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Justin W. Herby
Professor Frances
Honors 292
8 November 2015
Big Data: Improving EMS through Rapid Response Times
Time is the most valuable asset in emergency medicine. In incidents of trauma, the main
purpose of paramedics and emergency medical technicians are to quickly deliver the patient to
the closest hospital available. During these instances, one minute can be the difference between
life and death. Emergency medical service companies, such as Mobile Medical Response, strive
to possess the most rapid response times in order to ensure that time plays the smallest role
possible in the outcome of a patients. Dispatchers at EMS control centers can use different types
of big data, specifically past locations of cardiac arrests and traffic accidents, to help increase the
response time and better the chance of survival for those in critical condition.
Big data, as its name implies, is an extremely large set of data that can be used to reveal
patterns or show correlations relating to different variables. In the modern world, data is first
stored digitally on a server or personal hard drive; however, gathering or digitizing vast amounts
of data does nothing until it goes through a process called datafication. Humans can derive
patterns from a small amount of digitized data, but when it comes to big data, finding a pattern is
an insurmountable task. Thus, through datification, computers are able to understand and use the
data for analyzation through algorithms and other methods. “…Google used optical characterrecognition software that could take a digital image and recognize the letters, words, sentences,
and paragraphs on it” (Mayer-Schönberger & Cukier 84). Emergency medicine providers can use
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this datification process to help improve their efficiency when responding to cardiac arrests and
traffic accidents.
“The Golden Hour” is a term coined by Dr. Crowley at the University of Maryland
Medical Center. In every case of trauma, EMS providers strive to have their patients transferred
to the hospital within an hour or less.
“From his personal experiences and observations in post-World War II Europe, and then
in Baltimore in the 1960s, Dr. Cowley recognized that the sooner trauma patients reached
definitive care—particularly if they arrived within 60 minutes of being injured—the
better their chance of survival” (Eisele).
When dispatch operators receive an emergency call, the closest ambulance is sent to the caller’s
location; however, that ambulance could still be quite a distance away from the caller. Location
becomes a determining factor on whether or not the ambulance will be able to respond and
transfer the patient within the Golden Hour, and when it comes to cardiac arrests, response time
is everything. Dispatchers collect this data and use computer analysis to help find patterns based
on time, day, and location to have ambulances prepared in the right areas.
Dispatchers collect the data relating to cardiac arrests based on location and the history of
traffic accidents in a city over many years to help station ambulances in the most efficient areas.
“Using mapping, or GIS, the locations of past cardiac arrests can be plotted along with
the time of day and response times. This leads to interfacing with coverage maps, AVL
and the ultimate timely response. Although evidence has proven that the eight-minute
response time standard is arbitrary, the same evidence shows that interventions at the
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four- to five-minute mark after a cardiac arrest markedly improve survival. This is data
that saves lives” (Overton).
By stationing an ambulance in areas that have a higher rate of cardiac arrests or traffic accidents,
the response time can be shortened. For example, an ambulance may be stationed near a
neighborhood that has more elderly people, which would be an area that would be more
susceptible to cardiac arrests. In terms of traffic accidents, Saginaw’s most deadly road is M-46
(Tower). Thus, parking an ambulance near dangerous intersections on M-46 could prevent
fatalities. Time is the most crucial element in both of these types of illnesses/injuries.
The variables of cardiac arrests in this type of data can affect how it may be interpreted.
“Data on cardiac arrest outcomes are generally collected and reported in 2 different formats: a
registry, which is used for quality improvement, and a research report, which examines specific
interventions and outcomes” (Jacobs & Nadkarni). After logging the data into the registry, the
researchers or analytical program must be able to figure out the variables that play into each
cardiac arrest. Whether it be by the time of day, age, sex, or pertinent medical history, the
variables have to be analyzed as well in order to determine how the stationing of ambulances can
be most efficient.
Researchers must careful pick and choose which data is pertinent to the analysis of traffic
accidents. For example, numerous car accidents occur each day in large cities, but most of them
do not require rapid responses by EMS providers simply because car accidents in larger cities are
often minor. “…a fender bender in a mall parking lot, at a busy intersection, or along a jammed
expressway, but the researchers found that, statistically, death from auto accidents increased
sharply the more rural a county was” (Howard). Researchers must only include data that focuses
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on traffic accidents that caused life-threating or fatal injuries, because the Golden Hour is most
crucial in these instances.
Using big data to increase response times is a relatively new concept to emergency
medicine. Dispatchers have just begun to implement these types of data sets into their system.
Datafication could stretch far beyond cardiac arrests and traffic accidents, and the type of
patterns that could be seen could make EMS response even more efficient. “Today we have the
tools (statistics and algorithms) and the necessary equipment (digital processors and storage) to
perform similar tasks much faster, at scale, and in many different contexts” (Mayer-Schönberger
& Cukier 101). These dispatchers have the ability to analyze insurmountable amounts of data
and use it to their advantage, but the viewers of that data must make the proper correlations that
the data shows.
EMS dispatchers should not rely solely on big data to provide the most rapid response
times. Data can show correlation, but it might not necessarily show causation. Using these
patterns should only be a tool to help dispatchers. “We must guard against overreliance on data
rather than repeat the error of Icarus, who adored his technical power of flight but used it
improperly and tumbled into the sea” (Mayer-Schönberger & Cukier 170). The line between
using the data and relying on the data can be thin.
When it comes to the ethical use of this data, little to no controversy surrounds the
subject. As stated previously, this type of data saves lives. The collection and publication of this
data could be considered some sort of privacy violation; however, most would say that it does
not cross the ethical border because the names of people involved in this data still remain
anonymous. More importantly, people should not mull over whether or not this usage of data is
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an ethical issue. Having the ability to use computer processing programs to analyze these large
sets of data to improve emergency medicine is remarkable.
The usage of big data could further revolutionize the emergency medical field.
Dispatchers have just now begun finding that patterns can help find the most efficient locations
to station ambulances. One must not look too much into the patterns that these data sets show,
because it can cause one to see correlations that do not necessary exist. Big data has its benefits,
and it can have its downfall; however, it is ultimately up to researchers and analysts to use the
data properly. “Once the world has been datafied, the potential used of the information are
basically limited only by one’s ingenuity” (Mayer-Schönberger & Cukier 96). EMS providers
should continue to use datafication to further improve response times as someone’s life will
depend on it.
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Works Cited
Howard, Brian Clark. "5 Surprising Facts on Why Cities Are Safer Than Rural Areas." National
Geographic. National Geographic Society, 24 July 2013. Web. 08 Nov. 2015.
Eisele, Charlie. "The Golden Hour." Journal of Emergency Medical Services. JEMS, 2008. Web. 08
Nov. 2015.
Jacobs, Ian, MD, and Vinay Nadkarni, MD. "Cardiac Arrest and Cardiopulmonary Resuscitation
Outcome Reports." Circulation AHA Journals. American Heart Association, 2004. Web. 8 Nov.
2015.
Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data: A Revolution That Will Transform How
We Live, Work, and Think. Boston: Houghton Mifflin Harcourt, 2013. Print.
Overton, Jerry. "Using Data and Technology to Improve Dispatch." Journal of Emergency Medical
Services. JEMS, 8 Dec. 2013. Web. 08 Nov. 2015.
Tower, Mark. "Saginaw County's Deadliest Road: M-46 Under Review as Fatalities Mount." MLive.
MLive Media Group, 12 July 2015. Web. 08 Nov. 2015.