Mayo Clinic Conference on Systems Engineering &

Effect of Interdependency of ED, ICU, OR and Nursing Units on Hospital-Wide System

Patient Flow

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Alexander Kolker, PhDOperations Analysis Project Manager

Outcomes DepartmentChildren’s Hospital and Health System

Milwaukee, Wisconsin

Mayo Clinic Conference on Systems Engineering & Operations Research in Health Care

Mayo School of Continuous Professional Development

August 19, 2010

• To demonstrate the power of the modern management engineering and its foundation-the operations research-for quantitative analysis of complex healthcare systems.

• To quantitatively illustrate the critical effect of subsystems’ interaction on the entire system outcome.

Objectives

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• To summarize fundamental Management Engineering principles and their use for managerial decision-making without a full-scale detailed simulation analysis.

• Main concept and some definitions.

• Typical hospital system as a set of interdependent subsystems:

• Subsystem 1: Emergency Department (ED).

• Subsystem 2: Intensive Care Unit (ICU).

Outline

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• Subsystem 3: Operating Rooms (OR)- Surgical Department.

• Subsystem 4: Medical/Surgical Nursing Units (Floor_NU).

• Interdependency of subsystems.

• Main take-away.

• Summary of fundamental management engineering principles.

Kolker, A, Queuing Theory and Discreet Events Simulation for Healthcare: from Basic Processes to Complex Systems with Interdependencies. Chapter 20. In: Handbook of Research on Discrete Event Simulation: Technologies and Applications, 2009, pp. 443 - 483. IGI Global Publishing, Hershey, PA.

Kolker, A, Process Modeling of Emergency Department Patient Flow: Effect of Patient Length of Stay on ED Diversion. Journal of Medical Systems, 2008, v. 32, N 5, pp. 389 -401.

This presentation is adapted from the following System Engineering Publications

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Kolker, A, Process Modeling of ICU Patient Flow: Effect of Daily Load Leveling of Elective Surgeries on ICU Diversion. Journal of Medical Systems, 2009, v. 33, N 1, pp. 27 - 40.

Kolker, A, Norell, B., O’Connor, M., Hoffman, G., Oldham, K., The Use of Predictive Simulation Modeling for Surgical Capacity Expansion Analysis. Presented at the 2010 SHS/ASQ Joint Conference, Atlanta, GA, February 26, 2010 (poster session).

Kolker, A, Efficient Managerial Decision Making in Healthcare Settings: Examples and Fundamental Principles. Chapter 1. In: Management Engineering for Effective Healthcare Delivery: Principles and Applications. Ed. A. Kolker, P. Story. IGI-Global Publishing, 2011.

• Modern medicine has achieved great progress in treating individual patients. This progress is based mainly on hard science: molecular genetics, biophysics, biochemistry, design and development of medical devices, imaging, drugs.

• However relatively little resources have been devoted to the proper functioning of overall healthcare delivery as an integrated system,

Main Concept

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A real impact on efficiency and sustainability of the healthcare system can be achieved only by using healthcare delivery engineering which is based on hard science such as: probability theory, forecasting, calculus, stochastic optimization, computer simulation, etc.

functioning of overall healthcare delivery as an integrated system,in which access to efficient care should be delivered to many thousands of patients in an economically sustainable way. (Joint report of National Academy of Engineering and Institute of Medicine, 2005).

Traditional (Intuitive) Management is based on• Past experience.• Intuition or educated guess. • Static pictures or simple linear projections.

What is Management?

Management is controlling and leveraging available resources (material, financial and human) aimed at achieving the performance objectives.

Some Definitions

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• Static pictures or simple linear projections.

Resource inputSys

tem

outp

ut

Linear projection assumes that the output is directly proportional to the input, i.e. the more resources (material and human) thrown in, the more output produced (and vice versa).

• Management Engineering (ME) is the discipline of building and using validated mathematical models of real systems to study their behavior aimed at making justified business decisions.

What is Management Engineering?

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• This field is also known as operations research.

Thus, Management Engineering is the application of mathematical methods to system analysis and decision-making.

• A goal that is clearly stated and measurable, so the decision-maker (manager) always knows if the goal is closer or farther away.

• Identification of available resources that can be leveraged (allocated) in different ways.

• Development of mathematical models or numeric computer algorithmsto quantitatively test different decisions for the use of resources and

Scientific Management is Based On

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The Underlying Premise of ME is

• Decisions should be made that best lead to reaching the goal.

• Valid mathematical models lead to better justified decisions than an educated guess, past experience, and linear extrapolations (traditional decision-making).

to quantitatively test different decisions for the use of resources and consequences of these decisions (especially unintended consequences) before finalizing the decisions.

Main Steps for System Engineering Analysis

S t e p 1

• Large systems are deconstructed into smaller subsystems using natural breaks in the system.

• Subsystems are modeled, analyzed, and studied separately.

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• Subsystems are then reconnected in a way that recaptures the interdependency between them.

• The entire system is re-analyzed using the output of one subsystem as the input for another subsystem.

S t e p 2

High-Level Layout of a Typical Hospital System

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KeyED – Emergency Room Floor NU – Med/Surg UnitsICU – Intensive Care Unit OR – Operating RoomsWR – Waiting Room

• Simulation and Analysis of the Main Subsystems:

§ Subsystem 1: Emergency Department (ED).

§ Subsystem 2: Intensive Care Unit (ICU).

Step 1

• Deconstruction of the entire hospital system into Main Subsystems.

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§ Subsystem 2: Intensive Care Unit (ICU).

§ Subsystem 3: Operating Rooms (OR).

§ Subsystem 4: Floor Nursing Units (NU).

Subsystem 1: Typical Emergency Department (ED)

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ED structure and in-patient unitsThe high-level layout of the entire hospital system:

Typical ED Challenges

ED Performance Issues

• ED ambulance diversion is unacceptably high (about 23% of time sample ED is closed to new patients).

• Among many factors that affect ED diversion, patient Length of Stay in ED (LOS) is one of the most significant factors.

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High Level ED Analysis Goal

• Quantitatively predict the relationship between patient LOS and ED diversion.

• Identify the upper LOS limit (ULOS) that will result in significant reduction or elimination ED diversion.

Stay in ED (LOS) is one of the most significant factors.

SimulationDigital clock

Typical ED Simulation Model Layout

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Arrival patternwk, DOW, time

Mode of transp

Disposition

ED pre-filled at the simulation start

Mode of Transportation

Modeling Approach

• ED diversion (closure) is declared when ED patient census reaches ED bed capacity.

• ED stays in diversion until some beds become available after patients are moved out of ED (discharged home, expired, or admitted as in-patients).

• Upper LOS limits (simulation parameters) are imposed on the

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• Upper LOS limits (simulation parameters) are imposed on the baseline original LOS distributions: A LOS higher than the limiting value is not allowed in the simulation run.

Take Away

Baseline LOS distributions should be recalculated as functions of the upper LOS limits.

500480

460440

420

400380

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3-Parameter Gamma Distribution of LOS_ home, Hrs

500

480

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Re-calculated bounded distribution of LOS_ home, Hrs

dTTf

TfLOSTf

LOS

original

originalnew

∫∫∫∫====

0

)(

)() ,(

Original unbounded distribution New re-calculated distribution

origTf )(

Given original distribution density and the limiting value of the random variable T, what is the conditional distribution of the restricted random variable T?

Modeling Approach – continued

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121086420

360340

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180160

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120100

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4020

0

LOS, Hrs

Frequency

Imposed LOS limit 6 hrs

121086420

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LOS, Hrs

Frequency

∫∫∫∫0

LOST if ,0)( >>>>====newTf

T, Hrs LOT, Hrs

LOS limit

Practically NO diversion

~ 0.5 %6 hrsCurrently 24% with LOS more than 6 hrs;

5 hrsCurrently 17% with LOS more than 5 hrs;

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Actual ED diversion was 21.5%

23.7%24 hrs24 hrsCurrent, 07 (Baseline)

NotePredicted ED diversion, %

LOS for admitted NOT more than

LOS for discharged home NOT more than

Scenario/option

Simulation Summary and Model Validation

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Low single digits diversion

~4%24 hrs5 hrs3

Low single digits diversion

~ 2%6 hrs6 hrs2

than 6 hrs;

Take-away:Take Away

• ED diversion could be negligible (~0.5%) if patients discharged home stay not more than five hours and admitted patients stay not more than six hours.

• Relaxing of these LOS limits results in a low digits percent diversion that still could be acceptable.

24.0

22.5

21.0

19.5

56810

U LO S _h o m e , h r s

S imu la te d D iv % a s a func tio n o f uppe r L O S l im its , h r s

What other combinations of upper LOS limits are possible to get a low single digit percent ED diversion?

Simulation Summary – continued

Perform full factorial DOE with two factors (ULOS_home and ULOS_adm) at six levels each using simulated percent diversion as a response function.

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ULOS_a dm, h r s

Mean predicted Div %

241 210865

19.5

18.0

16.5

15.0

13.5

12.0

10.5

9.0

7.5

6.0

4.5

3.0

1.5

0.0

101224Low single digits

% diversion

Conclusions for Subsystem 1: Emergency Department

• ED diversion can be negligible (less than 1%) if hospital-admitted patients stay in ED not more than six hours.

• Currently 24% of hospital-admitted patients in study hospital stay longer than this limit, up to 20 hours.

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hospital stay longer than this limit, up to 20 hours.

• This long LOS for a large percentage of patients results in ED closure/diversion.

Subsystem 2: Typical Intensive Care Unit (ICU)Patients move between the units:• If no beds in CIC, move to SIC• If no beds in MIC, move to CIC, else SIC, else NIC• If no beds in SIC, move CIC• If no beds in NIC, move to CIC, else SIC

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ICU Performance Issues

• Elective surgeries are usually scheduled for Operating Room block times without taking into account the competing demand from emergency and add-on surgeries for ICU resources.

• This practice results in:§ Increased ICU diversion due to ‘no ICU beds’.

§ Increased rate of medical and quality issues due to staff overload and capacity

Typical ICU Challenges

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§ Increased rate of medical and quality issues due to staff overload and capacity constraints.

§ Decreased patient throughput and hospital revenue.

High Level ICU Analysis Goal

• Establish a relationship between daily elective surgeries schedule, emergency and add-on cases and ICU diversion.

• Given the number of the daily scheduled elective surgeries and the number of unscheduled emergency and add-on admissions, predict ICU diversion due to lack of available beds.

ICU Census: Elective surgeries current pattern - No daily cap

Closed due to No ICU beds: 10.5 % of time

4748495051

Red zone:Critical census limit exceeded

Baseline – Existing Number of Elective Cases

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35363738394041424344454647

0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024

Hrs/ weeks

cns

wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17

• There is a significant variation in the number of scheduled elective cases between the same days of the different weeks (Monday to Monday, Tuesday to Tuesday, and so on).

• Smoothing the number of elective cases over time (daily load

Conclusions for Subsystem 2: Intensive Care Unit

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• Smoothing the number of elective cases over time (daily load leveling) is a very significant factor which strongly affects ICU closure time due to ‘no ICU beds.’

• Using Simulation it was demonstrated that daily load leveling of elective cases to not more than 4 cases per day will result in avery significant reduction of closure time due to ‘no ICU beds’ (from ~10.5% down to ~1%).

Typical Operational Challenges

• Is the number of general and specialized operating rooms and pre/post operative beds adequate to meet the projected patient flow and volume increases?

• If it is not, how many operating rooms and pre/post operative

Subsystem 3: Operating Rooms (OR)

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• If it is not, how many operating rooms and pre/post operative beds would be needed?

• Is the Operating Room utilization adequate?

The following OR Operational performance criteria were used

1. Patient delay to be admitted to a preoperative surgical bed should not exceed 15 minutes.

2. Delay to enter operating room from a preoperative surgical bed should not exceed:

General OR – 2 hours Urgent OR – 3 hoursCardiovascular OR – 5 hours Neurosurgery OR – 3 hoursOrthopedic OR – 2 hours Cardiac Cath Lab – 2 hours

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Orthopedic OR – 2 hours Cardiac Cath Lab – 2 hours

3. Percent of patients waiting longer than the acceptable delay to enter operating room from a preoperative surgical bed should not exceed 5%.

4. Delay to enter PACU beds from an operating room should not exceed 5 minutes.

5. Average annual utilization of operating rooms should be in the range of 60% to 90%.

The following simulation models were developed and analyzed

Model 1: Baseline operations - all surgical services function as currently specified between two floors. Construct two general operating rooms onto upper level floor to serve otolaryngology, gastroenterology and pulmonary patient volume from lower level floor.

Model 2: Move gastroenterology and pulmonary patient volume from upper level to a separate service area.

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Total annual patient volume included in the simulation models is in the range from 15,000 to 17,000.

Decision variables were: The number of pre-operative beds and PACU beds, number of Operating Rooms and special procedure rooms and their allocation for surgical services.

upper level to a separate service area.

Model 3: Separate service area for gastroenterology and pulmonary patient volume includes 2 to 3 special procedure rooms, 1 to 2 general OR, and 8 to 11 pre/post beds and PACU beds.

Simulation Model Layout (Scenarios 1 – 3)

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Operating Rooms: OpR-general; U_OR-urgent; CV_OR-cardiovascular; Cath_OR-catheterization; SPR-special procedure.

• Model 3 is selected as the best. Twelve Operating Rooms and four Special Procedure Rooms/OR will be adequate to handle patient volume up to the year 2013.

• Cath Lab capacity could become an issue by 2013 with

Conclusions for Subsystem 3: Operating Rooms (OR)

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• Cath Lab capacity could become an issue by 2013 with more than 10% of patients waiting longer than acceptable limit 2 hours.

• All other performance criteria will be met.

Patient Length of Stay

Subsystem 4: Medical/Surgical Nursing Units (NU)

Total number of specialized nursing units: 24 Total number of licensed beds: 380

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Census (i) (current period) = census (i-1) (previous period) + [# admissions (i) – # discharges (i) ]; i = 1, 2, 3, …….

This is a dynamic balance of supply (beds) and demand (admissions).

Patient Length of Stay (LOS) is in the range from 2 days to 10 days; The most likely LOS is 5 days.

Census (i) (current period) = census (i-1) (previous period) + [# admissions (i) – # discharges (i) ]; i = 1, 2, 3, …….

Simulated Census. Capacity 380 beds

370

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capacity limit

Mon Tue Wed Thu Fri Sat Sun

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0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152 156 160 164 168

days/ hours

cen

sus

Take Away: Percent of time Nursing Units are full (% diversion) is about 16%.

• Subsystems are reconnected in a way that recaptures the interdependency between them.

• The entire system is re-analyzed using the output of

Step 2

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• The entire system is re-analyzed using the output of one subsystem as the input for another subsystem.

• All subsystems are reconnected to each other.• The output of one subsystem is the input for another subsystem.

Step 2 – continued

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Hospital System Simulation Summary

Performance MetricsCurrent State

Baseline

Too aggressive ED improvement: patients admitted within 6 hours

Downstream Units: Better or worse than current state?

Less aggressive ED improvement: patients admitted within 10 hours

Downstream Units: Better or

words than current state?

95% CI of the number of patients waiting to get to ED (ED in)

25 – 27 8 – 10 Better 17 – 19 Better

95% CI of the number of patients waiting hospital admissions (ED out)

57 – 62 64 – 69 Worse 57 – 62 Neutral

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Number of patients left not seen (LNS) after waiting more than 2 hours

23 – 32 0 Better 0 – 3 Better

95% CI for % ED diversion 22% – 23% 0.4% – 0.5% Better 6.8% – 7.3% Better

95% CI for % ICU diversion 28% – 32% 30% – 34% Worse 28% – 32% Neutral

95% CI for % OR diversion 12% – 13% 13% – 15% Worse 12% – 13% Neutral

95% CI for % floor NU diversion 11% – 12% 11% – 12% Neutral 11% – 12% Neutral

Take Away

• Too aggressive ED improvement results in worseningthree out of seven hospital system performance metrics.

• Less aggressive ED improvement is more aligned with

Take-Away from Hospital System Simulation Summary

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• Less aggressive ED improvement is more aligned with the ability of downstream subsystems to handle increased patient volume.

• This illustrates important Management System Engineering Principles:

• Improvement in the separate subsystems (local optimization or local improvement) should not be confused with the improvement of the entire system.

• A system of local improvements is not the best system; it could be a very inefficient system.

Important System Engineering Principles

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it could be a very inefficient system.

• Analysis of an entire complex system is usually incomplete and can be misleading without taking into account subsystems’ interdependency.

Main Take-Away

Management Engineering helps to address the following typical pressing hospital issues:

• How many beds are needed for each unit.

• How many procedure rooms are needed for each service.

• How many nurses/physicians should each unit schedule for the particular day and night.

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day and night.

• How to reduce patient wait time and increase access to care.

• How to develop an efficient outpatient clinic schedule.

And so on, and so on…

And the Ultimate Goal:How to manage hospital operations to increase profitability (reduce costs, increase revenue) while keeping high quality, safety and outcomes standards for patients.

Summary of Some Fundamental Management Engineering Principles

• Systems behave differently than the sum of their independent components.

• All other factors being equal, combined resources are more efficient than specialized (dedicated) resources with the same total capacity/workload.

• Scheduling appointments (jobs) in the order of their increased duration

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• Scheduling appointments (jobs) in the order of their increased duration variability (from lower to higher variability) results in a lower overall cycle time and waiting time.

• Size matters. Large units with the same arrival rate (relative to its size) always have a significantly lower waiting time. Large units can also function at a much higher utilization % level than small units with about the same patient waiting time.

• Work load leveling (smoothing) is an effective strategy to reduce waiting time and improve patient flow.

• Because of the variability of patient arrivals and service time, a reserved capacity (sometimes up to 30%) is usually needed to avoid regular operational problems due to unavailable beds.

• Generally, the higher utilization level of the resource (good for the organization) the longer is the waiting time to get this resource

Summary of Some Fundamental Management Engineering Principles – continued

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organization) the longer is the waiting time to get this resource (bad for patient). Utilization level higher than 80% to 85% results in a significant increase in waiting time for random patient arrivals and random service time.

• In a series of dependent activities only a bottleneck defines the throughput of the entire system. A bottleneck is a resource (or activity) whose capacity is less than or equal to demand placed on it.

• An appointment backlog can remain stable even if the average appointment demand is less than appointment capacity.

• The time of peak congestion usually lags the time of the

Summary of Some Fundamental Management Engineering Principles – continued

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• The time of peak congestion usually lags the time of the peak arrival rate because it takes time to serve patients from the previous time periods (service inertia).

• Reduction of process variability is the key to patient flow improvement, increasing throughput and reducing delays.

QuizQ1. Improvement in the separate subsystems of the hospital system (local

improvement) can:1) Make the entire system more efficient2) Make no difference3) Make the entire system less efficient4) Both (2) and (3)

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Q2. Improvement in ED patient throughput and capacity: 1) Is always a first priority2) Can result in worsening in some other hospital operational metrics3) Should be aligned with the ability of downstream subsystems to handle increased patient volume4) Both (2) and (3)

AnswersQ1. Improvement in the separate subsystems of the hospital system (local

improvement) can:1) Make the entire system more efficient2) Make no difference3) Make the entire system less efficient4) Both (2) and (3) – Correct answer

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Q2. Improvement in ED patient throughput and capacity: 1) Is always a first priority2) Can result in worsening in some other hospital operational metrics3) Should be aligned with the ability of downstream subsystems to handle increased patient volume4) Both (2) and (3) – Correct answer

APPENDIX

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APPENDIX

A Simulation Model is the computer model that mimics the behavior of a real complex system as it evolves over the time in order to visualize and quantitatively analyze its performance in terms of:

• Cycle times.• Wait times.• Value added time.• Throughput capacity.• Resources utilization.

What is a Simulation Model?

• Resources utilization.• Activities utilization.• Any other custom collected process information.

• The Simulation Model is a tool to perform ‘what-if’ analysis and play different scenarios of the model behavior as conditions and process parameters change.

• This allows one to build various experiments on the computer model and test the effectiveness of various solutions (changes) beforeimplementing the change.

How Does a Typical Simulation Model Work?

A simulation model tracks the move of entities through the system at distinct points of time (thus, discrete events.) The detailed track is recorded of all processing times and waiting times. In the end, the system’s statistics for entities and activities is gathered.

Example of Manual Simulation (step by step)Let’s consider a very simple system that consists of:

• a single patient arrival line. • a single server.

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A few random numbers sampled from these two distributions are, for example: Inter-arrival time, min Service time, min

2.6 1.42.2 8.81.4 9.12.4 1.8…. ….and so on… and so on….

• a single server.Suppose that patient inter-arrival time is uniformly (equally likely) distributed between 1 min and 3 min. Service time is exponentially distributed with the average 2.5 min. (Of course, any statistical distributions or non-random patterns can be used instead).

We will be tracking any change (or event) that happened in the system. A summary of what is happening in the system looks like this:

Event # Time Event that happened in the system

1 2.6 First customer arrives. Service starts that should end at time = 4.

2 4 Service ends. Server waits for patient.

3 4.8 Second patient arrives. Service starts that should end at time = 13.6. Server idle 0.8 minutes.

4 6.2 Third patient arrives. Joins the queue waiting for service.

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5 8.6 Fourth patient arrives. Joins the queue waiting for service.

6 13.6 Second patient (from event 3) service ends. Third patient at the head of the queue (first in, first out) starts service that should end at time 22.7.

7 22.7 Patient #4 starts service…and so on.

In this particular example, we were tracking events at discrete points in time t = 2.6, 4.0, 4.8, 6.2, 8.6, 13.6, 22.7

DES models are capable of tracking hundreds of individual entities, each with its own unique set of attributes, enabling one to simulate the most complex systems with interacting events and component interdependencies.

Basic Elements of a Simulation Model

• Flow chart of the process: Diagram that depicts logical flow of a process from its inception to its completion.

• Entities: Items to be processed (i.e. patients, documents, customers, etc.).

• Activities: Tasks performed on entities (i.e. medical procedures, document approval, customer checkout, etc.).

Resources:

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• Resources: Agents used to perform activities and move entities (i.e. service personnel, operators, equipment, nurses, physicians).

Connections:

• Entity arrivals: They define process entry points, time and quantities of the entities that enter the system to begin processing.

• Entity routings: They define directions and logical condition flows for entities (i.e. percent routing, conditional routing, routing on demand, etc.).

Typical Data Inputs Required to Feed the Model

• Entities, their quantities and arrival timesPeriodic, random, scheduled, daily pattern, etc.

• Time the entities spend in the activities

This is usually not a fixed time but a statistical distribution. The wider the time distribution, the higher the variability of the system behavior.

• The capacity of each activity

The maximum number of entities that can be processed concurrently in

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The maximum number of entities that can be processed concurrently in the activity.

• The size of input and output queues for the activities (if needed).

• The routing type or the logical conditions for a specific routing.

• Resource Assignments

The number of resources, their availability, and/or resources shift schedule.