1. Patient Flow Optimization Decision Support Tool
Estimated Annual Charges ($000)
Based on Average Charge per APRDRG ( )

2. Agenda Introduction The Case for Simulation in Healthcare-Alex Kolker
Leveraging Predictive Analytics through Simulation in Healthcare
What Is Simulation Modeling and Why It Impacts Healthcare Delivery?
Carilion Clinic Case Study-Jim Montgomery
Patient Flow Optimization
Lessons Learned
Ongoing/Future Use of the Simulation Solution
Thank You/Q&A

4. Simulation Is…
Applied to a wide range of problems in healthcare management and operations
Used to study complex processes using a computer model that represents system behavior
A methodology of choice for situations that are too complicated to represent using analytic formulas (e.g., queuing analytic theory)

5. Simulation Answers Questions Such As…
How many nurses do you need at a time on any inpatient unit?
Should you add another CT scanner to your radiology service?
How many beds do you need to staff your ED at different times of the day or days of the week?
What additional resources (nurses, beds, etc.), if any, do you need to decrease your rate of LWBS in the ED?

6. Simulation Answers Questions Such As…
How many phlebotomists do you need to guarantee acceptable waiting times?
What is the highest occupancy/utilization rate you can tolerate?
How many beds or staff should you budget for?
There is no accurate way to answer the types of questions above without simulation modeling.
Everything else is just a guess!

7. What is Simulation and How Does a DES Model Work?
A discrete event simulation (DES) model of a system/process is a computer model that mimics the dynamic behavior of the system/process as it evolves over time in order to visualize and quantitatively analyze its performance.
The validated and verified model is then used to study the response of the system/process to input variables in order to identify improvements based on some improvement criteria.

8. What is Simulation and How Does a DES Model Work?
DES models track patients (documents, work- pieces) moving through the distinct steps of the system (events) at distinct points of time.
They are called Discrete Events.
The detailed track is recorded for all processing times and waiting times. Then the system’s statistics for entities and activities are gathered.

9. How can we simulate the patient arrivals and service system response?
How Can It Help Us?
How can we simulate the patient arrivals and service system response?
System
Arrivals Queue (Waiting Line) Service Exit

10. Inter-arrival time (min.)
Example
Suppose we measure the actual time between patient arrivals and the service time
Inter-arrival time (min.)
Service time (min.)
2.6
1.4
2.2
8.8
9.1
2.4
1.8

11. Event that happened in the system
Example continued
Let’s start at the time, t=0, with no patients in the system.
We will be tracking any change (event) that happened in the system.
Event #
Time
Event that happened in the system 1
2.6
1st customer arrives. Service starts that should end at time = 4 ( ) 2 4
Service ends. Server waits for patient. 3
4.8
( )
2nd patient arrives. Service starts that should end at time = ( ). Server idles 0.8 minutes.
6.2
( )
3rd patient arrives. Joins the queue waiting for service. 5
8.6
( )
4th patient arrives. Joins the queue waiting for service. 6
13.6
2nd patient’s (from event 3) service ends. 3rd patient at the head of the queue (1st in, 1st out) starts service that should end at time 22.7 ( ). 7
22.7
4th patient starts service…and so on.

12. Example System Output Statistics
In the end the statistics gathered are:
the average patient and server waiting time
its standard deviation
the number of patients in the queue
the confidence intervals
any other custom process statistics/information

13. System Capabilities
DES models are capable of tracking thousands of individual entities arriving randomly or in a complex pattern.
Each entity has its own unique attributes, enabling one to simulate the most complex systems with interacting events and component interdependencies.

14. Flaw of Averages Capacity and staffing decision/planning based only on
averages, without
taking into account
particular arrival and
service time
distributions, usually
result in significant
miscalculations.

15. Typical DES Applications Include:
Staff and production scheduling
Capacity planning
Cycle time and cost reduction
Throughput capability
Resources and activities utilization
Bottleneck finding and analysis
DES is the most powerful tool with which to perform quantitative “what-if” analysis and test different scenarios of process behavior as system parameters change over time.
Simulation allows one to experiment on the computer, and to test different options before going to the hospital floor for actual implementation.

16. The Power of Simulation
Used for problems difficult to solve analytically
Can experiment with system behavior without experimenting with the actual system
Compresses time
Valuable tool for training decision makers

17. Carilion Hospital Patient Flow Optimization
Case Study: Computer Simulation Model
Carilion Hospital Patient Flow Optimization
Jim Montgomery MHA, FACHE, CSSBB Senior Performance Improvement Consultant Informatics, Analytics and Improvement Division Carilion Clinic
Roanoke, Virginia

18. Carilion Agenda Value to Carilion Why Simulate? Demo
Variation
Demo
Scenarios of Interest
Key Output/Sample Results
Lessons Learned
Contact Info/Q&A

19. Hospital Patient Flow Optimization Value to Carilion Clinic
Cost
An environment in which to experiment without the cost of actually opening or expanding a unit
Time
The tool used during a throughput decision-making committee meeting for instant forecasts
Capacity
Knowing that the decision to open PCU bed space would reduce the percent of beds occupied by X amount and PCU unit census by Y amount

20. Making Throughput Decisions in a Complex Hospital System
The Challenges - Variation
Volume of arrivals
Timing of arrivals (day of week, time of day)
Service line specialty of admitting physician
Acuity level of patient at time of admission (requires Med/Surg unit, PCU, or ICU)
Bed capacity among nursing units
Transfer Center’s distribution of patients
Patients’ length of stay on each unit
Patients’ movement between nursing units (moves up in acuity, moves down in acuity, or moves to unit of similar acuity)

21. Demonstration

22. Key Model Outputs
Scenarios of Interest

23. A More Specific Research Question
If we:
1. Close 10 swing space PCU beds
2. Open a unit with 8 PCU beds
3. Open an additional unit with 9 PCU beds
Set aside a PCU ward for observation patients
What happens to:
1. Census on the units affected
2. Percent of beds filled on the units affected
3. Overall hospital census
4. Percent of Hospital beds filled

24. Scenarios of Interest (Playing What if)
Scenario 1: Old Base Case: Used to build model, based on data. Used for validation.
Scenario 2: New Base Case: Increases admissions/unit capacity to present rates
Scenario 3: Open 7M PCU2 and close Swing Units on 7S and 8S
Scenario 4: Expand capacity. Open 7W
Scenario 5: Establish 7E PCU exclusively as an observation unit

25. Key Output and Sample Results
Key Model Outputs
Key Output and
Sample Results

26. How are PCUs Affected? Percent Utilization

27. How are PCUs Affected? Percent Utilization

28. How are PCUs Affected? Average Census and Extra Capacity
Scenario 2 – The New Status Quo
Scenario 3 – Open 7MPCU2, Close 7 & 8 Swing Beds
Scenario 4 – Expand Capacity: Open 7W
Avg Census
Avg Extra Capacity
Key

29. How are PCUs Affected? Average Census and Extra Capacity
Scenario 2 – The New Status Quo
Scenario 3 – Open 7MPCU2, Close 7 & 8 Swing Beds
Scenario 4 – Expand Capacity: Open 7W
Avg Census
Avg Extra Capacity
Key

30. How are PCUs Affected? Average Census and Extra Capacity
Scenario 2 – The New Status Quo
Scenario 3 – Open 7MPCU2, Close 7 & 8 Swing Beds
Scenario 4 – Expand Capacity: Open 7W
Avg Census
Avg Extra Capacity
Key

31. Sample Results But What Else Changes?
Other PCU Beds – Change in Percent Occupied
AVERAGE PERCENT OF
BEDS OCCUPIED

32. Sample Results Impact on Med/Surg Wards? Change in Percent Occupied
AVERAGE PERCENT OF
BEDS OCCUPIED

33. Sample Results Estimated Annual Charges ($000)
Based on Average Charge per APRDRG ( )

34. Sample Results How Successful Is Our Attempt to Place Patients?
Physicians’ 1st Choice for Unit Assignment (Percent)

35. Lessons Learned
Shorten the process by partnering with ProModel - otherwise we would still be writing code
2. Build the model for rapid flexibility and the ability to change inputs through scenario building
3. Budget additional time for verification and validation
4. Make sure your data set includes all the patients occupying beds in your research locations (i.e., observation patients)
5. Plan for future ongoing use of the tool

36. Thank You Any Questions?
Jim Montgomery, MHA, FACHE, CSSBB
Alex Kolker, Ph.D.
To buy Alex’s book, go to
Search for Healthcare Management Engineering. What Does This Fancy Term Really Mean?
Jim Cain, MSN
ProModel Director Healthcare Solutions