Measuring and Improving EMS System Performance

2014

Robert M. Davis, MPA

The City of Virginia Beach Department of

Emergency medical Services

7/31/2014

Measuring and Improving EMS System Performance

“EMS Incident Hours, Unit Hour Unit Utilization, Response Times

Analysis and Matching Demand to Resources”

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 2009-2013. 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 responding 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.

Shift 1 experiences the majority share of EMS call demand and incident hours, but has the lowest average units staffed to meet demand; shift 1 has a higher reported UHUU rate.

Recommend variable staffing model to meet EMS demand; add more staffed units during shift 1 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

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

3

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

4

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.

5

Findings

Incident Hours

EMS Incident Hours By Year

Figure 1: EMS incident hours per year; total units 2009-2013

Figure 2: EMS incident hours per year; ambulance units 2009-2013

6

Figure 3: EMS incident hours per year; zone car units 2009-2013

Figures 1-3 illustrate the reported number of EMS incident hours6 per calendar

year 2009-2013. 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 2013 saw no

change in the number of EMS incident hours over the previous year. Whether this trend

will continue is unknown.

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).

7

EMS Demand and Incident Hours

Figure 4: EMS demand and incident hours comparison; 2009-2013

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 not a strong positive correlation

between EMS demand and EMS incident hours and the inferred causal relationship is

not statistically significant. This simply means one does not change with the other. This

observation then hypothesizes that increases in EMS demand are not the sole effect

responsible for seeing 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 2009-2013.

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 13%

during the prior two years of observation. Previous analysis has shown that transport

based EMS services consume and require a greater share of time to complete9; as the

7 EMS demand is the total number of EMS incidents reported in a calendar year.

8 Regression calculation reported an R Square=0.49 and a Significance f=0.18

9 When comparing other forms of response requiring treatment

8

number of transports have increased, this could help partially in identifying why the

number of incident hours has increased, at least when examining ambulance unit

hours10.

Figure 5: # of EMS transport services; 2009-2013

EMS Transports and Incident Hours Regression

Figure 6: Regression statistics; EMS transports and incident hours 2009-2013

10

Ambulance units are capable of providing transport to hospital; zone cars do not provide transport services

Regression Statistics

Multiple R 0.869436314

R Square 0.755919503

Significance F 0.05551033

9

Figure 7: Regression analysis; EMS transports and incident hours 2009-2013

Figure 6 (on page 8) are the regression statistics output from the regression

analysis. The results indicate that a positive correlation is present between the number

of EMS transports and the number of EMS incident hours; the R Square value indicates

that 75% 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=0.05 at the 95% confidence interval; this means that the

results of the calculation may have occurred at random only 5% 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.

10

EMS Unit Hours

EMS Units Staffed

Figure 8: Average EMS units staffed; 2009-2013

Figure 9: Average EMS unit hours; 2009-2013

Figures 8 and 9 are the average number of EMS units staffed 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:

11

( )

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 6

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EMS Unit Hours and Incident Hours Per Hour

Figure 10: Average unit hours & actual incident hours; 2009-2013

Figure 10 is the average unit hours in comparison to the actual reported incident

hours per hour of day from calendar year 2009-2013. 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 decreasing.

*Analyst’s Comment*

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

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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.

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 place.

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.

Response time is however 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)

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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 Association13. Geographic conditions of EMS service areas vary; urban,

metropolitan areas with high population density have a higher degree of responding to

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

system14.

VBEMS Response Times15

Figure 11: EMS units 90th

percentile response time; 2009-2013

Figure 11 is the 90th percentile16 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

13

(Fitch, 2007) 14

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. 15

See appendix B for an explainer on why we do not use average response times and use 90th

percentile instead 16

90th

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.

15

classification of the incident, number of patients and severity of injury or urgency of

emergency medical need.

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.

Figure 12: EMS units 90th

percentile response time; priority 1 incidents 2009-2013

16

Figure 12 illustrates the 90th percentile response time for incidents designated as

priority 1; priority 1 is a triage code that helps determine a sorting order based on the

severity of injury or nature of injury to determine priority level in providing services17 .

Figure 11 (page 15) and figure 12 present the observation that total EMS unit response

times have decreased along a negative linear trend; the last three years have reported

decreases in the 90th percentile response time. This has occurred during a period when

demand for EMS services has been increasing along with more reported incident hours.

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.

Figure 13: Average staffed EMS units and response time; 2009-2013

Figure 13 illustrates the data findings that as the average number of staffed units

have increased; the 90th percentile response time to EMS incidents has decreased as a

17

Priority 1 (Red) Serious but salvageable life threatening injury/illness. Victims with life-threatening injuries or illness (such as head injuries, severe burns, severe bleeding, heart-attack, breathing-impaired, internal injuries) are assigned a priority 1 or "Red" Triage tag code (meaning first priority for treatment and transportation) (Amick, 2001)

17

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

correlation.

Figure 14: Regression statistics; average staffed EMS units and response time 2009-2013

Figure 15: Regression analysis; average staffed EMS units and response time 2009-2013

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=0.03. 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 95% confidence, there is a 3% chance that the results of the

regression output could have occurred due to random chance and the correlation is

considered statistically significant.

What does this mean? This simply means that we can probabilistically determine

that an increase in the average number of staffed EMS units may result 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


Multiple R 0.905532

R Square 0.819989


18

note that correlation does not imply causation, instead this simply suggests that the

results of the calculation did not occur by random chance.

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; 90th

percentile 2009-2013

Figure 16 illustrates the 90th percentile of reported out of service times for all

EMS units (ambulance and zone car) for calendar year 2009-2013. 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.

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Figure 17: EMS incident hours and unit out of service 90th

percentile; 2009-2013

Figure 17 illustrates that even though the number of incident hours has increased

during calendar years 2009-2013 along with EMS demand in general (see figure 4 page

7) 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 (see figure 5 page 8) 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

Unit out of service time has decreased annually each year following calendar

year 2011

Incident hours have increased each annual year since calendar year 2009

EMS demand has shown an overall linear increase from calendar year 2009-

2013

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

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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; 90th


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.

21

Figure 19: EMS transports and time at hospital; 90th


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 implementation18, 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 2009-2013). Regression analysis test statistically validated that correlated relationships 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

(see figure 6 and 7 on pages and 9).

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 (see figure

15 page 17) and that inversely, a reduction in the number of units staffed will

yield longer response times.

18

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.

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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 00:43:00 minutes 90% of the time

The Unit Hour Unit Utilization for that 1 ambulance is = 43% unit utilization

43% 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.

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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 1: Unit hour utilization scale; J.R. Henry Consulting Inc.

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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)20

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 threshold21; 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 optimal utilization for

VBEMS in the context of this research.

19

(J.R. Henry Consulting Inc., 2011) 20

(Emergency Medical System Delivery: Analysis of System Performance and Recommendation to Improve Service, 2012) 21

(Chapter 6-Emergency Medical Services, 2009)

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VBEMS UHUU

Figure 20: Average # staffed units and uhuu; 2009-2013

Figure 20 illustrates the average number of staffed units (ambulance and zone

car) and the 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 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 is more of a rough estimation of what UHUU was for the year; however there

is a lack of confidence in presenting these findings.

25

Figure 21: Uhuu all EMS units by hour of day; 2009-2013

Figure 21 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 16% and reach

as high as 52% based on the time of day. The red line in figure 21 identifies the UHUU

threshold which rests at the baseline of 45%; this is the identified utilization rate when

optimal utilization has been reached and additional units may be required to be staffed

to meet demand needs.

26

Figure 22: Uhuu and incident hours by hour of day; 2009-2013

Figure 22 is the same data presented in figure 21 (see page 25) with the addition

of the total reported incident hours for calendar year 2013. The visualization of the data

illustrates that as incident hours increase, UHUU increases in kind. In other words, as

demand for EMS services increase, the amount of time staffed units spend out of

service increases.

27

Figure 23: regression analysis incident hours and uhuu all units; 2009-2013

Figure 24: Regression statistics incident hours and uhuu all units; 2009-2013

Figure 23 and figure 24 are the calculated outputs of the regression statistical

test to determine if a correlated relationship is present between total incident hours and

UHUU. The R Square=0.89; this means that 89% of the change in UHUU can be

accounted for as a result of the change in total incident hours, this signals a strong

positive relationship and that correlation is present between the variables. The

Significance F=3.9; this means that the probability that the results of the regression test

may occur as a result of random chance is 40%. As a result, it can be said with 95%

confidence that when the number of incident hours increase, the UHUU rate

probabilistically may increase (or decrease) 60% of the time not due to random chance.

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,



R Square 0.897215187

28

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”22; 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.

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 providers. 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.

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 25 (see page 29) is a density heat map of locations where demand for EMS

services occurred during the calendar year of 2013; 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 2 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.

22

(How Busy Are You, 2012)

29

Figure 25: EMS gis density map of demand for service; 2013

Figure 26 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 the second shift (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 2009-2013.

30

Figure 26: Uhuu and incident hours per shift; 2009-2013

UHUU Shift 1

Figure 27: Uhuu and average staffed units shift 1; 2009-2013

31

UHUU Shift 2

Figure 28: Uhuu and average staffed units shift 2; 2009-2013

Why does shift 1 have a higher UHUU than shift 2? That is the question figures

27 (see page 30) and figure 28 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.

32

Should shift staffing change?

Figure 29: % share of incident hours per shift; 2009-2013

Figure 29 illustrates that the majority share of EMS incident hours take place

during shift 1 and has increased overall along a positive linear trend.

Figure 30: % change incident hours shift 1; 2009-2013

33

Figure 31: % change incident hours shift 2; 2009-2013

The numbers of EMS incident occurrences have 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 21% in incident hours while shift 2 has seen an increase of 18%.

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.

Figure 32: Incident hours and average staffed units per shift; 2009-2013

34

Figure 32 illustrates the chief concern regarding average EMS unit staffing and

incident hours per shift. 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. Though the staffing difference may only be 1

to 2 staffed unit’s difference, keep in context that the numbers of incident hours that

take place during shift 1 are 46% greater on average than that of 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 city23 may experience UHUU that far exceeds the 0.45 (45%) threshold.

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

emergent incident (e.g. cardiac arrest) then not having an available unit to

respond to that incident may impair the patient outcome (e.g. condition worsens,

possible death)

2. VBEMS as a public provider EMS system adheres to a principle of “quality

customer service” to the residents and visitors of the City of Virginia Beach; this

simply means that residents tax dollars are used to fund VBEMS operations (to

an extent) and as such have an expectation of timely service delivery

23

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.

35

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. Figure 22 (see page 26)

illustrates that 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) as illustrated in figure 26 (see page 29). 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

(see figure 32 page 33 and figure 29 page 31).

Call Holding Incidents

Figure 33: # call holding incidents; 2009-2013

Figure 33 illustrates the total number of reported call holding EMS incidents per

calendar year 2009-2013. Overall, the number of call holding incidents has decreased

each year following year 2010.

36

Figure 34: # call holding incidents; 2009-2013

Figure 34 illustrates that the number of call holding incidents fluctuates based on

time of month which matches similar trends found in VBEMS’s demand analysis

findings. In essence, as demand increases, the number of call holding incidents

increases in kind.

Figure 35: hold incidents and average units staffing year 2013; 2013

Figure 35 illustrates the average number of staffed EMS units and the reported

number of call holding incidents per hour of day during calendar year 2013. The findings

37

mimic the results found in VBEMS’s demand analysis; the black box in figure 35

represents the time span identified as the “peak demand hours”. Peak demand is the

time of day by which demand for EMS service reaches its highest level. Visually, it

would appear that demand for EMS service and the number of call holding incidents

correlate24.

Call Holding Incidents Per Shift

Just as demand for EMS services fluctuates between time of day and between

shifts (see figure 29 page 31); the occurrence of call holding incidents fluctuates

between shifts as well.

Figure 36: Call holding incidents per shift; 2009-2013

24

See appendix (page ) for visualizations of years 2009-2012

38

Figure 37: % of call holding incidents per shift; 2009-2013

Figure 36 and 37 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 of occurrence during shift 1. This is further visual confirmation of a

possible correlation between demand and call holding incidents (e.g. demand affects

call holding).

Call Holding Incidents and Unit Staffing

Figure 38: Number of hold incidents by hour; 2009-2013

39

Figure 39: Average staffed units by hour; 2009-2013

Figure 38 and 39 illustrate the occurrence of call holding incidents and the

reported average unit staffing by hour of day from calendar year 2009-2013. The light

red shading in the graphs identify the time period of the day which comprises shift 1

(day shift). The data leads to the hypothesis that unit staffing and the occurrence of call

holding incidents are correlated; that is to say, that as the number of staffed units

increases, the reported occurrence rate of call holding incidents should decrease and

vice versa.

40

Figure 40: Number of call holding incidents and average number of staffed units; 2009-2013

Figure 40 provides a visual correlation that as the average number of staffed units has

increased, the total number of call holding incidents has decreased. It has already been

identified that during the same period (years 2009-2013) that demand is increasing.

Figure 41: Regression statistics average staffed units and call holding incidents, 2009-2013



R Square 0.74359162


41

Figure 42: Regression analysis average staffed units and call holding incidents; 2009-2013

The regression analysis and statistics produced from the calculation provide the

confirmation that a correlation is present between the average number of staffed units

and the number of call holding incidents; the R Square=0.74, 74% of the change in call

holding incidents may be explained by the change in the average number of staffed

units. The Significance F value=0.06; with 95% confidence it can be stated that the

results of the regression analysis occurred as a result of random chance 6% of the time;

94% of the time, it can probabilistically be determined that increasing the average

number of staffed units will result in a reduction of the number of occurred call holding

incidents.

42

Call Holding Incidents and UHUU

Figure 43: Number of call holding incidents and uhuu; 2009-2013

Figure 44: Regression statistics uhuu and call holding incidents; 2009-2013

Figure 45: Regression analysis uhuu and call holding incidents



R Square 0.927483965


43

The regression analysis and statistics produced from the calculation provide the

confirmation that a correlation is present between the UHUU and the number of call

holding incidents; the R Square=0.92, 92% of the change in call holding incidents may

be explained by the change in UHUU. The Significance F value=0.008; with 95%

confidence it can be stated that the results of the regression analysis occurred as a

result of random chance 0.8% of the time; 99.92% of the time, it can probabilistically be

determined that decreasing the UHUU of staffed units will result in a reduction of the

number of occurred call holding incidents.

Staffing Needs To Be Targeted

Increasing the number of staffed units during high demand periods

probabilistically may result in decreased UHUU (see figures 27 and 28 page 30).

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 (see figure 42 page 40). Increasing the

number of staffed EMS units probabilistically may lead to decreases in response times

as well (see figure 15 page 17).

Just adding more staffed units will only help UHUU and reduce “call holding”

incidents if those additional units are staffed in high demand areas where UHUU rates

are high. Simply adding more units to the system in an broad staffing approach may not

benefit the system and hurt performance and efficiency; if you simply add units to the

entire system with no specification to where demand is high, there is a probabilistic

likely-hood 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 (see figure 25 page 29). 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 – 4:00pm/5:00pm (see figure 22

page 26) 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.

44

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

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. Though demand is increasing,

services provided are increasing; the EMS system has managed to generate

improvements in its service delivery components of operations.

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 (see figure 29 page

31). 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

45

density demand analysis to identify areas, regions and zones where peak demand is

taking place.

Examination of Unit Hour Unit Utilization for the City of Virginia Beach’s

Department of EMS identifies variable Unit Hour Unit Utilization rates25 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.

25

UHUU rates of .45-.55 are designated as optimal utilization (J.R. Henry Consulting Inc., 2011)

46

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

47

Appendix

Appendix A

Methodology

Methodology

How was UHUU calculated?

Using the CAD data26, call records were pulled from years 2009-201327. The data

values examined for this analysis were:

Call number (incident number)

Curr_Dea (service designation i.e. fire, ems, police) etc28.

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%29 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

26

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. 27

Calendar years; January-December 28

A Curr_Dea value equal to EMS was used for this analysis. 29

This measure then reports that any measure above the reported value represents 10% of remaining observations

48

are the total hours for those units only and not a combined summation of incident

hours30.

Regression analysis

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.

30

The reported out of service time for ambulance units only and the reported out of service time for zone car units only

49

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 46: Average EMS units response time; all units 2009-2013

50

Appendix C

Average Staffed Units and UHUU (Ambulance and Zone Car)

Figure 47: Average staffed ambulance units and uhuu; 2009-2013

Figure 48: Average staffed zone car units and uhuu; 2009-2013

51

Appendix C (continued)…

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 (see figure

3 page 6) than the increase in additional zone car units staffed; supply does not meet

demand.

Figure 49: % change in incident hours and staffed units; zone car 2009-2013

52

Appendix C (continued)…

Ambulance staffing on the other hand has been able to increase supply greater

than changes in demand; as a result, UHUU for ambulance units have decreased as a

result of increased unit staffing.

Figure 50: % change in incident hours and staffed units; ambulance 2009-2013

53

Appendix D

Hold Incidents and Average Units Staffed Per Hour

Figure 51: Hold incidents and average units staffing; 2009


54

Appendix D (continued)…



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Data & Analytics

Measuring and Improving EMS System Performance