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Production Model Science & Theory Applied to a Service Industry
Enables Balancing of Patient Care, Employee
Wellbeing & Financial Stability in a Poor
Economic Environment
Production Model EMS Theory:
 Service Demands ARE Predictable
▪ Temporal (When is the Demand - Time of Day and Day of Week)
▪ Geospatial (Where is the Demand)

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Our “Product / Widget” is a Unit Hour
 Ambulance Available for One Hour
▪ Medical Staff
▪ Vehicles
▪ Supplies / Hardware
▪ Support Systems
▪ Administration
Supply our Unit Hours Using Peak-Load
Staffing to Meet Temporal Demand Curves
Based on a Service Reliability Standard / Goal

Saturday Staffing Vs. Demand
26
24
22
20
18
16
14
12
10
8
6
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2
0
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00
All Calls Staffing june 07 New Bid w/o downtime New Bid w downtime

 Efficiency & Effectiveness Drives Throughput
 Driven by Task Time / Call Segment Timeliness
▪ Call Processing Times
▪ Response Times
▪ On Scene Times
▪ Transport Times
▪ At Destination Times
 The Longer it Takes to Run an EMS Call The More
Resources You Need to Meet a Service Reliability Standard
 The Shorter it Takes to Run an EMS Call the Less Resources
You Need to Meet a Service Reliability Standard

 All Functions Performed Under a “Command &
Control” Structure using “Push Engineering” vs
“Pull Engineering”
 Controllers (Dispatchers) Make Key Process Decisions
Regarding Resource Allocation and Usage and Collect
Key Data for Metrics and Benchmarking
 Information Systems Used to Gauge Performance in
Real Time
 Clinicians Make All Clinical and Pathway Decisions
 Very Different then Fire or PD Model (Location of
Command & Control)

 Data Collected is Used to Improve Efficiency and Effectiveness for ALL Processes and Sub-
Processes in the System and is “Re-assessed”
Every 6 Months in Order to Adapt to Changes in Demand or Improvements in Efficiency
 Supply Chain Adjustments
▪ Temporal
▪ Geospatial

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Strong Similarities in Most Key Areas
Strong Evidence That ER Demand is Predictable and
Follows EMS Demand Curves
Allows us to Hypothesize That Other Patient Service
Demands are Also Predictable Based on ER Demand
Patterns and Admitted Patient Census :
 Lab
 X-Ray / CT
 Consulting Medical Groups
 Food Services
 Housekeeping
Substantial “Push” Based System Design Improvement
Opportunities
No Command & Control / Processes Siloed

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Patient Clinical Pathway Dictates Approach:
 ER Walk In/EMS Admission: Discharged from ED
 ER Walk In/EMS Admission: Admitted
 ED / Direct Patient Transfer: Admitted
 ED Patient Transfer: Discharged
Pathway Processes
 Before Admission (Registration / ER)
▪ Highly Contained & Limited Span of Control
▪ Minimal Silo Effect
 After Admission (Admissions / Floor / Unit)
▪ Poorly Contained & Large Span of Control
▪ Substantial Silo Effect

 Before Admission Processes
 Triage
 Registration
 Waiting Queue
 Room Assignment
 Primary Assessment RN
 Primary Assessment MD / PA
 Testing
 Treatment
 Reassessment (More Treatment / Testing Possible)
 Disposition Decision (Discharge / Admit)
 Discharge Patient

 After Admission Processes
 Room Status / Availability / Cleanliness
 RN Report ED to Floor
 Patient Transport
 RN Assessment
 MD Assessment
 Orders
 Testing
 Nutrition
 Other Ancillary Services (Medical & Customer Service)
 Reassessment (MD / RN)
 Disposition Decision (Stay, Transfer, Discharge)
 Discharge Patient

 Adoptable Best Practices
 Setting Service Reliability Standards
 Temporal Demand Analysis
 Peak Load Staffing
 Centralized Command & Control
 Centralized Data Collection & Analysis
 Real-time System Reactivity
 Bi-annual Adjustments to Demand / Efficiency
 “Push Based” Systems Engineering of Practices
 Utilizing APL vs AVL Systems

 Benefits
 Dramatically Improved Throughput Using Same or
Less Staffing
 Improved Customer Satisfaction
 Efficient and Effective Delivery of Care
 Improved Margins via Cost Reductions,
Capitalizing on Lost Opportunity Revenue &
Revenue Improvement Through Increased Patient
Volumes

 Pitfalls
 Significant Change
 MD / RN Rejections of:
▪ Schedules
▪ Command & Control
▪ Perceived Loss of Control
 Must be Combined With Clinical Standards That
Balance Competing Interests
 Capital Layouts
▪ Software & Hardware Must Be Created / Modified / Adapted
▪ Physical Plant Changes / Updates May be Necessary