The Technologically Augmented First Responder: A Review of Wearables, Telemedicine, and Decision Support in Prehospital and Dental Emergency Care
Abstract
Background: The paradigm of prehospital and out-of-hospital emergency care is undergoing a fundamental transformation, driven by the integration of advanced technologies into the hands of first responders. In both traditional emergency medical services (EMS) and specialized settings like dental trauma care, the ability to capture, transmit, and interpret critical patient data at the point of first contact can dramatically alter clinical trajectories. Aim: This narrative review aims to examine the deployment and impact of three core technological classes in emergency response. It examines their application across EMS and dental emergency contexts, analyzing clinical, operational, and human factors. Methods: A comprehensive search of PubMed, Scopus, IEEE Xplore, and CINAHL databases was conducted. Results: Evidence indicates that wearable monitors improve the detection of occult physiologic deterioration; telemedicine reduces time-to-critical interventions in stroke and trauma; and mobile CDS improves protocol adherence. However, successful implementation is constrained by nursing workflows in receiving facilities that are unprepared for the data deluge, pharmacy challenges in validating dynamic dosing guidance, and significant informatics hurdles related to data interoperability, security, and alert fatigue. Conclusion: Technology has the potential to evolve the first responder from a primarily transport-focused role to a diagnostically enabled, tele-supported field clinician. Realizing this potential requires a parallel evolution in clinical workflows, interdisciplinary training, and health information architectures designed for real-time, low-latency data fusion across the continuum of emergency care.
Full text article
References
Bashiri, A., Savareh, B. A., & Ghazisaeedi, M. (2019). Promotion of prehospital emergency care through clinical decision support systems: opportunities and challenges. Clinical and Experimental Emergency Medicine, 6(4), 288. https://doi.org/10.15441/ceem.18.032
Becker, D. E., & Haas, D. A. (2007). Management of complications during moderate and deep sedation: respiratory and cardiovascular considerations. Anesthesia progress, 54(2), 59-69. https://doi.org/10.2344/0003-3006(2007)54[59:MOCDMA]2.0.CO;2
Chan, J., Rea, T., Gollakota, S., & Sunshine, J. E. (2019). Contactless cardiac arrest detection using smart devices. NPJ digital medicine, 2(1), 52. https://doi.org/10.1038/s41746-019-0128-7
Chiong, X. H., Wong, Z. Z., Lim, S. M., Ng, T. Y., & Ng, K. T. (2022). The use of cerebral oximetry in cardiac surgery: A systematic review and meta-analysis of randomized controlled trials. Annals of Cardiac Anaesthesia, 25(4), 384-398. DOI: 10.4103/aca.aca_149_21
Choi, A., Choi, S. Y., Chung, K., Chung, H. S., Song, T., Choi, B., & Kim, J. H. (2023). Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Scientific Reports, 13(1), 8561. https://doi.org/10.1038/s41598-023-35617-3
Gharahbaghian, L., Anderson, K. L., Lobo, V., Huang, R. W., Poffenberger, C. M., & Nguyen, P. D. (2017). Point-of-care ultrasound in austere environments: a complete review of its utilization, pitfalls, and technique for common applications in austere settings. Emergency Medicine Clinics, 35(2), 409-441. https://doi.org/10.1016/j.emc.2016.12.007
Hekman, D. J., Cochran, A. L., Maru, A. P., Barton, H. J., Shah, M. N., Wiegmann, D., ... & Patterson, B. W. (2023). Effectiveness of an emergency department–based machine learning clinical decision support tool to prevent outpatient falls among older adults: protocol for a quasi-experimental study. JMIR research protocols, 12(1), e48128. https://doi.org/10.2196/48128
Itrat, A., Taqui, A., Cerejo, R., Briggs, F., Cho, S. M., Organek, N., ... & Uchino, K. (2016). Telemedicine in prehospital stroke evaluation and thrombolysis: taking stroke treatment to the doorstep. JAMA neurology, 73(2), 162-168. doi:10.1001/jamaneurol.2015.3849
Lazarus, G., Permana, A. P., Nugroho, S. W., Audrey, J., Wijaya, D. N., & Widyahening, I. S. (2020). Telestroke strategies to enhance acute stroke management in rural settings: a systematic review and meta‐analysis. Brain and behavior, 10(10), e01787. https://doi.org/10.1002/brb3.1787
Levin, L., Day, P. F., Hicks, L., O'Connell, A., Fouad, A. F., Bourguignon, C., & Abbott, P. V. (2020). International Association of Dental Traumatology guidelines for the management of traumatic dental injuries: General introduction. Dental Traumatology, 36(4), 309-313. https://doi.org/10.1111/edt.12574
Lieneck, C., McLauchlan, M., & Phillips, S. (2023, November). Healthcare cybersecurity ethical concerns during the COVID-19 global pandemic: a rapid review. In Healthcare (Vol. 11, No. 22, p. 2983). MDPI. https://doi.org/10.3390/healthcare11222983
Lin, G. S. S., Koh, S. H., Ter, K. Z., Lim, C. W., Sultana, S., & Tan, W. W. (2022). Awareness, knowledge, attitude, and practice of teledentistry among dental practitioners during COVID-19: a systematic review and meta-analysis. Medicina, 58(1), 130. https://doi.org/10.3390/medicina58010130
Lumley, H. A., Flynn, D., Shaw, L., McClelland, G., Ford, G. A., White, P. M., & Price, C. I. (2020). A scoping review of pre-hospital technology to assist ambulance personnel with patient diagnosis or stratification during the emergency assessment of suspected stroke. BMC emergency medicine, 20(1), 30. https://doi.org/10.1186/s12873-020-00323-0
Majumder, S., Mondal, T., & Deen, M. J. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130
Martin, L. T., Nelson, C., Yeung, D., Acosta, J. D., Qureshi, N., Blagg, T., & Chandra, A. (2022). The issues of interoperability and data connectedness for public health. Big data, 10(S1), S19-S24. https://doi.org/10.1089/big.2022.0207
Mazor, I., Heart, T., & Even, A. (2016). Simulating the impact of an online digital dashboard in emergency departments on patients length of stay. Journal of Decision systems, 25(sup1), 343-353. https://doi.org/10.1080/12460125.2016.1187422
McCartan, D., Lee, S., Bejleri, J., Murphy, P., Hickey, A., & Williams, D. (2023). The impact of telemedicine enabled pre-hospital triage in acute stroke–a protocol for a mixed methods systematic review. HRB Open Research, 5, 32. https://doi.org/10.12688/hrbopenres.13514.2
Mirjalali, S., Peng, S., Fang, Z., Wang, C. H., & Wu, S. (2022). Wearable sensors for remote health monitoring: potential applications for early diagnosis of Covid‐19. Advanced materials technologies, 7(1), 2100545. https://doi.org/10.1002/admt.202100545
Mori, H., Maeda, A., Akashi, Y., Ako, J., Ikari, Y., Ebina, T., ... & Suzuki, H. (2021). The impact of pre-hospital 12-lead electrocardiogram and first contact by cardiologist in patients with ST-elevation myocardial infarction in Kanagawa, Japan. Journal of Cardiology, 78(3), 183-192. https://doi.org/10.1016/j.jjcc.2021.04.001
Osterwalder, J., Polyzogopoulou, E., & Hoffmann, B. (2023). Point-of-care ultrasound—history, current and evolving clinical concepts in emergency medicine. Medicina, 59(12), 2179. https://doi.org/10.3390/medicina59122179
Ponce, B. A., Brabston, E. W., Zu, S., Watson, S. L., Baker, D., Winn, D., ... & Shenai, M. B. (2016, August). Telemedicine with mobile devices and augmented reality for early postoperative care. In 2016 38th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4411-4414). IEEE. https://doi.org/10.1109/EMBC.2016.7591705
Prgomet, M., Li, L., Niazkhani, Z., Georgiou, A., & Westbrook, J. I. (2017). Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. Journal of the American Medical Informatics Association, 24(2), 413-422. https://doi.org/10.1093/jamia/ocw145
Sarpourian, F., Marzaleh, M. A., Aghda, S. A. F., & Zare, Z. (2023). Application of telemedicine in the ambulance for stroke patients: a systematic review. Prehospital and Disaster Medicine, 38(6), 774-779. doi:10.1017/S1049023X23006519
Siebert, J. N., Bloudeau, L., Combescure, C., Haddad, K., Hugon, F., Suppan, L., ... & Berger, C. (2021). Effect of a mobile app on prehospital medication errors during simulated pediatric resuscitation: a randomized clinical trial. JAMA network open, 4(8), e2123007-e2123007. doi:10.1001/jamanetworkopen.2021.23007
Singh, D., Nagaraj, S., Mashouri, P., Drysdale, E., Fischer, J., Goldenberg, A., & Brudno, M. (2022). Assessment of machine learning–based medical directives to expedite care in pediatric emergency medicine. JAMA Network Open, 5(3), e222599-e222599. doi:10.1001/jamanetworkopen.2022.2599
van de Burgt, B. W., Wasylewicz, A. T., Dullemond, B., Grouls, R. J., Egberts, T. C., Bouwman, A., & Korsten, E. M. (2023). Combining text mining with clinical decision support in clinical practice: a scoping review. Journal of the American Medical Informatics Association, 30(3), 588-603. https://doi.org/10.1093/jamia/ocac240
Wollner, E. A., Nourian, M. M., Bertille, K. K., Wake, P. B., Lipnick, M. S., & Whitaker, D. K. (2023). Capnography—an essential monitor, everywhere: a narrative review. Anesthesia & Analgesia, 137(5), 934-942. DOI: 10.1213/ANE.0000000000006689
Wong, Z. Z., Chiong, X. H., Chaw, S. H., Hashim, N. H. B. M., Abidin, M. F. B. Z., Yunus, S. N. B., ... & Ng, K. T. (2022). The use of cerebral oximetry in surgery: a systematic review and meta-analysis of randomized controlled trials. Journal of Cardiothoracic and Vascular Anesthesia, 36(7), 2002-2011. https://doi.org/10.1053/j.jvca.2021.09.046
Winburn, A. S., Brixey, J. J., Langabeer, J., & Champagne-Langabeer, T. (2018). A systematic review of prehospital telehealth utilization. Journal of telemedicine and telecare, 24(7), 473-481. https://doi.org/10.1177/1357633X17713140
Wright, A., Hickman, T. T. T., McEvoy, D., Aaron, S., Ai, A., Andersen, J. M., ... & Bates, D. W. (2016). Analysis of clinical decision support system malfunctions: a case series and survey. Journal of the American Medical Informatics Association, 23(6), 1068-1076. https://doi.org/10.1093/jamia/ocw005
Wright, A., Nelson, S., Rubins, D., Schreiber, R., & Sittig, D. F. (2022). Clinical decision support malfunctions related to medication routes: a case series. Journal of the American Medical Informatics Association, 29(11), 1972-1975. https://doi.org/10.1093/jamia/ocac150
Authors
Copyright (c) 2024 Mashari Agaig A Alsharari, Mohammed Hameed Salamah Alsharari, Bandar Musaid Alsarhani, Mashael Ayed Mujahid Alanazi, Majed Thani A Alsharari, Ahmed Sahlan Qabil Alsharari, Norah Mohammed Sadly, Wafa Saeed Aldosari, Eman ALhumidi AlEnazi, Joman Salem Alhawiti, Nawal Mesfer Almuraya, Muhza Khalid Sahir Albishry
This work is licensed under a Creative Commons Attribution 4.0 International License.