Stoutian SSM

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Stoutian SSM
Frank Gresh Chief Information Officer - EMSA

• Jonathan D. Washko, BS-EMSA, NREMT-P
• Director of Strategic Development REMSA
• President Washko Associates, LLC

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Stoutian SSM

• Discussion Topics
• Stoutian philosophy and background
• What is a Temporal Demand Analysis
• What kind of data do you need to calculate it
• What are some of the pitfalls to watch out for
• What formulas do you use to calculated it
• What tools do you use to calculate it
• What do you do with this information when
  completed
• Research on the topic

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Stoutian Philosophy

• Jack was an Economist
• Jack proved that demand for our services was
  predictable on two distinct variables
• How many
• Where
• Therefore production model economic principles,
  approaches and sciences (those found in
  manufacturing) can also be applied to a service
  industry
• Named our product A Unit Hour
• Product then provides a quality service as an end
  result of a quality product
• Quality definition redefined for the industry
• Fractile Response Time Reliability vs Average
• Public Utility Model EMS System

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The EMS Product
So What is A Quality Unit Hour (QUH)? A
Quality Unit Hour is an ambulance that is
available to the EMS System for one hour that
responds to properly triaged calls for service,
is produced within a CQI environment that uses
modern technology to collect and assess accurate
data, is fully staffed, fully trained, fully
maintained, fully stocked, properly placed in
location and time, properly funded and safely
operates within an educated population
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Patient Care Employee Wellbeing
Financial Stability
Public Education
Control Center
Training Edu
Human Resources
Finance
Operations
The Quality Unit Hour
Supply / Logistics
Data Analytics
Safety Risk
QI / CQI / PI
Fleet Maint.
IT / Technology
PR/Marketing
The Quality Unit Hour Concept
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Temporal Demand Analysis Peak Load Staffing
Models
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Analyzing Demand Data
What is a Temporal Demand Analysis? A Temporal
Demand Analysis (or TDA) is an analysis of
arrayed and aggregated historical call volume by
week, hour of day and day of week. It is used to
help predict and determine the number of Quality
Unit Hours needed (Demand) for each hour of the
day and day of week. When completed, the
analysis will provide staffing needs for a total
of 168 hours (total number of hours in a week).
From this analysis, a Peak Load Staffing Schedule
can be built to match the prediction model
(Matching Supply with Demand).
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Analyzing Demand Data

• Temporal Demand Analysis Fundamental Assumptions
• Assumes Each Call Takes one hour to complete (11
  S/D Ratio)
• Needs to be adjusted to each system accordingly
• Use Task Time to adjust as needed if average is
  gtlt 60 minutes
• Systems with lower Task Times require less
  resources
• Systems with higher Task Times require more
  resources
• Adjustments can be made through demand
  multipliers or the performing of a Task Time TDA
  (A much more complex analysis)

Efficiency Alert! Controlling your systems Task
Time can have a HUGE financial impact on your
system staffing costs so long as controls are
kept to balance the triad.
Pitfall Alert! Inaccurate Task Time calculations
can substantially impact the outcome of a demand
analysis and put patient lives or an organization
at risk. Perform the Task Time Analysis with due
diligence and caution ensuring accuracy and
validity!
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Analyzing Demand Data

• Data Set Characteristics
• Bad in / bad out concept
• What to measure and why
• Requests, Responses or Transports?
• Call Priorities to include or exclude
• Standby / Special Events
• Multi-Unit Responses
• Other Variables (CCT, Specialized Units, Special
  Calls, Special Circumstances, etc.)

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Analyzing Demand Data

• Other Things You Need to Know
• Desired response time reliability percentage
• Inefficiency (LUH) buffer / cushion
• Call volume seasonality
• Some Art (SWAG)
• Response time requirements
• Response time zone balancing requirements
• Effects of city infrastructure (or lack there of)
• Effects of traffic patterns
• Effects of political Posts
• Effects of other unique system anomalies

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Analyzing Demand Data

• Extracting your data from CAD for Analysis
• Need to understand your CAD database schema
• How data is stored
• What table(s) it is in
• How the table relationships / keys work
• What fields to use to get you the data you want
• What format is the data in and does it need to be
  converted
• Need to understand your agencys reporting
  hierarchy and code files in CAD
• Response areas
• Priorities / Call Types
• Clock Start
• Cancel Types
• How certain types of calls you want to include
  are captured in the database

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Analyzing Demand Data

• Extracting your data from CAD for Analysis
• Need to query and filter your data to get
  accurate results
• Use SQL views or create queries via ODBC
  connection to your SQL database
• Date / time range of the dataset
• Data filters needed to get the types of calls you
  want to analyze
• Service line types to include or exclude
• Service areas to include or exclude
• Priorities / call types
• Other data anomalies
• Output your data into a usable format for your
  analysis template
• Excel, Access, Crystal, Etc

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Analyzing Demand Data

• Once your data is filtered and extracted, it then
  needs to be aggregated into Hour of Day (HOD) and
  Day of Week (DOW) formats
• Excel Pivot Tables
• Access Cross Tab Query

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Analyzing Demand Data

• Extracting your data from CAD for Analysis
• Data Array format and data fields needed for
  proper aggregation
• Day of week (XL formula Text(REF,DDD)
• Military date format (XL formula
  Text(REF,YYYYMMDD)
• Hour of day in hour ending (HE) format (XL
  formula Hour(REF)1)
• Build your array from this dataset as such

Monday HE1 HE2 HE3 HE4
20070507 0 2 3 1
20070514 1 3 2 5
20070521 2 5 2 3
20070528 5 2 3 0
And so on
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Analyzing Demand Data

• From this point, you then take this arrayed data
  and plug it (copy/paste) into a Temporal Demand
  Analysis (TDA) template similar to the one shown
  in this next segment

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A Temporal Demand Analysis for Monday
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A Temporal Demand Analysis for Monday

• Raw Demand Analysis Data.
• P1, P2, P3, P4 P7 Count of responses that
  arrived on scene by hour of day, day of week,
  chronologically ordered by date.
• A total of 20 weeks worth of most recent data
  from the CAD system.

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A Temporal Demand Analysis for Monday
Military Date Format of Arrayed Days (Mondays)
in Chronological Order In this case the date is
Monday February 03, 2003

• Raw Demand Analysis Data.
• P1, P2, P3, P4 P7 Count of responses that
  arrived on scene by hour of day, day of week,
  chronologically ordered by date.
• A total of 20 weeks worth of most recent data
  from the CAD system.

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A Temporal Demand Analysis for Monday
Hours of Day in Hour Ending Format e.g. 21
2000 through 2100

• Raw Demand Analysis Data.
• P1, P2, P3, P4 P7 Count of responses that
  arrived on scene by hour of day, day of week,
  chronologically ordered by date.
• A total of 20 weeks worth of most recent data
  from the CAD system.

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A Temporal Demand Analysis for Monday
Total of All Hours for Each Week (Totaled
Across) In this case, there were 196 Responses on
Feb. 10, 2003

• Raw Demand Analysis Data.
• P1, P2, P3, P4 P7 Count of responses that
  arrived on scene by hour of day, day of week,
  chronologically ordered by date.
• A total of 20 weeks worth of most recent data
  from the CAD system.

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A Temporal Demand Analysis for Monday
Represents that on February 17, 2003 there were
13 Responses between 1100 and 1200

• Raw Demand Analysis Data.
• P1, P2, P3, P4 P7 Count of responses that
  arrived on scene by hour of day, day of week,
  chronologically ordered by date.
• A total of 20 weeks worth of most recent data
  from the CAD system.

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A Temporal Demand Analysis for Monday

• Demand Analysis Analytics.
• Used to calculate the required number of Quality
  Unit Hours (Demand) by Hour of day for this
  particular day of the week (In this case, Monday)
• There are various statistical methods used to
  calculate system demand, all are accurate and
  correct. Experience has shown that Average Peak
  (a formula created by Jack Stouts team)
  consistently yields an accurate prediction of the
  90th Percentile of demand.

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A Temporal Demand Analysis for Monday
The Average High is a Stoutian Measurement that
represents approximately the 75th percentile of
demand. It is calculated by taking the maximum
number of calls in each consecutive 5 4 week
periods of a 20 week analysis then dividing the
sum of these number by 5 (or average of the 5
periods) In this example, the Average High for
0300 to 0400 5.8 The XL Formula
(Max(CRCR) Max(CRCR) Max(CRCR)
Max(CRCR) Max (CRCR)) / 5 The resultant is
then multiplied by the TMT Multiplier for TMT
Adjustments
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A Temporal Demand Analysis for Monday
The Average Peak is a Stoutian Measurement that
represents approximately the 90th percentile of
demand. It is calculated by taking the maximum
number of calls in each consecutive 2 10 week
periods of a 20 week analysis then dividing the
sum of these number by 2 (or average of the 2
periods) In this example, the Average Peak for
0300 to 0400 8.0 The XL Formula
(Max(CRCR) Max(CRCR) ) / 2 The resultant is
then multiplied by the TMT Multiplier for TMT
Adjustments
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Stoutian SSM - Research
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Stoutian SSM - Research
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Stoutian SSM - Research
The research conducted asked the question can the
know methods for EMS demand analysis predict call
volume? Assessed many of the same mathematical
models shown today Stoutian Theory (Average
Peak) Smoothed Average Peak 90th Percentile
Ranking
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Stoutian SSM - Research

• The Results

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Stoutian SSM - Research

• The Results

This lends one to interpret that this doesnt
work.HOWEVER
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Stoutian SSM - Research

• The Results

Understand that Demand Analysis was not designed
to predict Call Volumeits designed to show what
staffing would need to be to meet a 90
reliability standard.which these results prove
when interpreted properly (Actually its 96
accurate!!!!!!)
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Stoutian SSM - Research

• The Results

My Conclusions It works and works well based on
my years of experience. Unfortunately the
researchers asked the wrong question
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Many ways to the dance

• Remember who we are doing this for
• Patients
• Crews
• Might be more than one right way.
• Dont get hung up on the numbers.
• What works for you and your system?

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Questions Contact Information EMSA Phone
405-297-7053 Email greshf_at_emsa.net REMSA Phone
775-858-5700 x140 Email jwashko_at_remsa-cf.com Web
www.REMSA-CF.com