UK and Southern Ontario, Canada.ParticipantsExpert panel from the UK-Canada Emergency Calls Data analysis and GEospatial mapping (EDGE) Consortium.ResultsMapping was iterative first identifying the common ACSCs shared between the two countries then identifying the respective clinical impression codes for each country that mapped to those shared ACSCs as well as to mental health conditions. Experts from the UK-Canada EDGE Consortium contributed to both phases and were able to independently match the codes and then compare results. Clinical impression codes for paramedics in the UK were more extensive than those in Ontario. The mapping revealed some interesting inconsistencies between paramedic impression codes but also demonstrated that it was possible.ConclusionThis is an important first step in determining the number of ASCSs and mental health conditions that paramedics attend to, and in examining the clinical pathways of these individuals across the health system. This work lays the foundation for international comparative health services research on integrated pathways in primary care and emergency medical services.

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The Prevalence and Characteristics of Non-Transports in a Provincial Emergency Medical Services System: A Population-Based Study

Cited by 6 publications

References 26 publications

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

Paramedic Education to Support the Use of Low-Acuity Care Pathways: A Scoping Review Protocol

Development of indicators for avoidable emergency medical service calls by mapping paramedic clinical impression codes to ambulatory care sensitive conditions and mental health conditions in the UK and Canada

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