Skip to main content
    • Aa
    • Aa

Design of a Model to Predict Surge Capacity Bottlenecks for Burn Mass Casualties at a Large Academic Medical Center

  • Mahshid Abir (a1), Matthew M. Davis (a2), Pratap Sankar (a3), Andrew C. Wong (a4) and Stewart C. Wang (a5)...

To design and test a model to predict surge capacity bottlenecks at a large academic medical center in response to a mass-casualty incident (MCI) involving multiple burn victims.


Using the simulation software ProModel, a model of patient flow and anticipated resource use, according to principles of disaster management, was developed based upon historical data from the University Hospital of the University of Michigan Health System. Model inputs included: (a) age and weight distribution for casualties, and distribution of size and depth of burns; (b) rate of arrival of casualties to the hospital, and triage to ward or critical care settings; (c) eligibility for early discharge of non-MCI inpatients at time of MCI; (d) baseline occupancy of intensive care unit (ICU), surgical step-down, and ward; (e) staff availability—number of physicians, nurses, and respiratory therapists, and the expected ratio of each group to patients; (f) floor and operating room resources—anticipating the need for mechanical ventilators, burn care and surgical resources, blood products, and intravenous fluids; (g) average hospital length of stay and mortality rate for patients with inhalation injury and different size burns; and (h) average number of times that different size burns undergo surgery. Key model outputs include time to bottleneck for each limiting resource and average waiting time to hospital bed availability.


Given base-case model assumptions (including 100 mass casualties with an inter-arrival rate to the hospital of one patient every three minutes), hospital utilization is constrained within the first 120 minutes to 21 casualties, due to the limited number of beds. The first bottleneck is attributable to exhausting critical care beds, followed by floor beds. Given this limitation in number of patients, the temporal order of the ensuing bottlenecks is as follows: Lactated Ringer's solution (4 h), silver sulfadiazine/Silvadene (6 h), albumin (48 h), thrombin topical (72 h), type AB packed red blood cells (76 h), silver dressing/Acticoat (100 h), bismuth tribromophenate/Xeroform (102 h), and gauze bandage rolls/Kerlix (168 h). The following items do not precipitate a bottleneck: ventilators, topical epinephrine, staplers, foams, antimicrobial non-adherent dressing/Telfa types A, B, or O blood. Nurse, respiratory therapist, and physician staffing does not induce bottlenecks.


This model, and similar models for non-burn-related MCIs, can serve as a real-time estimation and management tool for hospital capacity in the setting of MCIs, and can inform supply decision support for disaster management.

AbirM, DavisMM, SankarP, WongAC, WangSC. Design of a Model to Predict Surge Capacity Bottlenecks for Burn Mass Casualties at a Large Academic Medical Center. Prehosp Disaster Med. 2013;28(1):1-10.

Corresponding author
Correspondence: Mahshid Abir, MD, MSc Rand Corporation 1200 South Hayes Street Arlington, VA 22202 USA E-mail
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

2. KL Koenig , GD Kelen . Executive Summary: The Science of Surge Conference. Acad Emer Med. 2006;13:1087-1088.

3. GD Kelen , ML McCarthy . The science of surge. Acad Emer Med. 2006;13(11):1089-1094.

11. L Welling , SM Van Harten , P Patka , et al. The café fire on New Year's Eve in Volendam, the Netherlands: description of events. Burns. 2005;31(5):548-555.

12. EJ Mahoney , DT Harrington , WL Biffl , J Metzger , T Oka , WG Cioffi . Lessons learned from a nightclub fire: institutional disaster preparedness. J Trauma. 2005;58(3):487-491.

13. B Ma , W Wei , ZF Xia , et al. Mass chemical burn casualty: emergency management of 118 patients with alkali burn during a Matsa typhoon attack in Shanghai, China in 2005. Burns. 2007;33(5):565-571.

15. PMJ Christie , RR Levary . The use of simulation in planning the transportation of patients to hospitals following a disaster. J of Medical Systems. 1998;22(5):289-300.

16. JP de Ceballos , F Turégano-Fuentes , D Perez-Diaz , M Sanz-Sanchez , C Martin-Llorente , JE Guerrero-Sanz . 11 March 2004: the terrorist bomb explosions in Madrid, Spain–an analysis of the logistics, injuries sustained and clinical management of casualties treated at the closest hospital. Crit Care. 2005;9(1):104-111.

19. J Cassuto , P Tarnow . A discotheque fire in Gothenburg 1998. A tragedy among teenagers. Burns. 2003;29(5):405-416.

20. DP Mackie , HM Koning . Fate of mass burn casualties: implications for disaster planning. Burns. 1990;16(3):203-206.

22. JL Hick , D Hanfling , JL Burstein , et al. Health care facility and community strategies for patient care surge capacity. Ann Emerg Med. 2004;44(3):253-261.

23. L Rubinson , JL Hick , JR Curtis , et al. Definitive care for the critically ill during a disaster: medical resources for surge capacity. Chest. 2008;133:32S-50S.

25. S Tricklebank . Modern trends in fluid therapy for burns. Burns. 2009;35(6):757-767.

29. DJ Barnett , RD Balicer , CB Thompson , et al. Assessment of local public health workers’ willingness to respond to pandemic influenza through the application of the extended parallel process model. PLoS One. 2009;4(7):e6365.

37. ML McCarthy , SL Zeger , R Ding , D Aronsky , NR Hoot , GD Kelen . The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337-346.

38. LM Schweigler , JS Desmond , ML McCarthy , KJ Bukowski , EL Ionides , JG Younger . Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301-308.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Prehospital and Disaster Medicine
  • ISSN: 1049-023X
  • EISSN: 1945-1938
  • URL: /core/journals/prehospital-and-disaster-medicine
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 1
Total number of PDF views: 30 *
Loading metrics...

Abstract views

Total abstract views: 230 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 23rd June 2017. This data will be updated every 24 hours.