1887
Volume 2025, Issue 3
  • ISSN: 0253-8253
  • EISSN: 2227-0426

Abstract

Ambulance collisions pose a significant occupational risk to personnel, patients, and the public. Despite ongoing efforts to improve safety measures, the complex nature of emergency response operations continues to pose challenges in reducing collision risks.

This study investigates the role of the dedicated Vehicle Collisions Review Panel at Hamad Medical Corporation Ambulance Service (HMCAS) in identifying, understanding, and managing risks associated with ambulance collisions.

A retrospective quantitative analysis of HMCAS ambulance collision records from 2023 was conducted using descriptive and bivariate analyses, along with supervised and unsupervised machine learning (ML) techniques – including multinomial logistic regression (MLR), decision tree (DT) analysis, association rule mining (ARM), and time series forecasting – to uncover hidden patterns, predictive insights, and future projections.

A total of 131 ambulance collisions were analyzed. The majority of incidents involved emergency urban ambulances. MLR and DT achieved prediction accuracies of 41% and 35%, respectively. ARM revealed significant association between daytime incidents, normal road conditions, and the absence of patient involvement. Time series forecasting predicted a gradual increase followed by stabilization in collision incidents.

This study highlights the crucial role of a dedicated collision review panel in managing and mitigating ambulance collision risks. ML techniques provided evidence-based support for decision-making. Future research is needed to evaluate the long-term impacts of targeted training programs and safety protocols.

© 2025 Farhat, Alinier, Khedhiri, Ramos, Derbel, Rekik, et al., licensee HBKU Press.
Loading

Article metrics loading...

/content/journals/10.5339/qmj.2025.75
2025-08-22
2026-04-15

Metrics

Loading full text...

Full text loading...

/deliver/fulltext/qmj/2025/3/qmj.2025.75.html?itemId=/content/journals/10.5339/qmj.2025.75&mimeType=html&fmt=ahah

References

  1. Eskandari Z, Ghomian Z, Alibabaei A, Ahmadinejad H, Sohrabizadeh S. Air ambulance base development for responding to traffic accidents in Iran. Trauma Monthly. 2021 Jun 1; 26:(3):157–66. http://doi.org/10.30491/TM.2021.279823.1273
     [Google Scholar]
  2. Salam S, Mehta A, Kim D, Seyi-Olajide J, Allo N, Malolan C, et al. Improving Lagos State emergency medical services by analysing road traffic accident data, response time, and efficient allocation of ambulances. The Lancet Glob Health. 2022 Mar 1; 10:: S26. http://doi.org/10.1016/S2214-109X(22)00155-3
     [Google Scholar]
  3. Wiwekananda KSS, Hamukti RP, Yogananda KSS, Calisto KE, Utomo PS. Understanding factors of ambulance delay and crash to enhance ambulance efficiency: An integrative literature review. J Community Empower Health. 2020 Dec 31; 3:(3):213. http://doi.org/10.22146/jcoemph.57338
     [Google Scholar]
  4. UNDRR. Road Traffic Accident | UNDRR. 2023 [cited 10 Jul 2024]. Available from:http://www.undrr.org/understanding-disaster-risk/terminology/hips/tl0052
     [Google Scholar]
  5. Hareru HE, Negassa B, Kassa Abebe RL, Ashenafi E, Zenebe GA, Debela BG, et al. The epidemiology of road traffic accidents and associated factors among drivers in Dilla Town, Southern Ethiopia. Front Public Health. 2022 Nov 10; 10::1007308. http://doi.org/10.3389/fpubh.2022.1007308
     [Google Scholar]
  6. Qatar News Agency. Traffic-Related Deaths, Grave Injuries in Qatar Decline by 24.3 Percent in 2023. 2024 [cited 19 Jun 2024]. Available from:https://www.qna.org.qa/en/News-Area/News/2024-03/07/0071-traffic-related-deaths,-grave-injuries-in-qatar-decline-by-243-percent-in-2023
     [Google Scholar]
  7. Delavary M, Ghayeninezhad Z, Lavallière M. Prevalence and characteristics of ambulance collisions, a systematic literature review. Safety. 2023; 9:(2):24. http://doi.org/10.3390/safety9020024
     [Google Scholar]
  8. Alsofayan YM, Alhajjaj FS, Alowais JM, Alsuhaymi FSM, Almutairi AA, Alsharef MK, et al. The epidemiology of prehospital ambulance crashes: A national experience across Saudi Red Crescent Authority. Cureus. 2023 Jul 17; 15:(7):e42049. http://doi.org/10.7759/cureus.42049
     [Google Scholar]
  9. Giribabu D, Ghosh K, Hari R, Chadha I, Rathore S, Kumar G, et al. Road accidents on Indian National highways, ambulance reachability and transportation of injured to trauma facility: Survey-based introspection of golden hour. J Fam Med Prim Care. 2024 Feb 22; 13:(2):704–712. http://doi.org/10.4103/jfmpc.jfmpc_1832_23
     [Google Scholar]
  10. Alinier G, Meyer J, Farhat H, Bayoumy A, Gonzales E, Ragbheer S, et al. Initial results from a driving safety survey distributed to ambulance paramedics in Qatar. J Local Glob Health Sci. 2015 Nov 12; 2015:(2):59. http://doi.org/10.5339/jlghs.2015.itma.59
     [Google Scholar]
  11. Alinier G, Meyer J, Hutton D, Farhat H, Bayoumy A, Gonzales E, et al. Raising awareness about driving safety in an ambulance service. J Emerg Med Trauma Acute Care. 2016 Oct 9; 2016:(2):21. http://doi.org/10.5339/jemtac.2016.icepq.21
     [Google Scholar]
  12. Gangaram P, Alinier G, Menacho AM. Crisis resource management in emergency medical settings in Qatar. Int Paramed Pract. 2017 Aug 2; 7:(2):18–23. http://doi.org/10.12968/ippr.2017.7.2.18
     [Google Scholar]
  13. Farhat H, Abid C, El Aifa K, Gangaram P, Jones A, Khenissi MC, et al. Epidemiological determinants of patient non-conveyance to the hospital in an emergency medical service environment. Int J Environ Res Public Health. 2023 Jul 20; 20:(14):6404. http://doi.org/10.3390/ijerph20146404
     [Google Scholar]
  14. Demir S, Tunçbilek Z, Alinier G. Prehospital emergency health services in Qatar. J Paramed Pract. 2022 Nov 23; 14:(11):456–62. http://doi.org/10.12968/jpar.2022.14.11.456
     [Google Scholar]
  15. Devi KG, Balasubramanian K, Ngoc LA. Machine Learning and Deep Learning Techniques for Medical Science. CRC Press; 2022. 413.
    [Google Scholar]
  16. Zhang M, Chen Y, Susilo W. Decision tree evaluation on sensitive datasets for secure e-Healthcare systems. IEEE Trans Dependable Secure Comput. 2023 Sep; 20:(5):3988–4001. http://doi.org/10.1109/TDSC.2022.3219849
     [Google Scholar]
  17. Ramasamy S, Nirmala K. Disease prediction in data mining using association rule mining and keyword based clustering algorithms. Int J Comput Appl. 2020 Jan 2; 42:(1):1–8. http://doi.org/10.1080/1206212X.2017.1396415
     [Google Scholar]
  18. Rao AB, Kiran JS, G P. Application of market–basket analysis on healthcare. Int J Syst Assur Eng Manag. 2023 Dec 1; 14:(4):924–9. http://doi.org/10.1007/s13198-021-01298-2
     [Google Scholar]
  19. Hossain MJ. Comparative analysis of ARIMA, SARIMAX, and random forest models for forecasting future GDP in relation to unemployment rate. Int J Manage Accounting Econ. 2023 Nov; 10:(11):924–37. http://doi.org/10.5281/zenodo.10473611
     [Google Scholar]
  20. Siamba S, Otieno A, Koech J. Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya. PLOS Digit Health. 2023 Feb 1; 2:(2):e0000084. http://doi.org/10.1371/journal.pdig.0000084
     [Google Scholar]
  21. Farhat H, Alinier G, El Aifa K, Makhlouf A, Gangaram P, Howland I, et al. Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort-based. Health Sci Rep. 2024 Apr 23; 7:(4):e2056. http://doi.org/10.1002/hsr2.2056
     [Google Scholar]
  22. de Anda HH, Moy HP. EMS ground transport safety. In StatPearls. Treasure Island (FL): StatPearls Publishing; 2024[cited 15 Aug 2024]. Available from:http://www.ncbi.nlm.nih.gov/books/NBK558971
     [Google Scholar]
  23. Sanddal TL, Sanddal ND, Ward N, Stanley L. Ambulance crash characteristics in the US defined by the popular press: A retrospective analysis. Emerg Med Int. 2010; 2010::525979. http://doi.org/10.1155/2010/525979
     [Google Scholar]
  24. Ito S, Asai H, Kawai Y, Suto S, Ohta S, Fukushima H. Factors associated with EMS on-scene time and its regional difference in road traffic injuries: A population-based observational study. BMC Emerg Med. 2022 Sep 15; 22:(1):160. http://doi.org/10.1186/s12873-022-00718-1
     [Google Scholar]
  25. Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: A narrative review. Acute Med Surg. 2022 Mar 1; 9:(1):e740. http://doi.org/10.1002/ams2.740
     [Google Scholar]
  26. Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, et al. Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Med Res. 2021 Aug 11; 8:(1):44. http://doi.org/10.1186/s40779-021-00338-z
     [Google Scholar]
/content/journals/10.5339/qmj.2025.75
Loading
Evaluating the outcome of Ambulances Collisions Review Panel in Middle Eastern environment using epidemiological supervised and unsupervised machine learning analyses
Qatar Medical Journal 2025, 75 (2025); https://doi.org/10.5339/qmj.2025.75
/content/journals/10.5339/qmj.2025.75
/content/journals/10.5339/qmj.2025.75
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Ambulance collision, ambulance safety, corrective measures, emergency medical services and machine learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error