Mass Casualty Information Decision Support
By
Joan M. Culley, PhD, MPH, MS, RN, CWOCN
Citation:
Culley, J. (October 2011). Mass Casualty Information Decision Support. Online Journal of
Nursing Informatics (OJNI), 15, (3). Available at http://ojni.org/issues/?p=XXX
Abstract
Response to major public health disasters represents one of the greatest challenges to a
nation’s emergency response infrastructure. Currently there is no universally accepted
framework or metrics for measuring the effectiveness/efficacy of information decision support
systems used to triage patients during all hazards mass casualty disasters. An effective
information technology infrastructure requires: 1) a scientifically grounded approach for
developing decision support and measuring performance; and 2) a common language and
vocabulary. Policy development for response to mass casualty events is predicated on accurate
information related to variables that have the greatest impact on outcomes of care. Research is
needed to build a scientifically grounded understanding of the information needs of the
healthcare and public health workforce during emergency decision making. Two
recommendations are discussed: 1) the need for mass casualty disaster responses ontologies; and
2) the development of comprehensive models that consider the contextual complexity of all
hazards events.
Key Words: Information decision support systems, performance measures, all hazards events,
information technology infrastructure, mass casualty triage, emergency decision making
2
Introduction
Events such as hurricane disasters, tsunamis, and the 2001 attacks on the World Trade
Center and Pentagon clearly illustrate the inadequacy of current mass casualty response systems.
The world faces an uncertain future clouded by the persistent threat of terrorist actions, as well as
the management of large numbers of seriously injured patients that could be expected during
catastrophic events. Such events could produce large numbers of casualties that would
overwhelm existing healthcare facilities, jeopardizing the lives of victims and healthcare
providers. Effective coordination and management of mass casualty events requires the
availability of timely, accurate information about patients, resources, and environmental
conditions.
The lack of a scientifically grounded understanding of the information and technology
needs of the healthcare and public health workforce during mass casualty events, and the paucity
of ontologies that permit knowledge sharing, hamper scientific inquiry into mass casualty
response research. The use of information decision support systems for the care of mass
causalities has been the focus of the majority of mass casualty research without a clear
understanding of the data or technology needed to provide real-time decision support for
healthcare providers and public health personnel. No consensus currently exists on standardized
definitions or the common factors that influence the effective management of mass causalities.
Our understanding of mass casualty disaster response relies on a conceptualization of the
concepts and relationships that hold among them to create a standardized knowledge base
(ontology) for the study of mass casualty response.
Capturing details about the collection and analysis of accurate, reliable, timely, and
appropriate data during the chaos of a mass casualty event is challenging. It is difficult to predict
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when and where a mass casualty event will take place which makes conducting prospective or
randomized studies to examine the efficacy of decision support data and methods used during
mass casualty events difficult.
Research is needed to build a scientifically grounded understanding of the information
and technology needs of the healthcare and public health workforce during emergency decision
making. This paper discusses the importance of a scientifically grounded approach for
developing decision support and measuring performance based on a common language
(ontology) and the use of comprehensive theoretical models that consider the contextual
complexity of all hazards mass casualty events. Theoretical models combined with the use of
ontologies provide the framework to study data from multiple sources and perspectives to better
understand the variables that have the greatest impact on outcomes of care.
Background
Mass causality events are almost a daily occurrence. On average a disaster occurs in the
world each day. Over the past 20 years more than 188 million people have been affected by
disasters worldwide resulting in loss of life and $535 billion in economic damages
(http://www.unisdr.org/eng/media-room/facts-sheets/fs-trends--natural-disasters.htm). On
January 12, 2010 the largest earthquake ever recorded in the Caribbean hit Haiti killing over
230,000, leaving thousands homeless, many without adequate healthcare, or socioeconomic
services. March 11, 2011 one of the largest earthquakes ever recorded (9.0 magnitude) spawned
a deadly tsunami that impacted Japan’s east coast, leaving thousands dead, injured, or homeless,
and the world fearful of possible radiation leaks from damaged nuclear power plants. Over the
past twenty years more than 26,000 deaths occurred in the United States from catastrophic
events. August 29, 2005, with winds gusting over 175 mph, Hurricane Katrina hit the Gulf coast
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of Louisiana, Mississippi, Alabama, and the pan handle of Florida killing 1,056 people (67%
were over the age of 61). It took more than 4 days to airlift over 4,000 people from the area to
safety. November 5, 2009, Maj. Nidal Malik Hasan fired more than 100 rounds of ammunition at
soldiers processing for deployment at Fort Hood in Texas resulting in 13 deaths and 30 people
wounded. On January 6, 2005, in Graniteville, South Carolina, two trains collided releasing 60
tons of chlorine gas that resulted in the deaths of 9 people, the treatment of over 250 people and
injuries, and long term health and socioeconomic effects to more than 600 residents of the
community.
These disasters serve as stark reminders that timely and adequate response to major
public health disasters represents one of the greatest challenges to a nation’s emergency response
infrastructure. Each is an example of the uniqueness of all hazards events. All hazards events
refer to catastrophic events that include natural occurrences such as hurricanes and floods,
unintended (technological) events such as chemical spills, fires, and deliberate acts such as
terrorism. All hazards events may also involve radiological or biological material.
Mass casualty events such as these, put huge demands on health and emergency services.
Continuity of health services must be maintained while increasing surge capacity (i.e., the ability
to handle increased numbers of patients quickly and efficiently). This is especially true in cases
where resources are limited and victims are isolated from services. The management of large
numbers of causalities expected during mass casualty events creates a disparity between needs
and resources. The primary goal of mass casualty triage is to identify and treat patients that have
the greatest chance for survival with healthcare intervention. When casualties overwhelm
existing resources the ability of healthcare providers to do the greatest good for the greatest
number is predicated on the validity and effectiveness of triage data that is used to make life and
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death decisions about the priority of care for victims. Decision support systems provide the
information needed to determine priorities based on the appropriate allocation of limited
resources.
Statement of the Issue
The organizational structure for each mass casualty event is distinctive in terms of its
short and temporary lifecycle. For example, the potential for conflicting prioritization criteria for
treatment, as well as the disparate and unpredictable nature of the multidisciplinary work force
teams, environmental conditions, and patients, makes each event unique (Culley, 2007; Hoey and
Schwab, 2004). Mass casualty incidents require that responders have sufficient information on
which to base their understanding (frequently referred to as “situational awareness”) of the event
as it evolves. Situational awareness refers to the ability to make timely, effective decisions
during rapidly evolving events. When situational awareness is lost, administrators are forced to
make resource allocation decisions without adequate information, consequently leading to the
ineffective utilization of staff, supplies, equipment, and ultimately poor patient outcomes
(Culley, 2007).
A successful response to an all hazards event depends on clear, accurate, complete,
timely, valid, and relevant data that track patients, personnel, resources, and potential hazards
(Sundnes and Birnbaum, 2003). Existing information systems are inadequate to support these
functions (Ash, Berg, and Coiera, 2004; Birnbaum, 2002) and often provide contradictory
information (Jederberg, 2005). Reliable decision support depends upon the use of common
terms and abbreviations understood by all members of the workforce to facilitate the concurrent
analysis of workforce and environmental factors, treatment priorities, and overall effectiveness
(Joint Commission on Accreditation of Healthcare Organizations, 2005).
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However little is understood about the complexity of mass casualty response, the
influence of these factors on disaster response, and the factors that must be considered in
designing and evaluating information decision support under these conditions. The science of
disaster medical research is observational or often anecdotal and more descriptive than analytical
(Benson, Koenig and Schultz, 2006; Birnbaum, 2000; Birnbaum, 2002; Burstein et al., 1996;
Chan et al., 2004; Domres et al., 2001; Frykberg, 2002; Gebbi and Merill, 2002; Hoey and
Schwab, 2004; Ihlenfeld, 2003; Kilner, 2002; Knop et al,. 1988; Landau, 1982; MacKenzie,
Alcorta and Kelen, 1999; Parker, 2006; Rüter, 2006; Schultz, Koenig and Noji, 1996; Sharma,
2005; Sundnes and Birnbaum, 2003). Most of the research related to the use of information
decision support systems employed for the care of mass casualties involves the evaluation of
existing or emerging technologies without a clear understanding of the information, technology,
systems, and decision support needs of healthcare providers and public health officials (Culley
and Effken, 2009).
Currently there is no universally accepted theoretical framework to guide the design of
information-decision support and measuring the effectiveness or efficacy of information decision
support systems. (Culley and Effken, 2009; Sundnes and Birnbaum, 2003). Only one of several
adult triage systems currently in use in the United States has been evaluated using outcome data
from transportation disaster injuries (Kahn, 2009). No other system includes assessments of
incidents involving chemical, radiological, or biological material, and none have been evaluated
for use with the evolving information and communication technologies (Armstrong, Frykberg
and Burnis, 2008; Fernandes et al., 2005), an important gap in disaster research (Jenkins et al.,
2008). Research is needed to build a scientifically grounded understanding of the information
needs of the healthcare and public health workforce during emergency decision making. This
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provides the structure to develop standardized data collection methodologies and accurate quality
standards, metrics, and assessments (Ash, Berg, and Coiera, 2004) to evaluate the efficacy of
information decision support.
Mandates for Improving Information Processing
The Institute of Medicine and numerous studies discuss the need for clinical information
systems and technology in the area of emergency preparedness and response. In 2001, the
Institute of Medicine (IOM) published a report that called for fundamental change to close the
quality gap in the current health system. The report offered a set of performance expectations for
the 21st century health care system that included key steps to strengthen clinical information
systems which have been relatively untouched by the revolution in information technology.
Disaster care is cited as a special area of concern. In 2008, the IOM published another report that
established research priorities in emergency preparedness and response for public health systems.
Recommendation 2 addressed the development of communications that improve the timely
exchange of vital and accurate information with diverse audiences. Recommendation 3 stressed
the importance of the systems and infrastructure needed for a sustainable disaster response and
recommendation 4 emphasized the importance of research that will generate criteria for
evaluating public health emergency preparedness, response, and recovery and metrics for
measuring their efficiency and effectiveness. This was again reinforced in the 2010 IOM report
that recommended a blueprint for the future of nursing that included better data collection and an
improved information infrastructure for effective workforce planning and policy making. The
Centers for Disease Control and Prevention (CDC) also published a report (2006) that
encourages the exploration of: the relationship between health systems’ performance and health
outcomes; approaches for evidence-based public health decision support systems and
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prioritization systems; measurement tools and data evaluation strategies regarding public health
outcomes; and technologies to enable effective communication of information and data to the
appropriate persons at the appropriate time and in the appropriate format.
The US is lagging in the adoption of national policies that promote the use of information
technology (Davis, Schoen, and Stremikis, 2010). This is particularly evident in disaster care
where currently the majority of systems for disaster response and field health care rely on paper
triage tags and pen and paper to record aggregate information on patients, ambulance availability
and hospital status. Radio and face-to-face interactions have been the primary communication
methods to deliver information about patient and resource status (Culley, 2007).
Both military and civilian researchers are currently developing sophisticated information
system technology prototypes to coordinate and enhance the care of mass casualties. Computer
miniaturization, personal digital assistants (PDAs), and wireless technologies have allowed the
development of mobile data acquisition and monitoring devices that are being tested specifically
for use at disaster sites. For example, the WIISARD First Responder (WFR) (Killeen et al., 2006)
developed by a group of scientists in San Diego and the Tactical Medical Coordination System
(TacMedCS) developed by the US Army (Davis and Ryan, 2001; Williams, 2007) are designed
to replicate the use of paper tags, but with the functionally of electronically recorded data,
barcode scanning, and wireless data transmission to a real-time patient database accessible to
multiple users simultaneously (Killeen et al., 2006). Technologically sophisticated facilities may
track patients, staff, and equipment using bar-coded ID bands or monitor bed occupancy as
secondary indicators of the demands on the healthcare delivery system (Fry and Lenert, 2005).
However, none of these systems or tools has yet been tested under disaster conditions involving
hundreds or thousands of multiple casualties (Ihlenfeld, 2003).
9
All of these reports and studies emphasize the need for further research to build a
scientifically grounded understanding of the information and technology needs of the healthcare
and public health workforce during emergency decision making. We currently lack the research
to determine appropriate performance measures that take into account the complexity of
variables that influence mass casualty disaster response.
Considerations
The goal of healthcare personnel during mass casualty events is to increase survivability
with minimal disability within a context of restricted resources. Organizational systems must
have the capability and flexibility to respond in a manner that positively influences these
outcomes. An information technology infrastructure is needed to support efficient and effective
data collection, analysis, evaluation and dissemination across the scope of services required
during all hazards events. An effective and efficient information technology infrastructure
requires a scientifically grounded approach for developing decision support and measuring
performance with a common language and vocabulary. Two recommendations are discussed: 1)
the need for mass casualty disaster responses ontologies; and 2) the development of a
comprehensive theoretical model that considers the contextual complexity of all hazards events.
Mass Casualty Disaster Response Ontologies
The success of mass casualty disaster response largely depends on finding and
successfully integrating related information from the various responding agencies at the local,
regional, state, federal and international levels to make decisions throughout all phases of the
disaster response continuum. This involves the management of large amounts of heterogeneous
data that are created, collected and integrated among the multiple agencies involved in disaster
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response. Interoperability or the ability to share information maintained individually by these
agencies becomes problematic when different vocabularies and meanings are applied to the data.
In order to study mass casualty response we must first be able to explain the reality of
mass casualty disaster response by breaking it down into concepts, relationships and rules so that
the knowledge can be shared with others (Dobratz and Pilkington, 2004; Xun and Zlatanov,
2007). This is also known as knowledge representation that concerns the language, ontologies,
and vocabulary used to represent data and information in a way that facilitates inference, the
drawing of conclusions and the sharing of knowledge. The term ontology arises from both
cognitive science and artificial intelligence and is concerned with how people store and process
information.
A unified language system is necessary to develop mass casualty disaster response
ontologies and vocabularies that consistently structure multiple local, regional, state, federal,
international, and public health datasets and information for knowledge sharing. This ontological
approach provides a representation of the constructs or the variables that identify the major
components in any mass casualty event, the relationships between them, and the performance
indicators to measure the efficacy and effectiveness of each construct. Currently, ontologies that
cut across the broad scope of triage services required during mass casualty disaster response and
provide an information technology infrastructure for mass casualty decision support are lacking.
The Incident Command System (ICS) (FEMA Emergency Management Institute ICS
Resource Center, n.d.) used in the United States and other countries organizes scalable
emergency responses to incidents of any magnitude. The ICS system is activated in any
emergency situation and establishes the organizational structure that brings together a workforce
from multiple disciplines that includes: local/regional (police, fire, first responders, public health
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personnel, volunteers, medical treatment facilities, all logistics support, etc.); state (national
guard, emergency planning operations, etc.); and federal (Federal Emergency Management
Agency, Department of Homeland Security, Red Cross, emergency management agencies, etc.)
resources and agencies. An information technology infrastructure is needed to facilitate the
communication and sharing of information among these disparate groups.
One way to deal with the problem is the use of ontology to define the language, codes,
and messages implicit in the knowledge of mass casualty disaster response. Two examples of an
ontological approach to the development of a nationwide health information technology
infrastructure that allows for the electronic use and accurate exchange of health information
include:

The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD9-CM codes) which lists diagnoses and identifying codes used to report diagnoses on
claims (http://govpulse.us/entries/2010/01/13/E9-31216/health-information-technologyinitial-set-of-standards-implementation-specifications-and-certificati); and

The Systematized Nomenclature of Medicine - Clinical Terms, (SNOMED CT) which
systematically organizes the computer processable collection of medical terminology that
covers most areas of clinical information such as diseases, findings, procedures,
microorganisms, pharmaceuticals etc. It allows a consistent way to index, store, retrieve,
and aggregate clinical data across specialties and sites of care
(http://www.nlm.nih.gov/research/umls/Snomed/snomed_faq.html).
A standardized vocabulary and terminologies are the foundation for the development of a
mass casualty disaster response infrastructure with the capability of providing reliable decision
support, and comparisons of data across healthcare, public health and disaster response systems.
12
This enhances the ability to conduct disaster research to study the effects of variables on
outcomes, cost on effectiveness and the development of performance outcome measures.
All Hazards Conceptual Model
A theory-driven approach to the development of an all hazards mass casualty information
technology infrastructure would allow researchers to ask questions about the interactions
between patients-victims, healthcare and public health professionals, organizational systems,
information technology, information systems and outcomes (Culley, 2007). A comprehensive
theoretical model would facilitate the study of information needs for public health emergency
response. This includes the effects of cognitive workload, environmental factors, and technology,
as well as the impact of promising interventions (Auf der Heiden, 2006) thereby expanding the
evidence base for disaster medicine and the development of performance measures to guide
resource allocation.
An all hazards approach structured around a continuity of care model (as opposed to
episodic, one-on-one care) supported by interdisciplinary teams of healthcare and public health
professionals may help to move the science forward in the area of emergency
preparedness/response, disaster medical research, and ultimately maximize survival rates during
times of mass casualty incidents (Culley, 2007; Landau et al., 1982). Research that focuses on an
understanding of the interdisciplinary structure, processes, outcomes and contextual variables
that affect mass casualty response provides the framework to identify and measure the specific
contributions of each variable to outcomes and cost efficiency. Previous research conducted by
Culley (2007) validated constructs or the conditions that identify the major components in any all
hazards event. Table 1 outlines the major components of conceptual approach to all hazards mass
casualty research.
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Table 1
Major Components for an All Hazards Conceptual Model (Culley, 2007)
Construct/Condition
Patient
Components
demographics of each victim in an incident; as well as the
number and type of injury(ies)
Workforce
characteristics (credentials, licenses, certifications, experience,
training, education) of the wide skill mix of individuals who
may never have worked together before
Resources
the amount, type and location of assets that are available
Environmental Context
characteristics of the type of all hazards event (natural,
unintentional/technological, intentional, chemical, radiological,
biological)
Organizational Complexity
size of the workforce, degree of technology used and specialties
involved
Organizational Customs
leadership style, incentive structure and beliefs about the
prioritization criteria of victims for care
Technology
characteristics of the technology infrastructure needed to
support information decision processing to provide secure data
and information at the right time, place, amount and format
Information
characteristic of the data that is accessed processed and
disseminated in a way that provides accurate, complete, and
secure information to authorized users in the proper amount,
language and format needed at each level of the organization for
situational awareness
Structure
organizational framework (hierarchical to flexible) needed to
support the needs of the workforce in managing the diverse
needs of each patient
Triage
process of classifying and prioritizing patients for care
throughout the continuum of response
Outcomes
goals related to patients and resources (overtriage – the
overestimate of patient injuries that inappropriately uses limited
resources at the potential expense of patients with more severe
injuries; or undertriage – the assignment of critically injured
patients needing immediate care to a delayed category that can
lead to preventable deaths or deformities
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Conclusions
All hazards catastrophic events involve the interplay of a complex health and multiagency system that takes place in a sociotechnical contextual setting characterized by
multidisciplinary teams working together in a chaotic highly dynamic environment. Mass
casualty events breed exceptions to protocol and workflow that permeate many decisions and the
dependence upon clear, accurate, and speedy communication processes. This paper illustrates the
necessity for mass casualty disaster response ontologies and an all hazards conceptual model
approach as the building blocks for the study of mass casualty information decision support.
There remains a great need for research in this area to create metrics, performance measures, and
effective methods to measure the efficacy and efficiency of mass casualty information decision
support systems appropriateness to triage victims of mass casualty events.
15
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Author Bio:
Joan M. Cully has over 30 years of experience to the profession of nursing as a researcher,
administrator, educator, consultant, retired Naval Reserve Officer, and Board Certified Wound,
Ostomy and Continence Nurse (CWOCN). Educational background includes: a PhD with a major
in Healthcare Informatics and Nursing Systems, an MS in Nursing Administration and an MPH in
Health Education. Service includes national and regional committees and task forces that have been
responsible for setting standards and guidelines for nursing practice. Scholarship accomplishments
included publications in peer reviewed journals, podium and poster presentations at national
conferences and funded grants. Primary areas of research include mass casualty triage model
validation, work force characteristics, simulation to enhance training in emergency preparedness,
information needs and processing for first responders during mass casualty events and medical
outcome measures. Current funding and Principal Investigator on a $320,850.00 National Library of
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Medicine/National Institutes of Health Grant titled Mass Casualty Triage Validation Study
(1R21LM010833-01).
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