Identifying the Opportunities and Challenges of Using Artificial Intelligence in the Diagnosis, Initial Management, and Triage of Trauma Patients in Pre-Hospital Emergency Care: A Qualitative Meta-Synthesis Study

Rahimi, Alireza

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Identifying the Opportunities and Challenges of Using Artificial Intelligence in the Diagnosis, Initial Management, and Triage of Trauma Patients in Pre-Hospital Emergency Care: A Qualitative Meta-Synthesis Study

Alireza Rahimi ^*

Student Research Committee, Alborz University of Medical Sciences, Karaj, Iran, rahimi.b@ut.ac.ir , rahimi.b@ut.ac.ir

Abstract: (14 Views)

Background: This research aimed to analyze and identify the opportunities and challenges of applying artificial intelligence (AI) in the pre-hospital emergency setting for the diagnosis, initial management, and triage of trauma patients. The objective is to provide a practical roadmap for the policymaking and safe implementation of this technology.

Methods: This qualitative study was conducted using a meta‑synthesis (research synthesis) approach. A systematic search was performed in major scientific databases to identify original articles published between 2015 and 2026 (1394–1404 in the Iranian calendar). After screening the studies based on the PRISMA guidelines, data from the eligible articles were extracted using a structured checklist and analyzed through thematic content analysis (TCA). The trustworthiness of the findings was assessed and confirmed according to Lincoln and Guba’s criteria.

Results: Out of a total of 221 retrieved articles, 24 eligible studies were included in the analysis. The findings indicated that AI application, on one hand, possesses significant potential to transform clinical and operational processes. These potentials include enhancing triage accuracy, intelligent patient monitoring, decision support, logistics optimization, and efficient documentation. On the other hand, realizing these opportunities is contingent upon overcoming a wide range of obstacles, including data and information issues, infrastructural and technical limitations, algorithmic constraints, human and organizational barriers, and ethical and legal considerations.

Conclusion: Although AI, by enabling intelligent monitoring and improving triage accuracy, has the potential to revolutionize the management of the “golden hour” in trauma care, its implementation is confronted with five multifaceted categories of challenges. The results indicate that the successful translation of these opportunities into sustainable clinical practice cannot be achieved solely through technological advancement; rather, it requires systemic transformation, an integrated approach, and the adoption of evidence‑based policies to simultaneously address infrastructural, human, and legal barriers.

Keywords: Artificial intelligence, trauma, prehospital emergency care, meta‑synthesis, opportunities, challenges

Acknowledgments: The author thanks the Student Research and Technology Committee of Alborz University of Medical Sciences for providing research facilities.

Availability of data and materials: The data generated and analyzed during this study are available from the author upon request.

Competing interests: The author declares no competing interests.

Consent for publication: Not applicable, as this study used secondary data.

Ethics approval and consent to participate: This study was conducted in accordance with the principles of the Declaration of Helsinki.

Funding: This research received no financial support.

Author contributions: Alireza Rahimi was solely responsible for all aspects of this study, including conceptualization, study implementation, data analysis, and manuscript preparation.

Full-Text [PDF 1680 kb] (4 Downloads)

Type of Study: Original | Subject: Special

References

1. Alberdi, F., García, I., Atutxa, L., & Zabarte, M. (2014). Epidemiology of severe trauma. Medicina Intensiva (English Edition), 38(9), 580-588. [DOI:10.1016/j.medine.2014.06.002]

2. Toy, J., Warren, J., Wilhelm, K., Putnam, B., Whitfield, D., Gausche‐Hill, M., ... & Goolsby, C. (2024). Use of artificial intelligence to support prehospital traumatic injury care: A scoping review. JACEP Open, 5(5), e13251. [DOI:10.1002/emp2.13251]

3. Mercer, S., Park, C., & Tarmey, N. T. (2015). Human factors in complex trauma. BJA Education, 15(5), 231-236. [DOI:10.1093/bjaceaccp/mku043]

4. Voskens, F. J., van Rein, E. A., van der Sluijs, R., Houwert, R. M., Lichtveld, R. A., Verleisdonk, E. J., ... & van Heijl, M. (2018). Accuracy of prehospital triage in selecting severely injured trauma patients. JAMA surgery, 153(4), 322-327. [DOI:10.1001/jamasurg.2017.4472]

5. Fuller, G., Pandor, A., Essat, M., Sabir, L., Buckley-Woods, H., Chatha, H., ... & Turner, J. (2021). Diagnostic accuracy of prehospital triage tools for identifying major trauma in elderly injured patients: a systematic review. Journal of Trauma and Acute Care Surgery, 90(2), 403-412. [DOI:10.1097/TA.0000000000003039]

6. Chee, M. L., Chee, M. L., Huang, H., Mazzochi, K., Taylor, K., Wang, H., ... & Liu, N. (2023). Artificial intelligence and machine learning in prehospital emergency care: A scoping review. Iscience, 26(8). [DOI:10.1016/j.isci.2023.107407]

7. Adebayo, O., Bhuiyan, Z. A., & Ahmed, Z. (2023). Exploring the effectiveness of artificial intelligence, machine learning and deep learning in trauma triage: A systematic review and meta-analysis. Digital health, 9, 20552076231205736. [DOI:10.1177/20552076231205736]

8. Abe, D., Inaji, M., Hase, T., Takahashi, S., Sakai, R., Ayabe, F., ... & Maehara, T. (2022). A prehospital triage system to detect traumatic intracranial hemorrhage using machine learning algorithms. JAMA Network Open, 5(6), e2216393-e2216393. [DOI:10.1001/jamanetworkopen.2022.16393]

9. Amiot, F., & Potier, B. (2025). Artificial intelligence (AI) and emergency medicine: balancing opportunities and challenges. JMIR Medical Informatics, 13(1), e70903. [DOI:10.2196/70903]

10. Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Barriers to and Facilitators of Technology Adoption in Emergency Departments: A Comprehensive Review. International Journal of Environmental Research and Public Health, 22(4), 479. [DOI:10.3390/ijerph22040479]

11. Clark, M., & Severn, M. (2023). Artificial intelligence in prehospital emergency health care. Canadian Journal of Health Technologies, 3(8). [DOI:10.51731/cjht.2023.712]

12. Mallon, O., Lippert, F., & Pilot, E. (2025). Artificial intelligence in prehospital emergency care systems in low-and middle-income countries: cure or curiosity? Insights from a qualitative study. Frontiers in Public Health, 13, 1632029. [DOI:10.3389/fpubh.2025.1632029]

13. Short, E. C. (1931). Curriculum studies methodology (M. Mehrmohammadi et al., Trans., 2008 ed.). Tehran: SAMT Publications. [In Persian].

14. Sandelowski, M., & Barroso, J. (2006). Handbook for synthesizing qualitative research. Springer Publishing Company.

15. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Beverly Hills, CA: Sage Publications, Inc. [DOI:10.1016/0147-1767(85)90062-8]

16. Kamali, Y. (2017). The methodology of meta synthesis and implications for public policy. Politics Quarterly, 47(3), 721-736. [In Persian].

17. Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2019). Machine learning approaches for predicting critical outcomes in emergency department triage. Annals of Emergency Medicine, 73(4), 416-425.

18. Spangler, D., Hermansson, T., Smekal, D., & Blomberg, H. (2020). A machine learning model for prediction of trauma patient mortality and injury severity using prehospital data. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28, 65.

19. Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer‑Cooley, O., Pestrue, J., Phillips, M., Konye, J., & Penoza, C. (2020). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 180(8), 1065-1070. [DOI:10.1001/jamainternmed.2021.2626]

20. Jang, Y., Kim, K., & Lee, S. (2021). Dynamic ambulance redeployment using reinforcement learning for emergency medical services. Computers & Industrial Engineering, 157, 107323.

21. Lau, Y., Cheung, N., & Wong, H. (2023). Predicting ambulance transport time using advanced regression models to optimize trauma team activation. Prehospital Emergency Care, 27(2), 250-258.

22. Hwang, J., Kim, Y., Lee, J., & Park, S. (2022). Artificial intelligence-assisted point‑of‑care ultrasound for rapid trauma assessment: A clinical evaluation study. Ultrasound in Medicine & Biology, 48(7), 1300-1309.

23. Suh, M., Park, J., & Kim, H. (2021). Augmented reality-assisted emergency procedures using artificial intelligence guidance. IEEE Journal of Biomedical and Health Informatics, 25(12), 4572-4580.

24. Goss, F., Zhou, L., Weiner, M., & Zhou, Y. (2021). Natural language processing for clinical documentation improvement and automated medical record abstraction. Journal of Biomedical Informatics, 118, 103789.

25. Eppich, W., Cheng, A., & Grant, V. (2015). Promoting excellence and reflective learning in simulation (PEARLS): Development and rationale for a blended approach to healthcare simulation debriefing. Simulation in Healthcare, 10(2), 106-115. [DOI:10.1097/SIH.0000000000000072]

26. Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544-1547. [DOI:10.1001/jamainternmed.2018.3763]

27. Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019). Why digital medicine depends on interoperability. npj Digital Medicine, 2, 79. [DOI:10.1038/s41746-019-0158-1]

28. Scott, R. E., Mars, M., & Hebert, M. (2020). Telehealth in emergency and disaster response: Challenges and opportunities. Journal of Telemedicine and Telecare, 26(10), 579-585.

29. Alwasel, K., Jones, A., & Kumar, N. (2021). Edge computing for healthcare monitoring systems: Challenges and opportunities in mobile medical environments. IEEE Access, 9, 128-142.

30. Futoma, J., Simons, M., Panch, T., Doshi‑Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, 2(9), e489-e492. [DOI:10.1016/S2589-7500(20)30186-2]

31. Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What clinicians want: Contextualizing explainable machine learning for clinical end use. npj Digital Medicine, 2, 76.

32. Sendak, M. P., Gao, M., Nichols, M., Lin, A., & Balu, S. (2020). Machine learning in health care: A critical appraisal of challenges and opportunities. npj Digital Medicine, 3, 17.

33. Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517-518. [DOI:10.1001/jama.2017.7797]

34. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. [DOI:10.1126/science.aax2342]

35. Price, W. N., Gerke, S., & Cohen, I. G. (2019). Potential liability for physicians using artificial intelligence. JAMA, 322(18), 1765-1766. [DOI:10.1001/jama.2019.15064]

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