Real-time geotracking and cataloging of mass casualty incident markers in a search and rescue training simulation: Pilot study


  • Kendall Park, BS
  • Kourtney Meiss
  • Luke Guerdan
  • Ev Cheng
  • Josiah Burchard
  • John Gillis, BSCSE
  • Prasad Calyam, PhD
  • Salman Ahmad, MD, FACS, FCCM



mass casualty incidents, mesh networks, geotracking, internet-of-things, disaster medicine


Objective: Search and rescue after mass casualty incidents relies on robust data infrastructure. Federal Emergency Management Agency (FEMA’s) Task Force 1 (TF1) trains its volunteers to locate and virtually tag scene incidents using a global positioning satellite (GPS) device programmed with markers for each incident (Iron Sights). The authors performed a pilot study comparing Iron Sights™ to a Wi-Fi-based real-time incident geolocation and virtual tagging dashboard (Panacea™) in creating a dynamic common operating picture.

Design: Twenty-nine stations were placed at a predefined scene incident, each featuring a set of varying waypoint markers using standard FEMA/TF1 nomenclature. Two volunteers performed the experiment for both the Iron Sights and Panacea systems, digitally tagging all station waypoints.

Setting: TF1 simulation training field.

Main outcome measure(s): Metrics compared included GPS location precision, marker accuracy, and delay between scene sweep and common operational picture (COP) generation.

Results: Two hundred and sixty-one waypoints were digitally tagged after excluding three stations for missing data. The average GPS location difference for all waypoints between Iron Sights and Panacea was 3.65 m. Marker tagging accuracy between Iron Sights and Panacea was equivalent and not statistically different (78.8 percent vs 66.2 percent, respectively, p = 0.11). Waypoints were tagged in 26.59 minutes and 10.55 minutes on average, respectively. Time from scene sweep to virtual COP generation was 7.97 minutes for Iron Sights after complete scene sweep and 37 seconds for Panacea for each waypoint posting in real-time.

Conclusions: Panacea generated the COP in real-time compared to a delay with Iron Sights while maintaining the same location precision and marker accuracy. This pilot trial successfully demonstrated the ability to provide real-time actionable intelligence to incident commanders during mass casualty search and rescue missions. Larger field trials are recommended to refine the system and broaden its capabilities.

Author Biographies

Kendall Park, BS

Department of Surgery, University of Missouri School of Medicine, Columbia, Missouri

Kourtney Meiss

Undergraduate student, Department of Computer Science, Wofford College, Spartanburg, South Carolina

Luke Guerdan

Undergraduate student, Department of Computer Science and Engineering, University of Missouri, Columbia, Missouri

Ev Cheng

Undergraduate student, Department of Computer Science, Vassar College, Poughkeepsie, New York

Josiah Burchard

Undergraduate student, Department of Computer Science, Southeast Missouri State University, Cape Girardeau, Missouri

John Gillis, BSCSE

Department of Computer Science and Engineering, University of Missouri, Columbia, Missouri. Dave Weber, BSCE, MSCE, Missouri Task Force One, Columbia, Missouri

Prasad Calyam, PhD

Department of Computer Science and Engineering, University of Missouri, Columbia, Missouri

Salman Ahmad, MD, FACS, FCCM

Department of Surgery, University of Missouri School of Medicine, Columbia, Missouri


Issues E, Report W, Newgard CD, et al.: National preparedness goal. Resuscitation. 2015;78(3): 177. doi:10.1109/THS.2008.4534447.

Gillis J, Calyam P, Bartels A, et al.: Panacea’s glass : Mobile cloud framework for communication in mass casualty disaster triage. In: International Conference on Mobile Cloud Computing, Services, and Engineering; March 30-April 3, 2015; San Francisco, CA. doi:10.1109/MobileCloud.2015.39.

Vassell M, Apperson O, Calyam P: Intelligent Dashboard for augmented reality based incident command response co-ordination. IEEE Annual Consumer Communications and Networking Conference (CCNC); January 9-12, 2016; Las Vegas, NV. doi:10.1109/CCNC.2016.7444921.

Gillis J, Calyam P, Apperson O, et al.: Panacea’s cloud: Augmented reality for mass casualty disaster incident triage and co-ordination. 13th IEEE Annual Consumer Communications and Networking Conference (CCNC 2016); January 9-12, 2016; Las Vegas, NV. doi:10.1109/CCNC.2016.7444772.

Cheng E, Meiss K, Park K, et al.: Contextual geotracking service of incident markers in disaster search-and-rescue operations. In: 15th International Symposium on Network Computing and Applications (NCA); October 31-November 2, 2016; Cambridge, MA. doi:10.1109/NCA.2016.7778586.

NASA: NASA TLX: Task Load Index. Planta Med. 2010; 35(4): 308-315. doi:10.1055/s-0028-1097222.

Hart SG: Nasa-task load index (NASA-TLX); 20 years later. Proc Hum Factors Ergon Soc Annu Meet. 2006; 50(9): 904-908. doi:10.1177/154193120605000909.

Sharek D: A useable, online NASA-TLX tool. Proc Hum Factors Ergon Soc. 2011; 55(1): 1375-1379. doi:10.1177/1071181311551286.

Chemodanov D, Esposito F, Sukhov A: AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications. Futur Gener Comput Syst. 2017; 92: 1051-1065. doi:10.1016/j.future.2017.08.009.

Knudson MM, Velmahos G, Cooper ZR: Response to mass casualty events : From the battle field to stop the bleed campaign. Trauma Surg Acute Care Open. 2016; 1(1): 1-3. doi:10.1136/tsaco-2016-000023.

Morris TJ, Pajak J, Havlik F, et al.: Battlefield Medical Information System-Tactical (BMIST): The application of mobile computing technologies to support health surveillance in the Department of Defense. Telemed J E Health. 2006; 12(4): 409-416. doi:10.1089/tmj.2006.12.409.

Fry EA, Lenert LA: MASCAL: RFID tracking of patients, staff and equipment to enhance hospital response to mass casualty events. AMIA Annu Symp Proc. 2005; 1: 261-265.

Jokela J, Simons T, Kuronen P, et al.: Implementing RFID technology in a novel triage system during a simulated mass casualty situation. Int J Electron Healthc. 2008; 4(1): 105-118. doi:10.1504/IJEH.2008.018923.

Zhao X, Rafiq A, Hummel R, et al.: Integration of information technology, wireless networks, and personal digital assistants for triage and casualty. Telemed J E Health. 2006; 12(4): 466-474. doi:10.1089/tmj.2006.12.466.

Tollefsen WW, Gaynor M, Pepe M, et al.: iRevive: A pre-hospital database system for emergency medical services. Int J Healthc Technol Manag. 2005; 6(4-6): 454-469. doi:

Gao T, Massey T, Selavo L, et al.: The advanced health and disaster aid network: A light-weight wireless medical system for tiage. IEEE Trans Biomed Circuits Syst. 2007; 1(3): 203-216. doi:10.1109/TBCAS.2007.910901.

Lenert LA, Kirsh D, Griswold WG, et al.: Design and evaluation of a wireless electronic health records system for field care in mass casualty settings. J Am Med Inform Assoc. 2011; 18(6): 842-852. doi:10.1136/amiajnl-2011-000229.

Remick K, Shackelford S, Oh JS, et al.: Surgeon preparedness for mass casualty events: Adapting essential military surgical lessons for the home front. Am J Disaster Med. 2016; 11(2): 77-87. doi:10.5055/ajdm.2016.0228.



How to Cite

Park, BS, K., K. Meiss, L. Guerdan, E. Cheng, J. Burchard, J. Gillis, BSCSE, P. Calyam, PhD, and S. Ahmad, MD, FACS, FCCM. “Real-Time Geotracking and Cataloging of Mass Casualty Incident Markers in a Search and Rescue Training Simulation: Pilot Study”. American Journal of Disaster Medicine, vol. 14, no. 2, Apr. 2019, pp. 89-95, doi:10.5055/ajdm.2019.0319.