Computer implementation of intelligent medical rescue operations management system in mass casualty incidents

• Autorzy: Tomczyk Łukasz , Kulesza Zbigniew

Identyfikatory

DOI

10.5604/01.3001.0054.9327

• Języki publikacji: EN

Abstrakty

EN

Due to rapid development of biocybernetics and technical computer science, dispatching of medical emergency services can be significantly improved. Thus the effectiveness of conducting rescue operations during mass casualty incidents (MCI) can be increased. Selected optimization methods and the expert system have been used to develop an Intelligent Medical Rescue Operations Management System (IMROMS). This system is the basis for the real emergency medical suport system that could be implemented in the emergency units. In the current paper IMROMS hardware and software implementation problems have been discussed, including the analysis of the current state of information-telecommunication technologies (ICT) support for the rescue system in provinces of Poland. The IMROMS consists of computer support for the following emergency workstations: Data Communication System of Provincial Emergency System (DCSPES), Medical Emergency Coordinator (MEC) and Medical Emergency Dispatcher (MED) workstations, Medical Emergency Supervisor (MES) workstation, Casualty Health Monitor (CHM) mobile devices, Hospital Emergency Department (HED) workstations and Medical Rescue Teams (MRTs) workstations. Computer software implements the following modules: optimization module (IMROMS-OM), expert system (IMROMS-ES), geographical information system – (IMROMSGIS). Optimization module has been developed on the basis of the computer simulator for optimal decision-making in medical rescue operations (CSMRO). The IMROMS software was developed to carry out hypothetical rescue operations with the support of a computer, the operation of which was tested under near-real conditions during the 14th Warmia and Mazury Championship in Medical Rescue, held in Olsztyn, Poland, in 2016. The Championships were attended by 20 medical rescue teams from all over Poland. The competition scenario assumed a specific post-accident conditio of the victims, their injuries and the status of their basic vital signs. Participants performed initial segregation according to the START algorithm, first without computer support – in the traditional way - and then using IMROMS. Identification took place at the scene and involved entering health data such as the status of basic vital functions, including respiratory characteristics, blood pressure, respiratory effort and capillary return and the type of injury suffered by the victim. Data was entered by paramedics using the Casualty Health Monitor (CHM) mobile device. A MED/MEC computer support station using the CSMRO optimization module provided a solution based on this to assign HED and MRT to MCI victims. The time spent by rescuers at the scene of an MCI incident during initial segregation was significantly reduced and, consequently, the time taken to carry out actions in the subsequent individual stages of the rescue operation was also shorter.

Słowa kluczowe

EN

mass casualty incident; emergency medicine; computer support; optimization methods; medical rescue operations management

• Wydawca: Szkoła Główna Służby Pożarniczej
• Czasopismo: Zeszyty Naukowe SGSP / Szkoła Główna Służby Pożarniczej
• Rocznik: 2024
• Tom: Nr 92 (tom 1)
• Strony: 193--218
• Opis fizyczny: Bibliogr. 51 poz., rys., tab.

Twórcy

autor

Tomczyk Łukasz

• University of Warmia and Mazury in Olsztyn, Faculty of Technical Sciences, Olsztyn, Poland

• https://orcid.org/0000-0002-6915-6180

autor

Kulesza Zbigniew

• Bialystok University of Technology, Faculty of Electrical Engineering, Bialystok, Poland

Bibliografia

• 1. Adler, C., Krüsmann, M., Greiner-Mai, T., Donner, A., Chaves, J.M., Estrem, À.V., (2011, May). IT-supported management of mass casualty incidents: the e-triage project. Proceedings of the 8th International ISCRAM Conference, pp. 1–5, Lisbon, Portugal.
• 2. Apiratwarakul, K., Cheung, L., Tiamkao, S., Phungoen, P., Tientanopajai, K., Taweepworadej, W., Kanarkard, W., Ienghong, K., (2022). Smart Glasses: A New Tool for Assessing the Number of Patients in Mass-Casualty Incidents. Prehospital and Disaster Medicine, 37(4), 480–484.
• 3. Bari, L.F., Ahmed, I., Ahamed, R., Zihan, T.A., Sharmin, S., Pranto, A.H., & Islam, M.R., (2023). Potential Use of Artificial Intelligence (AI) in Disaster Risk and Emergency Health Management: A Critical Appraisal on Environmental Health. Environmental Health Insights, 17.
• 4. Bazyar, J., Farrokhi, M., Khankeh, H., (2019). Triage systems in mass casualty incidents and disasters: A review study with a worldwide approach. Open Access Maced J. Med. Sci., 7, 482–494.
• 5. Bolduc, C., Maghraby, N., Fok, P., Luong, T., Homier, V., (2018). Comparison of Electronic Versus Manual Mass-Casualty Incident Triage. Prehospital and Disaster Medicine. 33(3), 273–278.
• 6. Boltin, N., Valdes, D., Culley, J., (2018). Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study. JMIR Mhealth and Uhealth, 6(6).
• 7. Borowicz, A., Kucap, M., (2016). Analysis of accident procedures multiple/mass casualty. Na Ratunek, (2), 32–34.
• 8. Challen, K., Walter, D., (2013). Major incident triage: Comparative validation using data from 7th July bombings. Injury, 44, 629–633.
• 9. Chenais, G., Lagarde, E., & Gil-Jardiné, C., (2023). Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges. Journal of Medical Internet Research, 25.
• 10. Coskun, T., Benzina, A., Artinger, E., Binder, C., Klinker, G., (2012, January). Usercentered development of ui elements for selecting items on a digital map designed for heavy rugged tablet pcs in mass casualty incidents. IHI ‘12 Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 151–160. Miami, Florida, USA.
• 11. Craigie, R.J., Farrelly, P.J., Santos, R., Smith, S.R., Pollard, J.S., & Jones, D.J., (2020). Manchester Arena bombing: lessons learnt from a mass casualty incident. BMJ Military Health, 166(2), 72–75.
• 12. Damaševičius, R., Bacanin, N., Misra, S., (2023). From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management. Journal of Sensor and Actuator Networks, 12(3), 41.
• 13. Dean, M.D., Nair, S.K., (2014). Mass-casualty triage: Distribution of victims to multiple hospitals using the save model. Eur. J. Oper. Res., 238, 363–373.
• 14. Fernando, Y.S., (2023). Revolutionizing Emergency Response and Disaster Management with AI: Creating Intelligent Systems for Triage, Resource Allocation, and Situational Awareness during Healthcare Crises. International Journal of Applied Machine Learning and Computational Intelligence, 13(12), 11–20.
• 15. Follmann, A., Ohligs, M., Hochhausen, N., Beckers, S.K., Rossaint, R., Czaplik, M., (2019). Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine. Journal of Medical Internet Research, 21(1).
• 16. Follmann, A., Ruhl, A., Gösch, M., Felzen, M., Rossaint, R., Czaplik, M., (2021). Augmented Reality for Guideline Presentation in Medicine: Randomized Crossover Simulation Trial for Technically Assisted Decision-making. JMIR Mhealth Uhealth. 9(10).
• 17. Ganz, A., Schafer, J.M., Yang, Z., Yi, J., Lord, G., Ciottone, G., (2014, November). Mobile DIORAMA-II: Infrastructure less information collection system for mass casualty incidents. Proceedings of the 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, pp. 2682–2685. Chicago, IL, USA.
• 18. Holgersson, A., Björnstig, U., (2014). Mass-casualty attacks on public transportation. Journal of Transportation Security, 7 (1), 1–16.
• 19. Iyengar, K.P., Venkatesan, A.S., Jain, V.K., (2023). Shashidhara, M.K.; Elbana, H.; Botchu, R. Risks in the management of polytrauma patients: Clinical insights. Orthop. Res., 15, 27–38.
• 20. Jain, T., Sibley, A., Stryhn, H., Hubloue, I., (2018). Comparison of unmanned aerial vehicle technology assisted triage versus standard practice in triaging casualties by paramedic students in a mass-casualty incident scenario. Prehospital and Disaster Medicine. 33(4), 375–380
• 21. Kosashvili, Y., Loebenberg, M.I., Lin, G., Peleg, K., Zvi, F., Kluger, Y., Blumenfeld, A., (2009). Medical consequences of suicide bombing mass casualty incidents: the impact of explosion setting on injury patterns. Injury, 40(7), 698–702.
• 22. Majchrzak, T.A., Noack, Neuhaus, P., Ückert, F., (2013). Supporting the allocation of traumatized patients with a decision support system. In: Murray, J.E. (ed.), Using Social and Information Technologies for Disaster and Crisis Management, Chapter 11, pp. 166–181.
• 23. Mentler, T., Herczeg, M., (2015). Interactive cognitive artefacts for enhancing situation awareness of incident commanders in mass casualty incidents. Journal of Interaction Science, 3(7), 1–7.
• 24. Nistor, M.S., Pham, T.S., Pickl, S., et al., (2020, October). A Concise Review of AIBased Solutions for Mass Casualty Management. CEUR Workshop Proceedings, 2805, Kherson, Ukraine.
• 25. Olivia, D., Attigeri, G., Saxena, A., (2024). Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction. Int. J. Inf. Technol., DOI:10.1007/s41870-024-02052-0
• 26. Petrella, R.J., (2024). The AI Future of Emergency Medicine. Annals of Emergency Medicine, 84(2), 139–153.
• 27. Piliuk, K, Tomforde, S., (2023). Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform, 180.
• 28. Pino, E.J., Curtis, D., Stair, T.O., Guttag, J.V., Ochno-Machado, L., (2012). Wireless Patient Monitoring in a Clinical Setting. In: Yuce, M.R., Khan, J. (eds), Wireless Body Area Networks: Technology, Implementation, and Applications, 19–40.
• 29. Podgorski, M., Nadolny, K., (2016). Procedures for dealing with a mass casualty incident. Unification of actions at the scene of an incident, with particular reference to the paramedics. Na Ratunek, 2, 25–31.
• 30. Postma, I.L., Weel, H., Heetveld, M.J., van der Zande, I., Bijlsma, T.S., Bloemers, F.W., Goslings, J.C., (2013). Mass casualty triage after an airplane crash near Amsterdam. Injury, 44, 1061–1067.
• 31. Režek, P., Žvanut, B., (2024). Towards optimal decision making in mass casualty incidents management through ICT: A systematic review. International Journal of Disaster Risk Reduction, 102.
• 32. Rodriguez, D., Heuer, S., Guerra, A., Stork, W., Weber, B., Eichler, M., (2014, November). Towards automatic sensor-based triage for individual remote monitoring during mass casualty incidents. 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 544–551. Belfast, UK.
• 33. Saremi, A., Abbasi, B., Karimi-MansoorAbad, E., Ashourian, Y., (2023). Revolutionizing Emergency Medicine: A Comprehensive Review of Artificial Intelligence Applications and Their Impact. Sarem Journal of Medical Research, 8(3), 145–156.
• 34. Schmollinger, M., Gerstner, J., Stricker, E. et al., (2024). Evaluation of an App-Based Mobile Triage System for Mass Casualty Incidents: Within-Subjects Experimental Study. Journal of Medical Internet Research, 26.
• 35. Tahernejad, A., Sahebi, A., Abadi, A.S.S. et al., (2024). Application of artificial intelligence in triage in emergencies and disasters: a systematic review. BMC Public Health, 24, 3203.
• 36. Tomczyk L., Kulesza Z. (2016). A method of prioritizing victims of a mass casualty event for managing medical rescue operations. Control and Cybernetics, 45(3), 355–370.
• 37. Tomczyk, L., Kulesza, Z., (2023). Multiple-Criteria Decision-Making for Medical Rescue Operations during Mass Casualty Incidents. Appl. Sci., 13.
• 38. Ullah, F., Khelil, A., Sheikh, A.A., Felemban, E. Bojan, H.M.A., (2013, October). Towards automated self-tagging in emergency health cases. Proceedings of the IEEE 15th International Conference on e-Health Networking, Applications & Services (Healthcom), pp. 658–663. Lisbon, Portugal.
• 39. Yang, D., Schafer, J., Wang, S., Ganz, A., (2014, August). Autonomous mobile platform for enhanced situational awareness in mass casualty incidents. Proceedings of the 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC), pp. 898–901. Chicago, Illinois, USA.
• 40. Zuratynski, P. et al., (2018). The management of a mass casualty incident/disaster by a medical dispatcher in a centralized medical dispatch center. Borgis – Postępy Nauk Medycznych, 5, 279–283.
• 41. Digital solution for efficient operational management by rescue services, fire brigades and civil protection. https://rescuewave.de/products-and-services. [25.11.2024]
• 42. LINDO API 14 – Powerful Library of Optimization Solvers and Mathematical Programming Tools. Available online: https://www.lindo.com/index.php/products/lindoapi-for-custom-optimization-application. [25.11.2024]
• 43. Michalik, K., (2006). PC-SHELL – Szkieletowy system ekspertowy, http://www.pjuszczuk.pl/wp-content/uploads/2016/10/Dokumentacja-pcs-shell.pdf. [15.11.2024]
• 44. RescueWave: Order instead of Chaos in Major Accidents The idea also comes from Karlsruhe Institute of Technology (KIT), https://www.kit.edu/kit/english/pi_2018_020_rescuewave-order-instead-of-chaos-in-major-accidents.php. [21.11.2024]
• 45. Siuta, M., (n.d.). A mass casualty incident through the eyes of the first responders, https://www.mp.pl/ratownictwo/algorytmy/140004,zdarzenie-masowe-oczami-pierwszegozrm. [05.12.2024]
• 46. Tetra Enhanced Data Services (TEDS). https://www.motorolasolutions.com/en_xu/products/tetra/teds.html. [14.11.2024]
• 47. Williams, D., (2007). Tactical medical coordinating system (TacMedCS), https://apps.dtic.mil/sti/pdfs/ADA477535.pdf. [15.11.2024]
• 48. The Act of 8 September 2006 on National Medical Rescue Service (Journal of Laws/Dz.U. of 2024, item 652).
• 49. The Regulation of the Minister of Health of 10 January 2014 on framework procedures of accepting emergency calls by medical dispatchers and dispatching emergency medical teams (Polish Journal of Laws/Dz.U. of 2014, item 66).
• 50. The National Health Emergency System, https://www.gov.pl/web/zdrowie/systempanstwowe-ratownictwo-medyczne. [05.12.2024]
• 51. Incidents with a large number of victims, https://www.gov.pl/web/zdrowie/zdarzenia-zduza-liczba-poszkodowanych. [05.12.2024]

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).