1 / 33

Stoutian SSM

Frank Gresh Chief Information Officer - EMSA. Stoutian SSM. Jonathan D. Washko, BS-EMSA, NREMT-P Director of Strategic Development – REMSA President – Washko & Associates, LLC. Stoutian SSM. Discussion Topics Stoutian philosophy and background What is a Temporal Demand Analysis

colorado-morse
Télécharger la présentation

Stoutian SSM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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

  3. 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

  4. 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

  5. • 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

  6. Temporal Demand Analysis & Peak Load Staffing Models

  7. 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).

  8. Analyzing Demand Data • 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!

  9. 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.)

  10. 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

  11. 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 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

  12. 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

  13. 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

  14. 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…

  15. 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…

  16. A Temporal Demand Analysis for Monday

  17. 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.

  18. 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.

  19. A Temporal Demand Analysis for Monday Hours of Day in Hour Ending Format e.g. 21 = 20:00 through 21:00 • 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.

  20. 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.

  21. A Temporal Demand Analysis for Monday Represents that on February 17, 2003 there were 13 Responses between 11:00 and 12:00 • 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.

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

  23. 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 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

  24. 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 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

  25. Stoutian SSM - Research

  26. Stoutian SSM - Research

  27. 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

  28. Stoutian SSM - Research • The Results:

  29. Stoutian SSM - Research • The Results: This lends one to interpret that this doesn’t work….HOWEVER

  30. Stoutian SSM - Research • 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!!!!!!)

  31. 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

  32. 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?

  33. Questions & Contact Information EMSA Phone: 405-297-7053 Email: greshf@emsa.net REMSA Phone: 775-858-5700 x140 Email: jwashko@remsa-cf.com Web: www.REMSA-CF.com

More Related