©2012 MFMER | slide-1

Thomas Roh

Center for Science of Healthcare Delivery, Mayo Clinic

Tarun Mohan Lal

Systems and Procedures, Mayo Clinic

Discrete-Event Simulation for Process

Redesign/Re-engineering in Healthcare

Outline

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� Simulation 101� What’s special about simulation?

� Why use it?

� Applications in Healthcare

� Workshop Overview & Takeaways

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“Medicine is moving toward pay for results.

They [healthcare professionals] need better tools to understand what their potential performance could be in order

to make better decisions.”

Dr. Douglas L. Wood, Mayo Clinic

Application of Discrete-Event Simulation in Health Care: a Review, Michael Thorwarth et. al. (2009)

Discrete Event Simulation Definition

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� Decision making tool

� Mathematical representation of a system that allows easy manipulation to study the effects

� Recreates history that closely mirrors

past events� Assumes past events are a

good predictor of future events

� Accounts for variability

� Graphical model� Visual representations of how a

system flows

Significance of Simulation

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� Number of exam rooms needed to reduce the patient waiting time

� Approach 1: Best Guess � Arrival rate : 5 patients/hour� Service Time: 12.1 minutes/patient (5 patients/hour).⇒no delays (patients arrive at approximately the same rate we can serve)

� Number of exam rooms needed is only 1

Patient Arrival Time Service Time (min)

1 1 172 15 253 25 134 32 25 40 296 56 27 81 18 103 09 113 210 119 30

Avg. 5 patients/hr 12.1 minutes/patient

Significance of Simulation

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� Includes variability� Average Waiting Time – 10.1 minutes

� Thus in order to reduce the wait time you need a minimum of 2 exam rooms

Patient Arrival Time Service Time Service Start Time Departure Time Time in System Waiting Time1 1 17 1 18 17 02 15 25 18 43 27 23 25 13 43 56 31 174 32 2 56 58 26 245 40 29 58 87 47 186 56 2 87 89 33 317 81 1 89 90 9 88 103 0 103 103 0 09 113 2 113 115 2 010 119 30 119 149 30 0

Avg. Waiting 10.1

Cost of having frustrated patients or excess waiting time in emergency department?

What if… � What if you want to change the layout of the facility but are unsure of its impact on patient flow and the facility costs $5 M?

� What would you do if you are trying to decide if an additional service line with existing staff?

� What if you are trying to convince someone that adding 10 beds in ED is sufficient instead of 20 beds?

� What if you need to compare options?

� What if your company is implementing a new IT tool to improve the workflow and you need to revise your staffing plans?

� What if patients do not follow FIFO rule?

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When to Use/Not Use Simulation?

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Use Not Use

� Impossible or expensive to test in real life

� Justify using data

� “What if ” analysis

� Pinpoint problem areas

�Realize constraining resources

� Sufficient data is not available

� Simpler methods will achieve the same results

�Variability in process is very small

Simulation Model Building Process

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Reaching the Point of no return w/

Clients…

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Overview of Applications in Healthcare

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Healthcare Delivery

Applications

Outpatient

Clinics

Special

Units

Supply Chain

Surgical

Procedures

Emergency

Departments

Hospitals

McDonnell , G. (July, 2007).Workshop on Multiscale Modeling using AnyLogic 6 with Health Examples at International System Dynamics Society Conference. Boston, MA

Application I in Healthcare

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� Physical Capacity and Resource Planning Hospital Operations� Background and Problem Statement

� St. Mary’s Hospital, Rochester MN Expansion of PACU

� Pre-Op and Post-Op occurs in the same rooms

� Redesign of Rooms

� Current - “open-air” rooms with multiple beds

� Future - one patient/room, nurse can possibly attend to two rooms

� How many beds will we need to construct?

� Why use simulation?� Expensive to build beds

� Inter-related processes

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OR

OR

Pre/Post Expansion Rooms

Main PACU

PED

Pre-Op Only

Results

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� Original Design� 85 Beds

� 75 newly constructed� 15 Peds� 70 Universal Beds

� Design Adjustment� 74 Beds

� 60 newly constructed & 14 unchanged� 44 Outpatient Beds� 8 PED Beds� 8 Swing Beds

� 14 unchanged� Inpatient Beds

Application II in Healthcare

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� Determining the effects of new methods and technology� Background and Problem Statement

� Local primary care network call center

� Nurses Triage

o Does the patient need to see a physician?

o Treatment for minor problems

o Prescriptions

� FTE needed to maintain abandonment rate 5% or lower

� Why use simulation?� Several call distribution models want to be tested

� Variability in call volumes and distribution of service times

Call Distribution Models

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Centralized

All Calls

One Location

Site-Specific

All Calls

Site 2Site 3Site 4Site 5Site 6

Site 1

Virtually Centralized

Decentralized

All Calls

Site 1

CT 1

CT 2

Site 2

CT 1

CT 2

Site 3

CT 1

CT 2

Site 4

CT 1

CT 2

Site 5

CT 1

CT 2

Site 6

CT 1

CT 2

Results

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Model Type Monday Tuesday Wednesday Thursday Friday Weekly FTECentralized 35.5 32.5 32 30.5 30 32.9Site Specific 48 42 42 42 42 43.9

Virtually Centralized 44 39.5 39.5 39.5 39.5 41.0Decentralized 52.5 50 50 50 50 51.3

Additional Applications

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� Supply Chain Logistics� Blood bank

� Determine inventory needed on hand� Pharmacy

� Drug management and distribution throughout the health system

� Staff Scheduling and Work Allocation � Emergency Department

� Test redesign of function or process before implementation� Triageo Test new methods of handling patient intake

� ED Boardingo Find bottlenecks to admitting patients

� Patient Throughput o Build schedule to meet demand

Workshop Overview

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� Statistics for simulation

� Problem Development� Define expectations

� Model Building

� Result Analysis

� Case Studies

� Hands on experience developing your own model

Takeaways

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� Identify where you could apply simulation in your organization?

� Use of simulation modeling

� Build a simple model

� How to effectively use/implement/sell the results

� Learn from colleagues from other organizations

SHS Conference 2014

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� Discrete-Event Simulation for Process Redesign/Re-engineering in Healthcare Workshop� Friday, February 21st, 8 am – 12 pm

“ Trials have shown that modeling and simulation could reduce medical error costs by up to $17 billion across the country”

Congressman J.

Randy Forbes

Contact Information

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Thomas Roh

Senior Health Services Analyst

roh.thomas@mayo.edu

(507)293-0266

Tarun Mohan Lal

Health Systems Engineering Analyst

mohanlal.tarun@mayo.edu

(507)266-9842