Measuring and Improving EMS System Performance: 2015

2015
Robert M. Davis, MPA
The City of Virginia Beach Department of
Emergency Medical Services (VBEMS)
5/14/2015
Measuring and Improving EMS System
Performance
“EMS Incident Hours, Unit Hour Unit Utilization, Response Times
Analysis and Matching Resources to Demand”
This is a research endeavor presenting the findings of the Department of
EMS’s incident and unit hour analysis; this paper covers the reported
incident hours, unit hours and UHUU rate per EMS response units
(Ambulance and Zone Car). A regression analysis examines the
relationship between EMS demand, unit staffing, and UHUU and unit
response times for calendar years 2009-2014. How EMS data can be
used to drive system performance enhancements and improve services
to the community.

1
Executive Summary
 What is the amount of time consumed by an EMS incident? Incident hours; total
time a unit spends in response to an EMS incident (time of dispatch to time unit
clears).
 Demand for EMS service has/is increasing; incident hours are increasing. Zone
car incident hours have experienced the greatest rate of growth.
 Average number of EMS units staffed and response time are positively
correlated; as the number of staffed units increase, the response time decreases.
As the number of units staffed decreases, the response time increases.
 Unit Hour Unit Utilization is the % of time a staffed unit (ambulance and/or zone
car) spends committed to an EMS incident in a 1 hour period.
 Unit Hour Unit Utilization correlates with EMS incident hours; as the number of
incident hours increase, the reported UHUU rate increases.
 The day shift experiences the majority share of EMS call demand and incident
hours, but has the lowest average units staffed to meet demand; day shift has a
higher reported UHUU rate.
 Recommend variable staffing model to meet EMS demand; add more staffed
units during peak demand hours to improve UHUU and ameliorate “call holding”
incidents. Target staffing to high demand areas.
 Initial findings reveal that zone cars have a higher reported UHUU rate than
ambulances. % increase in zone car incident hours outpaces increases in zone
car staffing.
 Despite increases in demand for EMS services, EMS system performance has
actually improved: decreases in response time, decreases in unit out of service
time, decreases in unit time at hospital.
 EMS system performance may be improved through efficiency enhancements
and resource utilization efforts.

2
Table of Contents
Background.................................................................................................................................................4
What are incident hours?......................................................................................................................4
What is response time?.........................................................................................................................5
Objective......................................................................................................................................................5
Additional objectives..............................................................................................................................6
Findings .......................................................................................................................................................7
Incident Hours.........................................................................................................................................7
EMS Incident Hours By Year............................................................................................................7
EMS Demand and Incident Hours.......................................................................................................8
EMS Transport Services .......................................................................................................................9
EMS Transports and Incident Hours Regression............................................................................10
EMS Unit Hours........................................................................................................................................12
EMS Units Staffed................................................................................................................................12
EMS Unit Hours and Incident Hours Per Hour ................................................................................14
Response Times.......................................................................................................................................15
Response Times Background ............................................................................................................15
VBEMS Response Times ...................................................................................................................16
EMS Unit Staffing and Response Time ............................................................................................17
EMS Unit Out Of Service Time ..............................................................................................................20
Unit Hour Unit Utilization.........................................................................................................................24
What is UHUU? ....................................................................................................................................24
VBEMS UHUU......................................................................................................................................26
Ambulance UHUU................................................................................................................................27
Zone Car UHUU ...................................................................................................................................29
Peak Demand UHUU...........................................................................................................................30
Limitations of UHUU ............................................................................................................................31
Not every EMS unit has the same UHUU ........................................................................................32
UHUU Shift 1.........................................................................................................................................34
UHUU Shift 2.........................................................................................................................................35
Should shift staffing change? .............................................................................................................36
The danger of high UHUU ..................................................................................................................38

3
Analyst’s Staffing Recommendation......................................................................................................38
Call Holding Incidents..........................................................................................................................39
Call Holding Incidents Per Shift..........................................................................................................40
Call Holding Per Hour..........................................................................................................................41
Staffing May Need To Be Targeted...................................................................................................47
Conclusion.................................................................................................................................................47
Works Cited...............................................................................................................................................50
Appendix....................................................................................................................................................51
Appendix A............................................................................................................................................51
Methodology......................................................................................................................................51
How was UHUU calculated? ..........................................................................................................51
Appendix B............................................................................................................................................53
Why Don’t We Rely On Average Response Time? ....................................................................53

4
Background
What are incident hours?
Incident hours are the total amount of time consumed by an EMS response; this
is the measure of time from when a unit is dispatched, to the time a unit reports
“clearing” an EMS incident. This measure is a calculation of the Hours: Minutes:
Seconds a unit reports in response to an EMS incident. Response to an EMS incident
entails the following: cancelled calls for service, arrival on scene and EMS not needed,
treatment provided but no transport required, treatment and transport provided, patient
refusal and standby. Anytime a unit leaves its location and is designated as in response
to an EMS incident is calculated as a demand for EMS service; a responding unit
therefore consumes available staffing time, incident hours and unit hours.
What is Unit Hour Unit Utilization?
Unit Hour Unit Utilization (UHUU) is the percent of time a staffed unit (e.g.
ambulance and/or zone car) is consumed by work; work in this context refers to
“amount of time a unit spends out of service in response to a demand for EMS service”.
UHUU has two base model calculations:
1. Simple Method
a. Calculate total unit hours by average total incidents
i. 1 incident =1 hour
ii. 1 unit =1 hour
2. Complex Method
a. Calculate total unit hours by actual total response hours
i. 1 incident ≠ 1hour (actual hours unit out of service reported)
ii. 1 unit = 1 unit hour
The simple method is an “Assumed Average” of UHUU. The logic holds that 1
incident = 1 EMS unit response and that 1 unit = 1 unit hour out of service time to
respond to that incident. This measure is not accurate and can be deceiving. An EMS
incident often involves multiple unit responses; not just one. In addition, a response to
an EMS incident can be < 1 hour. The simple method is mainly used because it is
“simple”. Some EMS agencies may not have an analyst to calculate actual unit out of
service time and/or may not have access to CAD1
data to calculate out of service time.
The complex method is a more valid and reflective calculation in determining
UHUU. The downside to the method is it takes more time to calculate. The complex
method calculates the actual reported unit out of service time for all EMS responses;
1
CAD=Computer Aided Dispatch

5
what is the actual amount of time that EMS units spent out of service in response to an
EMS incident. Dividing the actual unit out of service times with the reported unit hours
provides the UHUU rate; the % of time a unit spends “out of service” or “responding” to
EMS incidents in a 1 hour time frame (i.e. 1:00pm-2:00pm) etc.
What is response time?
Response time is the amount of time it takes a unit once dispatched to arrive on-
scene2
. This time calculation involves the total amount of time it requires a unit to
turnout, travel time to scene and arrival on scene; together, this provides a total
reported unit response time to an EMS incident. Reported response time was
benchmarked internally and external benchmarking is in progress3
; internal
benchmarking examined Virginia Beach EMS’s own system performance over the
observed periods of collected data. External benchmarking is being conducted to
examine VBEMS performance against national and state wide EMS provider’s system
performance4
. Benchmarking simply means comparing two things or more against one
another and seeing how they compare.
Objective
The goal of this analysis was to construct an historical analysis of response time
performance data that could be used to construct an internal benchmark and external
benchmarking procedure for further replication. Response time performance is a
measure that receives a great deal of attention and focus from the community,
government administrative personnel and elected members. Traditional measure of
response time has been conducted on an ad hoc basis, with no definitive procedure or
methodology for how the measure is to be collected or reported. In addition, there
existed no attempts to internally benchmark5
, let alone externally benchmark response
time performance in the context of VBEMS.
This analysis serves as a means by which to establish such a procedure and
methodology along with internally tracking EMS system performance.
A regression test will aid in quantifying if a correlation may be present between
response time and number of staffed units. Will more staffed units’ aid in improving
EMS response? Does the number of staffed units have an effect on the reported
2
Time of unit dispatch/unit notification to unit reports arriving on scene.
3
Waiting on data request from Virginia Department of Health; access to statewide NEMSIS reported data cube
4
National and state level response time values were provided through the NEMSIS clearing house; a data
warehouse that collects voluntary submissions of EMS data from EMS providers. The Virginia Department of
Health provided state level measures as they are able to access NEMSIS data that is particular to a state and
individual locality. VBEMS does not have access to state level of locality submitted data values.
5
On a regular and continual basis

6
response time to an EMS incident? This question may be supported by anecdotal
observation or institutional knowledge; however it does not statistically validate the
hypothesis. As such, the regression test will provide a quantitative examination to
discern if staffing and response time may be related and provide a measure to validate
anecdotal observations.
Does current staffing meet EMS demand needs? Examining the relationship
between EMS demand patterns and EMS unit staffing levels through Unit Hour Unit
Utilization (UHUU) calculation; determining if current EMS unit supply is sufficient in
meeting current and future demand needs, providing staffing recommendations based
on quantitative outputs.
Additional objectives
Analyzing historical actual unit out of service time via incident hours, can aid
concurrent demand analysis and demand forecasting analysis. Tracking out of service
time via unit hours dedicated to EMS demand can be used to aid in determining how
many EMS units would have been needed given the number of incidents which
occurred; forecasting analysis has demonstrated that the number of EMS incidents that
may probabilistically occur can be forecasted; however, the number of incidents is only
a partial means by which to determine the actual number of EMS units that may be
needed to meet the response needs of the incident (i.e. demand for service).
Calculating unit out of service time per incident along with unit hour can help
probabilistically determine how many units may be needed given the forecasted amount
for EMS incidents that may occur during the specified period of time. This will aid in
tracking and developing performance outcomes in the VBEMS system.

7
Findings
Incident Hours
EMS Incident Hours By Year
Figure 1: EMS incident hours per year; total units 2009-2014
Figure 2: EMS incident hours per year; ambulance units 2009-2014

8
Figure 3: EMS incident hours per year; zone car units 2009-2014
Figures 1-3 illustrate the reported number of EMS incident hours6
per calendar
year 2009-2014. The number of incident hours has continued along a positive linear
trend; a steady rate of increase per individual calendar year. Zone car units as
illustrated in figure 3 have experienced the greatest rate of increase in EMS incident
hours. Ambulance units have experienced a continued pace of growth at a much slower
rate of change than that of zone cars by comparison. Calendar year 2014 saw a 3%
increase in the number of EMS incident hours over the previous year (Ambulance hours
increased 5%, while Zone car hours decreased -3%). Statistical analysis identifies that
demand for EMS service (911 calls) and the type of service requested (transports,
treatment at scene) may be correlated with the amount of time an EMS unit spends in
service.
EMS Demand and Incident Hours
6
EMS incident hours are the total amount of time units spent in response to EMS incidents (i.e. time of dispatch to
clear time).

9
Figure 4: EMS demand and incident hours’ comparison; 2009-2014
Figure 4 is a comparison of EMS demand7
and EMS incident hours; the number
of EMS incidents has increased along a linear path along with EMS incident hours.
However, a regression analysis8
calculates that there is a positive correlation between
EMS demand and EMS incident hours; however, the inferred causal relationship is not
statistically significant. This simply means that while a large % change seen in one
variable may be explained by the change in the other, the probability of the results of
this change occurring by random chance is greater than 5%. This observation then
hypothesizes that increases in EMS demand may explain the increases in unit out of
service time, or put simply, the number of hours (time) a unit spends in response to an
EMS incident.
While there is no statistically significant correlation between EMS demand and
EMS incident hours, the data does provide the observation that both the number of
incidents and the number of hours dedicated to an incident have increased along a
positive linear trend during calendar years 2009-2014.
EMS Transport Services
Along with the increase in incident hours and EMS demand, the type of EMS
service provided has changed as well. EMS transport services (ALS and BLS
transports) have increased along a positive linear trend; a total increase of 16% during
the prior three years of observation. Previous analysis has shown that transport based
7
EMS demand is the total number of EMS incidents reported in a calendar year.
8
Regression calculation reported an R Square=0.61 and a Significance f=0.06

10
EMS services consume and require a greater share of time to complete9
; as the number
of transports have increased, this could partially aid in identifying why the number of
incident hours has increased, at least when examining ambulance unit hours10
.
Figure 5: # of EMS transport services; 2009-2014
EMS Transports and Incident Hours Regression
Figure 6: Regression statistics; EMS transports and incident hours 2009-2014
9
When comparing other forms of response requiring treatment
10
Ambulance units are capable of providing transport to hospital; zone cars do not provide transport services

11
Figure 7: Regression analysis; EMS transports and incident hours 2009-2014
Figure 6 (on page 10) are the regression statistics output from the regression
analysis. The results indicate that a strong positive correlation is present between the
number of EMS transports and the number of EMS incident hours; the R Square value
indicates that 81% of the change in incident hours can be explained by the change in
the number of EMS transports. The value Significance F indicates that the correlation is
statistically significant with an alpha=0.01 at the 95% confidence interval; this means
that the results of the calculation may have occurred at random only 1% of the time.
Figure 7 is the visualization of the regression analysis; as the number of EMS transport
services increases, it may probabilistically determine that EMS incident hours will
increase as well.

12
EMS Unit Hours
EMS Units Staffed
Figure 8: Average EMS units staffed; 2009-2014
Figure 9: Average EMS unit hours; 2009-2014
Figures 8 and 9 are the average number of EMS units staffed on a daily basis
and the equivalent average EMS unit hours. 1 staffed unit=1 unit hour; there are 24
hours in a given day and 365 days in a calendar year:

13
( )
N=the average number of staffed units. The calculation therefore is the number of
staffed units * 24 hours * 365 days = uh (unit hours). This calculation is only an
aggregate average of the actual staffed units in a given time period; it should be noted
that staffing can be variable, there was not always 14.6 units staffed every day of the
week throughout the 2009 calendar year. As such, there is a limitation as to the
accuracy that the average unit hour calculation can provide. However, it does provide
some benefit in being able to monitor historical unit staffing11
.
The data illustrates that as the demand for EMS services has increased12
, the
average number of staffed units has increased to match demand. The rationale for
increasing unit staffing is to increase the number of available unit hours; as time is finite
(fixed), time cannot be added nor subtracted. Therefore, in order to meet demand
(incident hours), supply (unit hours) is increased in order to ensure a deficit does not
occur (e.g. demand becomes greater than supply).
If demand exceeds supply, this would create what is known as “call holding”
incidents. Call holding simply means that when an individual calls 911 requesting an
EMS service, that call will have to wait until a unit becomes available for dispatch to
respond. If there are 10 units staffed at 10:00am, but there are 11 calls for EMS service,
the 11th
call will have to hold until one of the 10 units is able to clear its current response
and be available for dispatch.
The objective then becomes to match supply with demand in order to mitigate
“call holding” incidents. To accomplish this, EMS unit hours must be equal to or greater
than the number of incident hours.
11
Examining unit staffing by individual hour, by individual month by individual year provides a more robust and
accurate measure of unit staffing. The endeavor is time consuming, but beneficial for more in-depth analysis of
unit staffing and system performance
12
See figure 4 on page 9

14
EMS Unit Hours and Incident Hours Per Hour
Figure 10: Average unit hours & actual incident hours; 2009-2014
Figure 10 is the average unit hours in comparison to the actual reported incident
hours per hour of day from calendar year 2009-2014. The data illustrates incident hours
have a degree of variability or seasonality relating to hour of day; depending on the hour
of day, there is a variation in the amount of incident hours which occurred. Unit staffing
is not as variable; the staffing model for VBEMS is based on a 2, 12 hour shift per day
schedule. The average number of units staffed during a given day actually decreases
when the number of incident hours is increasing; unit staffing increases as incident
hours are decreasing13
.
13
While figure 10 provides valuable information in historically tracking available unit hours and incident hours,
utilizing an average unit staffing calculation for each hour period for an entire year is not greatly beneficial. Though
there may have been an average number of 14 units staffed at 10:00am during the year of 2009, this does not
express that there were actually 14 units staffed at 10:00am during the year of 2009 every month, day and hour of
day. An average is merely an aggregate of all the staffing for the period observed and not the actual. Unit staffing
is reported on a monthly basis which is then used to generate the average unit staffing data. A more beneficial
measure would be to track actual unit staffing per individual day and actual incident hours per day, then drilling
down to hour of day. The limitation to this method is the level of demand intensity such a calculation requires.
Currently, such an analysis has not been conducted at the time of this report. It will be explored in future research
and analysis endeavors.

15
Response Times
Response Times Background
The analysis has explored the historic changes in demand for EMS services by
tracking the actual reported incident hours and the number of available unit hours to
meet demand. One measure of performance that is synonymous with demand is what
is known as response time. Response time simply is the time it takes an EMS unit from
when it is notified of an incident; to the time that unit arrives on scene of that dispatched
incident. There is a great deal of disagreement in regard to whether response time has
any valid influence in relation to improved patient outcome (e.g. if an ambulance gets to
you faster you have a higher chance of survival). Existing medical research findings
express that response time to medical intervention is only beneficial in the most critical
of cases (e.g. cardiac arrest, stroke, respiratory failure etc…), but these cases often
make up a smaller proportionate share of the EMS demand that takes place14
.
Response times are an expectation customers/recipients/citizens have when
they pick up the phone and dial 911. Whether that request for EMS service be emergent
or not, the expectation is that the EMS unit will arrive in a timely fashion to provide
emergency medical services. As such, VBEMS has developed its own internally
developed performance response time benchmark in order to track system
performance. The performance objective is to have an EMS unit from time of dispatch to
arrive on-scene in 13 minutes and 59 seconds or under 90% of the time.
Traditionally, the response time target among EMS and Fire Medic providers has
been 8 minutes and 59 seconds; this measure is not a national standard, in fact, there is
no established national response time that can be benchmarked against. The 8:59
measure was created in the 1970’s by Alvarez and Cobb in Seattle, Washington while
they were studying cardiac survival rates. The authors found that increases in cardiac
survival rates were attributed to three factors:
1. Reduced response time (under 8 minutes)
2. First responders performing CPR
3. Bystander CPR (citizen CPR training)
The next four decades as a result were then defined by the 8 minute and 59
second response time as a result of these studies, further instituted by the National Fire
Protection Association15
. Geographic conditions of EMS service areas vary; urban
metropolitan areas with high population density have a higher degree of responding to
14
This analysis will not delve into the details of the existing literature and the debate over response time and
patient care as that is not the scope of this analysis.
15
(Fitch, 2007)

16
the 8:59 response time than rural, sparsely populated areas. The city of Virginia Beach
is unique from a geographic standpoint due to it variability in geography; there are areas
which are urban with high population density and then there are areas which are more
rural and sparsely populated per square mile. As such, utilizing a broad system wide
calculation of response time neglects taking into consideration these identified
variances in geography that influence response time performance of the VBEMS
system16
.
VBEMS Response Times17
Figure 11: EMS unit’s 90
th
percentile response time; 2009-2014
Figure 11 is the 90th
percentile18
response time for all EMS units (ambulance and
zone car) to all EMS incidents (emergent and non-emergent dispatched calls for
service). This calculation comprises total EMS unit response per incident; 1 incident ≠ 1
unit response. In other words, 1 incident = 1+ unit or more depending on the
classification of the incident, number of patients and severity of injury or urgency of
emergency medical need.
16
One idea for creating a more accurate response time calculation is to divide the city of Virginia beach land area
into census tract regions; this would allow the ability to break the city off into pre-defined zones and designate
them into different geographic categories/classification (e.g. rural, suburban, urban). Utilizing GIS address mapping
pulled from CAD data could then be used to place incidents into those respective geographic categories and
calculate response times that way.
17
See appendix B for an explainer on why we do not use average response times and use 90
th
percentile instead
18
90
th
percentile means that 90% of EMS response times are the time posted and below that value; the remaining
10% of calls are above that response time.

17
Example:
 Call comes in requesting EMS service for a car wreck; that is = 1 incident
 Upon arrival at the scene there are three patients requiring EMS service
 Three ambulance units and a zone car arrive on scene to provide treatment and
transport if needed
 1 incident=3 units
 3 units=3 different response times
One unit may arrive on scene in 35 seconds; the other two units do not arrive
until 00:12:00 minutes, as a result the 90th
percentile response time for that incident is
essentially 00:12:00 minutes and not 00:00:35 seconds.
Response time data does not capture 1st
help on scene, the time when the very
first EMS unit arrives on scene; response time is an aggregated calculation of the entire
EMS systems unit response. Instead, the 90th
percentile response time measure
provides a mechanism to aid in determining the amount of time all units take from
dispatch to arrival on scene 90% of the time; this means that 90% of the time, all units
responded in the time reported or less! The remaining 10% of calls were above the
reported time.
EMS Unit Staffing and Response Time
One of the objectives of this analysis was to run a regression test to quantify if a
correlation may be present between response time and number of staffed units. Will
more staffed units’ aid in improving EMS response? Does the number of staffed units
have an effect on the reported response time to an EMS incident? This question may be
supported by anecdotal observation or institutional knowledge; however it does not
statistically validate the hypothesis. As such, as regression test will provide a
quantitative examination to discern if staffing and response time may be related and
provide a measure to validate anecdotal observations.

18
Figure 12: Average staffed ambulance units and response time; 2009-2014
Figure 13: Average staffed zone car units and response time; 2009-2014
Figure 12 and 13 illustrates the data findings that as the average number of
staffed units have increased; the 90th
percentile response time to EMS incidents has led
to a decrease in the overall trend as a result. However, this does not validate if one
causes the other “causation”, nor does it necessarily even correlate (e.g. does the
change in response time change when the number of staffed units changes?). In order
to determine if there is a correlation between the two, a regression analysis was
employed to statistically test if there is a statistically validated correlation.

19
Figure 15: Regression analysis; average staffed ambulance units and response time 2009-2014
Figure 14 and figure 15 are the output of the regression analysis calculations.
The output generates that the average number of staffed EMS units and the reported
90th
percentile response time are correlated (strong negative relationship); the R
Square=0.81, and the Significance F=0.01. This translates that 81% of the change in
90th
percentile response time may be explained by the change in the average number of
staffed EMS units; with 99% confidence, there is a 1% chance that the results of the
regression output could have occurred due to random chance and the correlation is
considered statistically significant.
A regression analysis examining the average number of staffed zone cars and
the 90th
percentile response time found similar results:
Regression Statistics
R Square 0.89
Significance f 0.01
*Statistically Significant
Table 1: Regression statistics zone car staffing and response time; 2009-2014
What does this mean? This simply means that we can probabilistically determine
that an increase in the average number of staffed ambulances and zone cars may result
(respectively) in a decrease in the 90th
percentile response time; inversely, a decrease
in the average number of staffed EMS units may result in an increase in the 90th
percentile response time. Please note that correlation does not imply causation, instead
this simply suggests that the results of the calculation did not occur by random chance.

20
EMS Unit Out Of Service Time
EMS unit out of service time calculates the 90th
percentile that units spent out of
service in response to an EMS incident. This calculation is derived by taking the time
lapse from unit notified time to unit clear time. This represents the amount of time an
EMS unit reported being “out of service” 90% of the time.
Figure 16: EMS unit out of service time; 90
th
percentile 2009-2014
Figure 16 illustrates the 90th
percentile of reported out of service times for all
EMS units (ambulance and zone car) for calendar year 2009-2014. Calendar year 2011
marked the full first implementation year of the electronic medical recording (EMR)
program. The EMR program replaced the paper form and provided the means for
emergency medical care data to be captured and reported electronically. Unit out of
service times began decreasing following the peak reported unit out of service times in
calendar year 2011; whether this trend will continue in the foreseeable future is difficult
to assess as a discernable trend cannot be determined with confidence.

21
Figure 17: EMS incident hours and unit out of service 90
th
percentile; 2009-2014
Figure 17 illustrates that even though the number of incident hours has increased
during calendar years 2009-2014 the reported 90th
percentile of unit out of service time
began decreasing following calendar year 2011. This is an interesting finding given that
in addition to an increase in incident hours, the number of transport based service have
increased during the same period which in previous reports has been identified as the
greatest time consuming EMS service provided (when examining treatment based EMS
responses). Given this finding in addition to the earlier findings made in this analysis,
some variable is responsible for improving unit out of service time following calendar
year 2011.
Examine the following variables observed so far in this analysis:
 Unit response time has decreased annually each year following calendar
year 2010-2013
 Unit out of service time has decreased annually each year following
calendar year 2011-2013
 Incident hours have increased each annual year since calendar year 2009
 EMS demand has shown an overall linear increase from calendar year
2009-2014
Increases in overall demand for service appear to be responsible for the majority of
increases in both unit out of service time and response time.

22
Hypothesis:
Introduction of the EMR program has aided in improving unit out of service times;
providers spend less time completing reporting requirements. This reduction in unit time
dedicated to records data entry has been reduced, specifically in the amount of time a
unit spends at the hospital following a patient transport service. Reduction in unit time is
an identified performance enhancement; in essence, performance has been improved.
Less unit time consumed equates to more available unit hours returned to the VBEMS
system which can be dedicated to EMS response to calls for service.
Figure 18: EMS unit time at hospital; 90
th
Figure 18 is the 90th
percentile of time a unit reports being at the hospital
following transport services provided to a patient. Calendar year 2011 marks the first full
year of implementation of the EMR program. Each calendar year following 2010 has
seen a reduction in the amount of time a unit spends out of service at the hospital; this
reduction in time is occurring even while the number of EMS transport services is
increasing. There was an insignificant increase in year 2014 of less than 1 minute.

23
Figure 19: EMS transports and time at hospital; 90
th
While the hypothesis may not equivocally be accepted nor may it not be fully
rejected as to whether unit out of service times at hospitals have decreased as a result
of EMR program implementation19
, the data outcomes of the analysis do show that EMS
system response performance has improved even though demand for services has
increased. Response times have decreased, unit out of service times have decreased
and unit time spent at hospital has decreased as well during the period of observation
(e.g. calendar years 2009-2014). Regression analysis test statistically validated that
correlated observations are present between the following variables:
 As the number of incidents requiring EMS transport based responses increase, it
may probabilistically be determined that total incident hours will increase in kind.
 The average number of staffed EMS units (ambulance and zone car) correlate
with reported unit response time; that is to say, it may probabilistically be
determined that more units staffed will yield shorter response times and that
inversely, a reduction in the number of units staffed will yield longer response
times.
19
Data limitation prohibits the ability of quantifiably testing the hypothesis that EMR implementation has any
correlated effect to the change in unit out of service time spent at hospital. Data can only be visualized and an
educated inference may be made, but lacks the validity of statistically testing the relationship between the two
variables.

24
Unit Hour Unit Utilization
What is UHUU?
Why is time so important in EMS? Time in EMS is often synonymous with
response times, how much time it took to get from point A to point B; however, response
time is not the only area of EMS system performance that deals with the importance of
time. This research has discussed the concept of incident hours and unit hours: incident
hours is the total time dedicated to an EMS incident and unit hours is the total amount of
time units are able to provide during a time period.
These concepts of incident hours and unit hours are used in a performance
metric known as Unit Hour Unit Utilization; simply put, it measures the % share of
available time a unit spends in response (unit dispatch to unit clear) to an EMS incident:
 1 staffed ambulance= 1 unit hour
 an EMS response will put a unit out of service 1:22:4120
minutes 90% of the time
 The Unit Hour Unit Utilization for that 1 ambulance is = 138% unit utilization
 138% of that one staffed ambulances’ available unit time was spent in response
to an EMS incident
Unit Hour Unit Utilization mainly addresses two issues of concern in EMS systems:
1. Workload
2. Efficiency
Workload addresses concerns about overworking (e.g. burnout) or underworking
(e.g. depreciation of worker skill) providers and efficiency addresses concerns about
allocating resources in the mot optimal way available (e.g. getting the most output
possible at the lowest cost). Optimal Unit Hour Utilization rates have a degree of
variance and lack a nationalized or even regionalized standard. UHUU rates are
understood to have different baseline’s based on the demographics and characteristics
of the area served; an EMS system located in rural, sparsely populated area is
expected to have a low UHUU rate, while a metropolitan area with a small geographic
area and with high population density will experience higher UHUU.
20
See figure 16 on page 20; year 2014 data value.

25
Unit Hour Utilization Rate Level of Utilization
.55-.45 Optimal Utilization
.45-.35 Above Average Utilization
.35-.25 Average Utilization
.25-.15 Below Average Utilization
.15-0.1 Poor Utilization
Table 2: Unit hour utilization scale; J.R. Henry Consulting Inc.
21
Unit-hour utilization (UHU) is often used as a primary measure of EMS unit
workload…There is some evidence to suggest that a UHU of approximately 0.42
represents the optimum utilization for responding to emergency calls, balancing
availability, and productivity. Too far above 0.42 and personnel are arguably
overworked, and the unit availability is low (i.e., often busy when call arrives). Too far
below 0.42 and the cost-effectiveness of the unit could be questioned (p. 4)22
As you can see, UHUU varies from provider to provider or in this case from
consulting agency to consulting agency. The EMS consulting firm J.R. Henry Consulting
Inc. designates a utilization rate of 0.45-0.5 as optimal utilization, while Matric
Consulting Group pegs optimal utilization at 0.42. The City of Olympia, Washington
cites in its own report that publicly provided ambulance services (such as VBEMS)
consider 0.45 as the UHUU rate threshold23
; anything above 0.45 is considered over
utilization and requires staffing additional ambulances at that point.
Given the variance in regard to optimal UHUU rates among EMS systems, the
UHUU rate of 0.45 will be used as the baseline to measure benchmark optimal
utilization for VBEMS in the context of this research. VBEMS has developed its own
performance measures though the POMP process that identify a desired UHUU rate of
45% for Zone Car units and a UHUU of 40% for ambulances.
21
(J.R. Henry Consulting Inc., 2011)
22
(Emergency Medical System Delivery: Analysis of System Performance and Recommendation to Improve Service,
2012)
23
(Chapter 6-Emergency Medical Services, 2009)

26
VBEMS UHUU
Figure 20: Average # staffed ambulance units and uhuu; 2009-2014
Figure 21: Average # staffed zone car units and uhuu; 2009-2014
Figure 20-21 illustrates the average number of staffed ambulance units and zone
car units with their reported UHUU rate for calendar years 2009-2013. Calculating
UHUU over the course of a year is not the best time period to use in examining
utilization rates; previous research has presented findings that show demand for EMS

27
service fluctuates by month of year and by hour of day. In addition, this research has
presented that EMS unit staffing fluctuates between shifts in a given day. This analysis
as presented by figure 20-21 is more of a rough estimation of what UHUU was for the
year; however there is a lack of confidence in presenting these findings.
Why does UHUU not decrease for Zone Cars? As more units are staffed UHUU
is probabilistically supposed to decrease correct? Yes, probabilistically UHUU should
decrease as additional units are staffed and more unit hours are available to meet
incident hours (demand). However, if the change in growth of demand is greater than
the change in the unit hours provided by additional unit staffing, UHUU will not
decrease. Simply put, demand for zone car unit response is increasing faster than the
increase in additional zone car units staffed; supply does not meet demand. In this
case, demand continues to exceed supply.
Ambulance staffing on the other hand has been able to increase their unit supply
greater than changes in demand; as a result, UHUU for ambulance units have an
overall decreasing trend as a result of increased unit staffing.
Ambulance UHUU
Figure 22: Uhuu ambulance units by hour of day; 2009-2014

28
Figure 23: Uhuu ambulance units by hour of day; 2014
Figure 22 and 23 illustrates the level of variance that occurs between UHUU and
hour of day. As previous research has presented, demand for EMS services fluctuates
dependent on the hour of day or time of day. UHUU can drop as low as 15% and reach
as high as 51% based on the time of day. The red line identifies the UHUU threshold
which rests at the baseline of 40%; this is the identified benchmark utilization rate when
peak optimal utilization has been reached and additional units may be required to be
staffed to meet demand needs. Anything above 40% UHUU for ambulance units signals
when provider burnout may result among the volunteer members.

29
Zone Car UHUU
Figure 24: Uhuu zone car units by hour of day; 2009-2014
Figure 25: Uhuu zone car by hour of day; 2014
Figures 24 and 25 illustrate the level of variance that occurs between UHUU and
hour of day. As previous research has presented, demand for EMS services fluctuates
dependent on the hour of day or time of day. UHUU can drop as low as 16% and reach
as high as 51% based on the time of day. The red line identifies the UHUU threshold
which rests at the baseline of 45%; this is the identified benchmark utilization rate when

30
peak optimal utilization has been reached and additional units may be required to be
staffed to meet demand needs. Anything above 45% UHUU for zone car units signals
when provider burnout may result among the career medics.
Peak Demand UHUU
Peak demand hours have been identified to fall between the hours of 10:00am
and 6:00pm Monday-Sunday; this period of time is when demand for EMS service
reaches its highest and as a result requires the most demand from the EMS system and
its providers to provide response.
Figure 26: Average staffing and average peak demand uhuu ambulance; 2009-2014

31
Figure 27: Average staffing and average peak demand uhuu zone car; 2009-2014
Figures 26-27 illustrate the peak demand UHUU for ambulance and zone car
units. Zone car units have historically not exceeded the UHUU threshold of 45% across
the yearly average. Ambulance units however have continuously exceeded to 40%
UHUU threshold; however, improved staffing levels by increasing the average number
of staffed ambulance units per day has led to improvements in the UHUU during peak
demand hours. Demand for EMS services significantly impacts UHUU rates, as such;
improvements in staffing must be adjusted to match changes in demand.
Limitations of UHUU
Unit Hour Unit Utilization as powerful as a tool it may be in tracking and
measuring EMS system performance, it is not without its limitations. UHUU is only able
to calculate the time a unit spends in response to an EMS incident from the perspective
of the dispatcher, not the perspective of the provider. What does that mean? Simply put,
the amount of time a unit spends in a given 1 unit hour period can only be calculated
from the unit’s time of dispatch (when an EMS unit is notified of an incident) to the time
the unit reports clearing. The time that is not recorded nor reported by the CAD is what
is known as the “Logistics Gap”24
; the logistics gap is the amount of time a provider
spends checking the EMS unit, stocking the unit with needed supplies, inventory
management recording, cleaning the unit following transport of a patient and the amount
of time the unit spends traveling back to its respective station following completion of a
response to an incident.
24
(How Busy Are You, 2012)

32
UHUU should only be used to determine how busy an EMS unit or service is in
relation to EMS incidents and responses; UHUU does not fully reflect the additional
work, time and assignments that take place and occur outside of responding to
incidents. These other associated duties and responsibilities as a result may add
additional labor requirements upon individual providers25
.
Not every EMS unit has the same UHUU
Not every EMS unit is the same. Though there may be a total staffing of 15 units
per hour during a 24 hour period (day), those units may be spread out over a varying
degree of areas across the city, town, locality etc… Not every location or area that each
unit covers may receive the same amount of demand for EMS services as others.
Figure 23 (see page 26) is a density heat map of locations where demand for EMS
services occurred during the calendar year of 2014; the map on the left is during the
hours of 6:00am-6:00pm and the map on the right is during the hours of 6:00pm-
6:00am. This represents the two twelve hour shifts that VBEMS staffing operates under.
As the map illustrates, not every station (where EMS units are housed and
respond from) experience the same level of call demand during the day. Depending on
the location and the hour of day some stations experience more demand for EMS
service than others.
25
An EMS unit may spend 40 minutes in total time responding to an incident; however, that unit will then spend
10 minutes traveling back to its housing station, 5 minutes cleaning the unit and 5 minutes restocking the unit to
replace used supplies during the previous response incident. By that logic, 67% of that unit hour was spent
responding to an incident and the remaining 33% of the unit hour was spent returning to the station and preparing
the unit for the next response.

33
Figure 28: EMS gis density map of demand for service; 2014
Figure 29-3126
illustrates the difference that variation of time has on demand for
EMS services. The data presents EMS incident hours occur at a higher rate during shift
1 (e.g. the day shift) and occur at a lower rate during shift 2 (e.g. the night shift). UHUU
for shift 1 is also higher than the UHUU of shift 2; however, both shift 1 and shift 2 data
present that UHUU has continued along a negative linear trend following calendar year
2010 even as the number of EMS incident hours has inversely been increasing along a
positive linear trend from calendar year 2009-2014.
26
See pages 34-35

34
Figure 29: Uhuu and incident hours per shift; 2009-2014
UHUU Shift 1
Figure 30: Uhuu and average staffed units shift 1; 2009-2014

35
UHUU Shift 2
Figure 31: Uhuu and average staffed units shift 2; 2009-2014
Why does shift 1 have a higher UHUU than shift 2? That is the question figures
29-31 aids in answering. Shift 1 has a greater number of incident hours that take place
and a lower staffing level of EMS units than in comparison to shift 2 which is the
inverse; shift 2 has fewer EMS incident hours and more staffed EMS units. The more
units staffed=the more unit hours available to respond to EMS incidents; the more EMS
incidents there are, the more EMS unit hours are needed to meet demand. As a result,
a higher supply of staffed units along with a lower demand for EMS services (911 calls)
leads to a lower UHUU for shift 2, while shift 1 has a higher UHUU as it has a lower
amount of staffed units and a higher demand for EMS services.

36
Should shift staffing change?
Figure 32: % share of incident hours per shift; 2009-2014
Figure 32 illustrates that the majority share of EMS incident hours take place
during shift 1 and has increased overall along a positive linear trend.
Figure 33: % change incident hours shift 1; 2009-2014

37
Figure 34: % change incident hours shift 2; 2009-2014
The number of EMS incident occurrences has increased at a far greater rate over
each calendar year of observation when in comparison to shift 2; shift 1 has seen an
overall increase of 27% in incident hours while shift 2 has seen an increase of 15%27
.
Though there is a far greater occurrence rate of incident hours occurring during
shift 1 and that rate of occurrence has been increasing each annual year at a greater
rate than that of shift 2, the average number of staffed EMS units is below the staffing
provided during shift 2. In summation, shift 1 is responding to a higher demand for EMS
service with less available staffed units (e.g. resources).
This analysis has presented findings that as demand increases, so too does the
number of incident hours; as the number of incident hours increases, the higher the
reported UHUU becomes. As such, if we examined individual units over the course of a
24 hour period, it is hypothesized that those units located in the high demand areas of
the city28
may experience UHUU that far exceeds the 0.45 (45%) threshold.
27
Keep in mind that shift 1 has a larger number of incident hours and as such any % increase in those hours is
significantly larger than any % increase in shift 2 which has a lower number of incident hours.
28
Tracking individual station UHUU by location in the city based on density mapping has been difficult to
implement with the limitations of CAD data available to be accessed. Another issue is that though a unit may start
at one particular station for that day, it may travel between multiple station areas due to where it is located when
responding to an EMS incident. One option would be to examine the number of staffed units per station zone and
the number of incidents per station zone to determine UHUU per zone. Individual unit UHUU will require a higher
degree of analytic capability that was not met at the time of this report.

38
The danger of high UHUU
If demand outpaces the available supply of staffed units in a given area, this
creates a “call holding” situation; this means an individual requesting EMS service must
wait until the next available unit clears from its current assignment and is able to
respond. Could you just send a unit from another area that isn’t as busy? The answer is
“Yes”, but doing so would then reduce the available unit coverage for that area you took
the unit from. If a call comes in requesting EMS service for that area you just moved the
unit from, you are back where you started and have created another “call holding”
incident.
Why is “call holding” bad? Call holding may be bad for two chief reasons:
1. If it is an emergency medical situation that is triaged (designated) as an critical
incident (e.g. cardiac arrest), then not having an available unit to respond to that
incident may impact the patient outcome (e.g. condition worsens, possible death)
2. There is a public expectation that an ambulance will immediately respond to
every call for service.
Analyst’s Staffing Recommendation
How would “call holding” be ameliorated? High UHUU is the result of too much
demand and not enough available supply; in other words, there are more calls for EMS
service than there are available EMS units to respond. Demand for EMS service
fluctuates during the 24 hour period in a given day; demand for EMS service is at its
highest demand point during the day shift of operations (shift 1). However, the average
number of units staffed during the 24 hour period does not match demand patterns;
there are more staffed units on average staffed during the night shift (shift 2) even
though shift 1 comprises the majority share of call volume and rate of incident hours.

39
Call Holding Incidents
Figure 35: # call holding incidents; 2009-2014
Figure 35 illustrates the total number of reported call holding EMS incidents per
calendar year 2009-2014. Overall, the number of call holding incidents has decreased
each year following year 2010-2012. Years 2013 and 2014 have seen increases in the
occurrence rate of call HOLD incidents.
Figure 36: % change in call hold incidents; year 2009-2014

40
Call Holding Incidents Per Shift
Just as demand for EMS services fluctuates between time of day and between
shifts, the occurrence of call holding incidents fluctuates between shifts as well.
Figure 37: Call holding incidents per shift; 2009-2014
Figure 38: % of call holding incidents per shift; 2009-2014
Figure 37 and 38 illustrate as demand for EMS service and incident hours occur
at a higher rate during shift 1 (day shift), the occurrence of call holding incidents also
occur at a higher rate during shift 1. This is further visual confirmation of a possible

41
correlation between demand and call holding incidents (e.g. demand affects call
holding).
Call Holding Per Hour
Figure 39: Number of hold incidents by hour; 2009-2014

42
Figure 40: # call hold by hour of day; year 2014
Figure 41: # of call hold # change by hour of day; year 2013-2014

43
Figure 39-4129
illustrate call holding incidents by hour of day. As the data
illustrates, the call HOLD incidents visually correlate with the level of demand for EMS
service; the time period of 10:00am – 6:00pm has been identified as the peak demand
time period when demand of EMS service is at its highest.
Figure 42: Average # daily staffed zone cars by hour of day; year 2009-2014
Figure 43: Average # daily staffed zone cars & # call hold; year 2013-2014
29
Refer to pages 41-42

44
Figures 42 and 43 illustrate the average daily staffing of EMS units against the
occurrence of call HOLD incidents; as is presented in figure 3930
, the number of call
HOLD incidents occur most frequently during the peak demand period during the day
and during shift 1 when the number of staffed units is lower.
Figure 44: Number of call holding incidents and average number of staffed ambulances; 2009-2014
Figure 44 provides a visual correlation that as the average number of staffed
ambulances has increased, the total number of call holding incidents trend has
decreased. It has already been identified that during the same period (years 2009-2014)
that demand is increasing.
30
See page 43

45
Figure 45: Regression analysis average staffed ambulances and call hold incidents; 2009-2014
The regression analysis and statistics produced from the calculation provide the
confirmation that a correlation is present between the average number of staffed
ambulances and the number of call holding incidents; the R Square=0.62, 62% of the
change in call holding incidents may be explained by the change in the average number
of staffed zone cars. The Significance F value=0.06; with 94% confidence it can be
stated that the results of the regression analysis occurred as a result of random chance
6% of the time; it may probabilistically be determined that increasing the average
number of staffed ambulances will result in a reduction of the number of call holding
incidents.

46
Figure 46: Number of call holding incidents and average number of staffed zone cars; 2009-2014
Figure 46 provides a visual correlation that as the average number of staffed zone cars
has increased, the total number of call holding incidents trend has decreased. It has already
been identified that during the same period (years 2009-2014) that demand is increasing.
Figure 46: Regression analysis average staffed zone cars and call holding incidents; 2009-2014
The regression analysis and statistics produced from the calculation provide the
confirmation that a correlation is present between the average number of staffed zone
cars and the number of call holding incidents; the R Square=0.66, 66% of the change in
call holding incidents may be explained by the change in the average number of staffed

47
zone cars. The Significance F value=0.04; with 96% confidence it can be stated that the
results of the regression analysis occurred as a result of random chance 4% of the time;
it may probabilistically be determined that increasing the average number of staffed
zone cars will result in a reduction of the number of call holding incidents.
Staffing May Need To Be Targeted
Increasing the number of staffed units during high demand periods
probabilistically may result in decreased UHUU. Increasing the number of staffed units
will in turn increase the number of available unit hours; as long as the number of
available unit hours is greater than the incident hours (demand) then supply may be
sufficient in meeting demand for EMS service and reduce the occurrence of “call
holding” incidents. Increasing the number of staffed EMS units probabilistically may lead
to decreases in response times as well.
Just adding more staffed units will only help UHUU and reduce “call holding”
incidents if those additional units are staffed during high demand periods and where
UHUU rates are high. Simply adding more units to the system in an broad staffing
approach may not benefit the system as efficiently and limit performance improvements;
if you simply add units to the entire system with no specification to where and when
demand is high, there is a probabilistic likelihood that units would need to be moved
constantly from their posting area in order to travel to high demand areas and back. It is
recommended that a targeted staffing approach be made to add additional EMS
response units to high demand areas and high demand times of the day.
High demand areas can be determined by examining individual unit’s UHUU and
through density heat mapping of EMS demand. Staffing should be increased based on
demand patterns for EMS services; this research shows demand is higher during shift 1
and as such additional units are recommended to be staffed during this period.
Requiring the addition of 2-3 (or more) additional staffed units during the entirety of the
12 hour shift for shift 1 may not be feasible given limitations of available personnel; as
such, it is recommended that a variable staffing shift be implemented. Staffing additional
2-3 EMS units during the hours when demand reaches its peak (high demand); the
hours of 9:00am/10:00am – 5:00pm/6:00pm may lower UHUU during these hours,
improve response time and ameliorate “call holding” incidents as increases in demand
will be offset with increases in supply.
Conclusion
EMS system performance analysis reveals that demand for EMS services have
been increasing; more calls for EMS service and more incident hours. The type of
services being provided has experienced changes as well; more EMS responses are

48
requiring treatment and transport services. Despite these changes in increasing
demand patterns for EMS services, EMS system performance has surprisingly
improved during the periods of observation.
 Overall EMS response time has decreased annually
 Overall EMS unit out of service time has decreased annually
 Overall EMS time at hospital time has decreased annually
The more unit time that is returned to the EMS system, the more time that is
available to respond to EMS demand for service; this is identification of overall EMS
system performance enhancement and improvement. Demand is increasing, services
provided are increasing and the EMS system has an opportunity to generate
improvements in its delivery of Emergency Medical Services.
Further enhancements and improvements to EMS service delivery have been
identified within the body of this research. Statistical regression models provided
quantitative support that increasing the number of staffed units probabilistically may
improve response times and UHUU rates. The occurrence of call holding incidents may
probabilistically be mitigated through staffing additional EMS response units as well. A
targeted staffing model was recommended as the apt choice in addressing concerns
regarding call holding incidents and improving response times. A general blanket
approach of increasing overall unit staffing was decided to be both inefficient and
wasteful due to variability of demand.
Rigorous data analysis identified that though the majority of demand, incident
hours and unit hours dedicated for EMS service occurs during the day shift (shift 1), the
average number of staffed EMS units is less than the night shift (shift 2) which
comprises the minority share of EMS demand and incident hours. In conjunction to this,
the occurrence rate for incidents resulting in call holding is also subsequently higher
during the day shift (shift 1). The data findings indicate that adding additional staffed
units during the peak demand hours which occur during the day shift (shift 1) will
probabilistically ameliorate the occurrence rate of call holding incidents and improve
UHUU rates for units that operate during those peak demand times.
Additional units would not need to be staffed during the full 12 hour shift period;
instead, additional units could be staffed and targeted just during the hours in which
peak demand is taking place. Where to staff those additional units would be based upon
density demand analysis to identify areas, regions and zones where peak demand is
taking place.

49
Examination of Unit Hour Unit Utilization for the City of Virginia Beach’s
Department of EMS identifies variable Unit Hour Unit Utilization rates31
for EMS
response units in delivery of services. Determining an optimal rate of utilization for EMS
response units has been identified as a challenge as utilization excludes additional labor
and time that is dedicated to responses that are not captured by CAD reporting data. In
addition, there is no established maxim or standard that can be targeted for using as a
baseline to track EMS UHUU system performance. There are a varying degree of
“optimal utilization” rates that exist among EMS provider systems and EMS consulting
agencies. As a result, VBEMS recommends leveraging UHUU calculations in the
context of demand analysis and its effects on response times, call holding incidents and
provider workload.
UHUU relating to zone car staffing should be given additional examination as the
rate of demand for EMS services resulting in zone car unit responses has experienced
continuous rates of growth in UHUU among the periods observed in this analysis.
31
UHUU rates of .45-.55 are designated as optimal utilization (J.R. Henry Consulting Inc., 2011)

50
Works Cited
Amick, R. (2001, November 08). THE 1994 EXPLORER MOCK DISASTER. Retrieved
July 21, 2014, from http://bcn.boulder.co.us:
http://bcn.boulder.co.us/community/explorer/ep493d4c.htm
Chapter 6-Emergency Medical Services. (2009, May 01). Retrieved July 21, 2014, from
Olympia.gov: http://olympiawa.gov/~/media/Files/Fire/FireMasterPlan/6-
EMS.ashx
Emergency Medical System Delivery: Analysis of System Performance and
Recommendation to Improve Service. (2012, May 14). Retrieved July 21, 2014,
from Carsrescue.org: http://carsrescue.org/wp-
content/uploads/2012/Downloads/emsd.pdf
Fitch, J. (2007, June 26). Response Times: Myths, Measurement and
Management. Retrieved July 21, 2014, from JEMS.com:
http://www.jems.com/article/communications-dispatch/response-times-myths44-
measure
How Busy Are You. (2012, November 08). Retrieved July 23, 2014, from The Happy
Medic: http://thehappymedic.com/2012/11/how-busy-are-you/
J.R. Henry Consulting Inc. (2011, June 14). Calculating Your EMS Service's Average
Cost of Service And Unit Hour Analysis. Retrieved June 16, 2014, from
emsconsult.org:
http://www.emsconsult.org/images/Unit_Hour_Analysis_with_instructions.pdf

51
Appendix
Appendix A
Methodology
How was UHUU calculated?
Using the CAD data32
, call records were pulled from years 2009-201433
. The data
values examined for this analysis were:
 Call number (incident number)
 Curr_Dea (service designation i.e. fire, ems, police) etc34
.
 Time of dispatch (when unit was dispatched to incident)
 Date of dispatch (date when unit was dispatched to incident)
 Onscene time (when unit arrived on scene)
 Clear time (when unit reported it had cleared from the incident)
The time values reported by the CAD data were then converted into an
HH:MM:SS time format to calculate total response and out of service times. Response
times were calculated using the 90th
percentile; the response time recorded 90%35
of
total response times reported. Unit out of service was the sum total of all the hours
reported as units out of service. The unit hours were calculated by using Chief Brazle’ s
EMS unit daily staffing sheet; this provided the average number of EMS units staffed for
each calendar month during the calendar year. Unit hours were calculated as follows:
 1 staffed unit = 1 unit hour
 Total number of units staffed X 24 hours in a day = total unit hours per individual
day
To calculate unit hour utilization; the total EMS unit out of service incident hours
were divided by the total average unit hours to determine UHUU rate. This calculation
was then repeated for ambulance units only and zone car units only; total incident hours
are the total hours for those units only and not a combined summation of incident
hours36
.
32
CAD dataset was constructed by Robert M. Davis the Public Safety Analyst. Additional data value fields were
developed by the analyst in order to calculate time series values. CAD does not provide calculation, calculation
must be performed manually through excel formula construction. Data was then culled for errors or values that
needed to be excluded to ensure validated calculation.
33
Calendar years; January-December
34
A Curr_Dea value equal to EMS was used for this analysis.
35
This measure then reports that any measure above the reported value represents 10% of remaining
observations
36
The reported out of service time for ambulance units only and the reported out of service time for zone car units
only

52
A regression analysis was employed to test if a statistically significant correlation
was present between units staffed and response times. An analysis was also employed
to examine the relationship between incident hours and UHUU; the purpose of this
statistical calculation was to determine if a causal inference could be made between
incident hours (demand) and UHUU. To conduct the regression analysis, a regression
table was constructed with a Y variable column and an X variable column; each variable
represents the two data values being examined and used for the calculation. Data was
pulled from the existing data outputs performed by the analysis of this research. The
calculation was performed using the Microsoft excel data analysis tool to produce
regression statistics and the associated visualization of the regression calculation.

53
Appendix B
Why Don’t We Rely On Average Response Time?
EMS time data has a propensity to be right skewed in nature; in essence, EMS
time data favors one side more than the other on the time scale; as a result, this creates
what is known as non-normal distributions. Figure 26 provides the same data produced
in this analysis, but instead of using 90th
percentile, this data is calculated using average
response time. The output of the data is indeed more favorable in presentation; it looks
as though response times are even shorter. This effect is created by the non-normal
distribution of the EMS time data which skews to the right; this means that the time
reported for EMS incidents is weighted heavily at the 00:00:00 side of the Y-axis. As a
result, this pulls any reported data values closer to its side of the axis and skews the
reported values when the calculation is executed.
Carol Pugh serves as the Informatics Coordinator for the Virginia Department of
Health’s Emergency Medical Services Division; she has been working with Virginia EMS
providers to institutionalize moving away from using average based calculation in EMS
response times as the standard deviation for those calculations are often high.
While average response times may look better, they do little in providing real
benefit to examining EMS system performance in an accurate and valid manner.
Moving forward, utilizing 90th
percentile response time measure or median response
time reporting will either voluntarily become adopted by a majority of EMS providers or
will be institutionalized at the state level as data validation and reporting becomes more
prominent in the field.
Figure 41: Average EMS unit’s response time; all units 2009-2014

54
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