Stoutian SSM Jonathan D. Washko, BS-EMSA, NREMT-P Director of Strategic Development – REMSA President – Washko & Associates, LLC Frank Gresh Chief Information Officer - EMSA

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

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

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 Quality Unit Hour Human Resources Public Education Control Center Training & Edu Operations Finance Supply / Logistics Data Analytics QI / CQI / PI Fleet Maint. Safety & Risk IT / Technology PR/Marketing The Quality Unit Hour Concept

Temporal Demand Analysis & Peak Load Staffing Models

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).

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 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! 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.) 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 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

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

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 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… MondayHE1HE2HE3HE4… … … … … ……………And so on

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…

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. 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 Order In this case the date is Monday February 03, 2003 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. Hours of Day in Hour Ending Format e.g. 21 = 20:00 through 21:00 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. Total of All Hours for Each Week (Totaled Across) In this case, there were 196 Responses on Feb. 10, 2003 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. Represents that on February 17, 2003 there were 13 Responses between 11:00 and 12:00 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 90 th Percentile of demand. A Temporal Demand Analysis for Monday

The Average High is a Stoutian Measurement that represents approximately the 75 th 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)) / 5 The resultant is then multiplied by the TMT Multiplier for TMT Adjustments A Temporal Demand Analysis for Monday

The Average Peak is a Stoutian Measurement that represents approximately the 90 th 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) ) / 2 The resultant is then multiplied by the TMT Multiplier for TMT Adjustments A Temporal Demand Analysis for Monday

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 90 th Percentile Ranking Stoutian SSM - Research

The Results: Stoutian SSM - Research

The Results: This lends one to interpret that this doesnt work….HOWEVER Stoutian SSM - Research

The Results: Actually its 96% accurate!!!!!!) Understand that Demand Analysis was not designed to predict Call Volume…its 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!!!!!!) 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 Stoutian SSM - Research

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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