Optimizing Interprofessional Trauma Care Pathways: A Systematic Review of Workflow Integration from Prehospital Stabilization to Definitive Disposition
Abstract
Background: Major trauma management demands seamless coordination across multiple specialized disciplines to mitigate the "golden hour" mortality risk. Fragmented care between prehospital and in-hospital phases remains a critical barrier to patient outcomes, underscoring the need for an integrated "trauma chain of survival." Aim: This narrative systematic review synthesizes evidence on interprofessional workflow optimization linking Emergency Medical Services (EMS), emergency nursing, respiratory therapy, clinical laboratory, hospital operations, sterile processing, and social work for adult major trauma patients. Methods: A systematic search of PubMed, CINAHL, Scopus, and Web of Science (2010–2024) identified 42 studies meeting inclusion criteria. Data were analyzed thematically to evaluate models of communication, resource allocation, and interdisciplinary synchronization. Results: Effective integration hinges on standardized communication protocols (e.g., digital handoff tools), co-located resuscitation teams, and real-time data sharing. Massive transfusion protocols activated by EMS, coupled with parallel processing by clinical labs, reduced time to transfusion by 33%. Social work early intervention decreased family-induced workflow disruptions by 28%. However, interoperability gaps between EMS and electronic health records persist. Conclusion: A structured, interprofessional workflow that emphasizes parallel processing and shared situational awareness significantly improves clinical efficiency and patient outcomes. Future implementation requires tailored training, interoperable health information technology, and trauma system policy reforms.
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References
AbiNader, M. A., Messing, J. T., Pizarro, J., Kappas Mazzio, A., Turner, B. G., & Tomlinson, L. (2023). Attending to our own trauma: Promoting vicarious resilience and preventing vicarious traumatization among researchers. Social Work Research, 47(4), 237-249. https://doi.org/10.1093/swr/svad016
Amin, T., Mobbs, R. J., Mostafa, N., Sy, L. W., & Choy, W. J. (2021). Wearable devices for patient monitoring in the early postoperative period: a literature review. Mhealth, 7, 50. https://doi.org/10.21037/mhealth-20-131
Bakhshaie, J., Fishbein, N. S., Woodworth, E., Liyanage, N., Penn, T., Elwy, A. R., & Vranceanu, A. M. (2023). Health disparities in orthopedic trauma: a qualitative study examining providers’ perspectives on barriers to care and recovery outcomes. Social work in health care, 62(6-7), 207-227. https://doi.org/10.1080/00981389.2023.2205909
Becker, C. D., Yang, M., Fusaro, M., Fry, M., & Scurlock, C. S. (2019). Optimizing tele-ICU operational efficiency through workflow process modeling and restructuring. Critical care explorations, 1(12), e0064. DOI: 10.1097/CCE.0000000000000064
Bhandarkar, P., Pal, R., Munivenkatappa, A., Roy, N., Kumar, V., & Agrawal, A. (2018). Distribution of laboratory parameters in trauma population. Journal of emergencies, trauma, and shock, 11(1), 10-14. DOI: 10.4103/JETS.JETS_70_17
Bruhn, M., Laugesen, H., Kromann-Larsen, M., Trevino, C. S., Eplov, L., Hjorthøj, C., & Carlsson, J. (2022). The effect of an integrated care intervention of multidisciplinary mental health treatment and employment services for trauma-affected refugees: study protocol for a randomised controlled trial. Trials, 23(1), 859. https://doi.org/10.1186/s13063-022-06774-z
Carsetti, A., Antolini, R., Casarotta, E., Damiani, E., Gasparri, F., Marini, B., ... & Donati, A. (2023). Shock index as predictor of massive transfusion and mortality in patients with trauma: a systematic review and meta-analysis. Critical Care, 27(1), 85. https://doi.org/10.1186/s13054-023-04386-w
Chan, K. S., Lo, H. Y., & Shelat, V. G. (2023). Carbon footprints in minimally invasive surgery: Good patient outcomes, but costly for the environment. World Journal of Gastrointestinal Surgery, 15(7), 1277. https://doi.org/10.4240/wjgs.v15.i7.1277
Gunderson, M. R., Florin, A., Price, M., & Reed, J. (2020). NEMSMA position statement and white paper: process and outcomes data sharing between EMS and receiving hospitals. Prehospital Emergency Care, 25(2), 307-313. https://doi.org/10.1080/10903127.2020.1792017
Hoonakker, P. L., Hose, B. Z., Carayon, P., Eithun, B. L., Rusy, D. A., Ross, J. C., ... & Kelly, M. M. (2022). Scenario-based evaluation of team health information technology to support pediatric trauma care transitions. Applied clinical informatics, 13(01), 218-229. DOI: 10.1055/s-0042-1742368
Landman, A. B., Rokos, I. C., Burns, K., Van Gelder, C. M., Fisher, R. M., Dunford, J. V., ... & Bogucki, S. (2011). An open, interoperable, and scalable prehospital information technology network architecture. Prehospital Emergency Care, 15(2), 149-157. https://doi.org/10.3109/10903127.2010.534235
Ludbrook, G., Grocott, M. P., Heyman, K., Clarke-Errey, S., Royse, C., Sleigh, J., & Solomon, L. B. (2023). Outcomes of postoperative overnight high-acuity care in medium-risk patients undergoing elective and unplanned noncardiac surgery. JAMA surgery, 158(7), 701-708. doi:10.1001/jamasurg.2023.1035
Mayock, D. E., Gogcu, S., Puia-Dumitrescu, M., Shaw, D. W., Wright, J. N., Comstock, B. A., ... & Widness, J. (2021). Association between term equivalent brain magnetic resonance imaging and 2-year outcomes in extremely Preterm infants: a report from the Preterm erythropoietin Neuroprotection trial cohort. The Journal of pediatrics, 239, 117-125. https://doi.org/10.1016/j.jpeds.2021.08.040
O'Connell, L. (2023). What do looked after children and young people (LACYP) identify as the key protective factors that promote their school engagement? (Doctoral dissertation, UCL (University College London)).
Patel, B. M., Reilly, J. P., Bhalla, A. K., Smith, L. S., Khemani, R. G., Jones, T. K., ... & Yehya, N. (2024). Association between age and mortality in pediatric and adult acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine, 209(7), 871-878. https://doi.org/10.1164/rccm.202310-1926OC
Perera, N., Birnie, T., Whiteside, A., Ball, S., & Finn, J. (2023). “If you miss that first step in the chain of survival, there is no second step”–Emergency ambulance call-takers’ experiences in managing out-of-hospital cardiac arrest calls. PloS one, 18(3), e0279521. https://doi.org/10.1371/journal.pone.0279521
Provis, J. M., Dubis, A. M., Maddess, T., & Carroll, J. (2013). Adaptation of the central retina for high acuity vision: cones, the fovea and the avascular zone. Progress in retinal and eye research, 35, 63-81. https://doi.org/10.1016/j.preteyeres.2013.01.005
Puzio, T. J., Kalkwarf, K., & Cotton, B. A. (2020). Predicting the need for massive transfusion in the prehospital setting. Expert Review of Hematology, 13(9), 983-989. https://doi.org/10.1080/17474086.2020.1803735
Rakhit, S., Nordness, M. F., Lombardo, S. R., Cook, M., Smith, L., & Patel, M. B. (2021, February). Management and challenges of severe traumatic brain injury. In Seminars in respiratory and critical care medicine (Vol. 42, No. 01, pp. 127-144). Thieme Medical Publishers, Inc. DOI: 10.1055/s-0040-1716493
Schilling, S., Armaou, M., Morrison, Z., Carding, P., Bricknell, M., & Connelly, V. (2022). Understanding teamwork in rapidly deployed interprofessional teams in intensive and acute care: A systematic review of reviews. Plos one, 17(8), e0272942. https://doi.org/10.1371/journal.pone.0272942
Sommer, A., Rehbock, C., Vos, C., Borgs, C., Chevalier, S., Doreleijers, S., ... & Krafft, T. (2022). Impacts and lessons learned of the first three COVID-19 waves on cross-border collaboration in the field of emergency medical services and Interhospital transports in the Euregio-Meuse-Rhine: a qualitative review of expert opinions. Frontiers in Public Health, 10, 841013. https://doi.org/10.3389/fpubh.2022.841013
Stewart, B., Hollis, S., Amato, S. S., Bulger, E., Mock, C., & Reynolds, T. (2019). Trauma care and development assistance: opportunities to reduce the burden of injury and strengthen health systems. Bulletin of the World Health Organization, 97(5), 371. https://doi.org/10.2471/BLT.18.213074
Stübig, T., Suero, E., Zeckey, C., Min, W., Janzen, L., Citak, M., ... & Gaulke, R. (2013). Improvement in the workflow efficiency of treating non-emergency outpatients by using a WLAN-based real-time location system in a level I trauma center. Journal of the American Medical Informatics Association, 20(6), 1132-1136. https://doi.org/10.1136/amiajnl-2012-001307
Velasco, J., Sanmartin, F. J., Galvez-Lara, M., Cuadrado, F., & Moriana, J. A. (2023). Psychological effects of professional exposure to trauma and human suffering: systematic review and meta-analysis. Trauma, Violence, & Abuse, 24(3), 1656-1676. https://doi.org/10.1177/15248380221074314
Viswanathan, K. P., Bass, R., Wijetunge, G., & Altevogt, B. M. (2012). Rural Mass Casualty Preparedness and Response: The Institute of Medicine's Forum on Medical and Public Health Preparedness for Catastrophic Events. Disaster Medicine and Public Health Preparedness, 6(3), 297-302. doi:10.1001/dmp.2012.38
Watts, S. A., Smith, J. E., Woolley, T., Rickard, R. F., Gwyther, R., & Kirkman, E. (2023). Resuscitation with whole blood or blood components improves survival and lessens the pathophysiological burden of trauma and haemorrhagic shock in a pre-clinical porcine model. European Journal of Trauma and Emergency Surgery, 49(1), 227-239. https://doi.org/10.1007/s00068-022-02050-6
Weediyawatta, P. K. G. P. (2022). Living with information silos: developing a framework to minimise the burden of “Information Silos” to strengthen the preventive health sector (Doctoral dissertation, Postgraduate Institute of Medicine).
Williams, D. M., et al. (2022). Parallel processing in trauma resuscitation. Annals of Emergency Medicine, 79(5), 456-467.
Wu, X., Cap, A. P., Bynum, J. A., Chance, T. C., Darlington, D. N., & Meledeo, M. A. (2023). Prolyl hydroxylase domain inhibitor: a potential pre-hospital pharmaceutical intervention for trauma and hemorrhagic shock. https://doi.org/10.21203/rs.3.rs-3411470/v1
Zaleski, J. (2018). Big data for predictive analytics in high acuity health settings. In Big data for the greater good (pp. 51-100). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-93061-9_4
Authors
Copyright (c) 2024 Mansour Ibrahim Ahmed Otudi, Ahmed Awad Farhan Alanezi, Mariam Abdullah Ali Alogdi, Naif Muneer Halil Alotaibi, Meshari Rushaydan Almutairi, Kaled Ali Aloneze, Mohammed Saudi, Maryam Mohammed Alqadhib, Saad Sanaitan almutairi, Kholoud Mohammed Abdu Mashragi, Mohammed Saleh Najdi, Hassan Mohd Alotibi, Mohammed Hassan Alfahadi, Fahad Jaber Alfahadi
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