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

Published online by Cambridge University Press:  23 October 2012

Mahshid Abir ,
Matthew M. Davis ,
Pratap Sankar ,
Andrew C. Wong  and
Stewart C. Wang
Mahshid Abir*
Affiliation:
Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan USA. Dr. Abir is now with the Department of Emergency Medicine, George Washington University, Washington, DC USA and Rand Corporation, Arlington, Virginia USA
Matthew M. Davis
Affiliation:
Department of Internal Medicine, Department of Pediatrics and the Robert Woods Johnson Foundation Clinical Scholars Program, University of Michigan, Ann Arbor, Michigan USA
Pratap Sankar
Affiliation:
TRW Automotive, East Lansing, Michigan USA
Andrew C. Wong
Affiliation:
Department of Emergency Medicine, University of California, Irvine, Irvine, California USA
Stewart C. Wang
Affiliation:
Department of Surgery, University of Michigan, Ann Arbor, Michigan USA
*
Correspondence: Mahshid Abir, MD, MSc Rand Corporation 1200 South Hayes Street Arlington, VA 22202 USA E-mail [email protected]
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Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

burnsdisasteremergency medicinemass-casualty incidentmodelsimulationsurgesurge capacity
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 28 , Issue 1 , February 2013 , pp. 23 - 32
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2013

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References

1.Institute of Medicine. Hospital-Based Emergency Care: At the Breaking Point. Washington, DC, USA: National Academies Press; 2006.Google Scholar
2.Koenig, KL, Kelen, GD. Executive Summary: The Science of Surge Conference. Acad Emer Med. 2006;13:1087-1088.CrossRefGoogle Scholar
3.Kelen, GD, McCarthy, ML. The science of surge. Acad Emer Med. 2006;13(11):1089-1094.CrossRefGoogle ScholarPubMed
4.US Department of Health and Human Services, Agency for Healthcare Research and Quality. Addressing surge capacity in a mass casualty event. http://archive.ahrq.gov/news/ulp/btbriefs/btbrief9.htm. Accessed September 12, 2012.Google Scholar
5.Joint Commission on Accreditation of Healthcare Organizations. Surge Hospitals: Providing Safe Care in Emergencies. Oak Brook Terrace, IL, USA; 2006.Google Scholar
6.Barbisch, D. Regional responses to terrorism and other medical disasters: developing sustainable surge capacity. In: Johnson JA, Ludlow GR, Jones WJ (eds). Community Preparedness and Response to Terrorism. Westport, CT: Praeger; 2005.Google Scholar
7. Roberts R. Disaster Surge Tool. http://www.emrocch.org/disastersurge/. Accessed October 30, 2010.Google Scholar
8.US Department of Health and Human Services, Centers for Disease Control and Prevention. FluSurge. http://www.cdc.gov/flu/tools/flusurge/. Accessed October 30, 2010.Google Scholar
9.US Department of Health and Human Services, Agency for Healthcare Research and Quality. Hospital Surge Model. http://archive.ahrq.gov/prep/hospsurgemodel/. Accessed September 12, 2012.Google Scholar
10.ProModel Corporation (2008). Promodel (Version 7.5) [computer software]. Orem, UT. http://www.promodel.com. Accessed October 30, 2010.Google Scholar
11.Welling, L, Van Harten, SM, Patka, P, et al. The café fire on New Year's Eve in Volendam, the Netherlands: description of events. Burns. 2005;31(5):548-555.CrossRefGoogle ScholarPubMed
12.Mahoney, EJ, Harrington, DT, Biffl, WL, Metzger, J, Oka, T, Cioffi, WG. Lessons learned from a nightclub fire: institutional disaster preparedness. J Trauma. 2005;58(3):487-491.CrossRefGoogle ScholarPubMed
13.Ma, B, Wei, W, Xia, ZF, 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.CrossRefGoogle ScholarPubMed
14.US Census Bureau. U.S. Population Projections. http://www.census.gov/population/projections/23PyrmdMI1.pdf. Accessed August 14, 2010.Google Scholar
15.Christie, PMJ, Levary, RR. The use of simulation in planning the transportation of patients to hospitals following a disaster. J of Medical Systems. 1998;22(5):289-300.CrossRefGoogle ScholarPubMed
16.de Ceballos, JP, Turégano-Fuentes, F, Perez-Diaz, D, Sanz-Sanchez, M, Martin-Llorente, C, Guerrero-Sanz, JE. 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.CrossRefGoogle ScholarPubMed
17.Michigan Department of Community Health, Office of Public Health Preparedness, EMS & Trauma Systems Section, et al. Developmental Template for the Hospital Management of Burn Patients Resulting from a Multi-casualty Incident. January 2010. http://www.michiganburn.org/images/content/MIBurnVer15.pdf. Accessed September 12, 2012.Google Scholar
18.American Burn Association. National Burn Repository 2000-2009. http://www.ameriburn.org/. Accessed March 21, 2010.Google Scholar
19.Cassuto, J, Tarnow, P. A discotheque fire in Gothenburg 1998. A tragedy among teenagers. Burns. 2003;29(5):405-416.CrossRefGoogle ScholarPubMed
20.Mackie, DP, Koning, HM. Fate of mass burn casualties: implications for disaster planning. Burns. 1990;16(3):203-206.CrossRefGoogle ScholarPubMed
21.American Burn Association. Burn Center Referral Criteria. http://www.ameriburn.org/BurnCenterReferralCriteria.pdf. Accessed October 30, 2010.Google Scholar
22.Hick, JL, Hanfling, D, Burstein, JL, et al. Health care facility and community strategies for patient care surge capacity. Ann Emerg Med. 2004;44(3):253-261.CrossRefGoogle ScholarPubMed
23.Rubinson, L, Hick, JL, Curtis, JR, et al. Definitive care for the critically ill during a disaster: medical resources for surge capacity. Chest. 2008;133:32S-50S.CrossRefGoogle ScholarPubMed
24.Barbisch, D, Haik, J, Tessone, A, Hanfling, D. Surge capacity. In: Koenig KL, Schultz CH (eds). Disaster Medicine. New York, NY: Cambridge University Press; 2010:35-50.Google Scholar
25.Tricklebank, S. Modern trends in fluid therapy for burns. Burns. 2009;35(6):757-767.CrossRefGoogle ScholarPubMed
26.US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. http://www.cdc.gov/nchs/data/ad/ad347.pdf. Accessed August 14, 2010.Google Scholar
27.Discovery Health. http://health.howstuffworks.com/human-body/cells-tissues/question593.htm. Accessed August 14, 2010.Google Scholar
28.Huckfeldt, RE. Critical care for patients with severe burn injury. In: Clark AD (ed). Burns: The Medical and Forensics Model. Tucson, AZ: Lawyers & Judges Publishing Company, Inc.; 2006:57-78.Google Scholar
29.Barnett, DJ, Balicer, RD, Thompson, CB, 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.CrossRefGoogle ScholarPubMed
30.Lanzilotti, SS, Galanis, D, Leoni, N, Craig, B. Hawaii medical personnel assessment: a longitudinal study of Hawaii doctors and nurses, their knowledge, skill and willingness to treat victims related to weapons of mass destruction and naturally caused casualty incidents. Hawaii Medical Journal. 2002;61(8):162-173.Google Scholar
31.Phillips, SJ, Knebel, A. Mass Medical Care with Scarce Resources: A Community Planning Guide. Rockville, MD: Agency for Healthcare Research and Quality; 2007.Google Scholar
32.Health Systems Research. Altered Standards of Care in Mass Casualty Events: Bioterrorism and Other Public Health Emergencies. Rockville, MD: Agency for Healthcare Research and Quality. Publication No. 05-0043; 2005.Google Scholar
33.US Department of Homeland Security, Homeland Security Grant Program. Supplemental Resource: MMRS Target Capabilities/Capability Focus Areas and Community Preparedness, February 2008. http://www.fema.gov/pdf/government/grant/hsgp/fy08_hsgp_guide_mmrs.pdf. Accessed November 1, 2010.Google Scholar
34. Barbisch, DF. Developing sustainable surge capacity for a regional health response to terrorism and other medical disasters [video recording]. American Public Health Association: Public Health and Environment, Washington DC, November 6-10, 2004. http://apha.confex.com/apha/132am/techprogram/paper_83055.htm. Accessed November 1, 2010.Google Scholar
35. PR Newswire. California Unveils World's Largest Mobile Civilian Hospital in Preparation for Major California Disaster. http://www.prnewswire.com/cgibin/stories.pl?ACCT=109&STORY=/www/story/08-25-2007/0004651300&EDATE=. Accessed November 1, 2010.Google Scholar
36.Joint Commission on Accreditation of Healthcare Organizations. Health Care at the Crossroads: Strategies for Creating and Sustaining Community-wide Emergency Preparedness Strategies. Oakbrook, IL; 2003.Google Scholar
37.McCarthy, ML, Zeger, SL, Ding, R, Aronsky, D, Hoot, NR, Kelen, GD. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337-346.CrossRefGoogle ScholarPubMed
38.Schweigler, LM, Desmond, JS, McCarthy, ML, Bukowski, KJ, Ionides, EL, Younger, JG. Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301-308.CrossRefGoogle ScholarPubMed