Stoutian SSM

Stoutian SSMStoutian SSM

Jonathan D. Washko, BS-EMSA, NREMT-PDirector of Strategic Development – REMSA

President – Washko & Associates, LLC

Frank GreshChief Information Officer - EMSA

Stoutian SSMStoutian 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

Stoutian PhilosophyStoutian 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

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

The EMS “Product”The EMS “Product”

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Human Resources

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Supply / Logistics Data Analytics

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The QualityUnit Hour Concept

Temporal Demand Analysis Temporal Demand Analysis & Peak Load Staffing & Peak Load Staffing

ModelsModels

Analyzing Demand DataAnalyzing 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).

Temporal Demand Analysis Fundamental Assumptions

Assumes Each Call Takes one hour to complete (1:1 S/D Ratio) Needs to be adjusted to each system accordingly Use Task Time to adjust as needed if average is >< 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 system’s 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!


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.)


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


Analyzing Demand DataAnalyzing 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 agency’s 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


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

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



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


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…

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.




Military Date Format of Arrayed Days (Mondays) in Chronological OrderIn this case the date is Monday February 03, 2003




Hours of Day in Hour Ending Formate.g. 21 = 20:00 through 21:00




Total of All Hours for Each Week (Totaled Across)

In this case, there were 196 Responses on Feb. 10, 2003




Represents that on February 17, 2003 there

were 13 Responses between 11:00 and 12:00


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 Stout’s team) consistently yields an accurate prediction of the 90th Percentile of demand.


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 03:00 to 04:00 = 5.8

The XL Formula: =(Max(CR:CR) + Max(CR:CR) + Max(CR:CR) + Max(CR:CR) + Max (CR:CR)) / 5The resultant is then multiplied by the TMT Multiplier for TMT Adjustments


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 03:00 to 04:00 = 8.0

The XL Formula: = (Max(CR:CR) + Max(CR:CR) ) / 2The resultant is then multiplied by the TMT Multiplier for TMT Adjustments


Stoutian SSM - ResearchStoutian 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


The Results:


The Results:

This lends one to interpret that this doesn’t work….HOWEVER


The Results:

Understand that Demand Analysis was not designed to predict Call Volume…it’s designed to show what staffing would need to be to meet a 90% reliability standard….which these results prove when interpreted properly (Actually it’s 96% accurate!!!!!!)Actually it’s 96% accurate!!!!!!)


The Results:

My Conclusions:It works and works well based on my years of experience. Unfortunately the researchers asked the wrong question


Many ways to the dance…Many ways to the dance… Remember who we are doing this

for… Patients Crews

Might be more than one right way. Don’t get hung up on the numbers. What works for you and your

system?

Questions & Contact Information

EMSAPhone: 405-297-7053Email: [email protected]

REMSAPhone: 775-858-5700 x140Email: [email protected]: www.REMSA-CF.com

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Stoutian SSM