Presentation Description: Patient flow is a universal challenge in healthcare. Through a systematic evaluation of our cardiac catheterization lab operations (EMR assessment, metric development, capacity analysis using simulation), we found opportunities to reduce unnecessarily bedded cath lab patients by 8 per day on high-demand telemetry units at a large Midwestern hospital. Objective/Purpose: : Frequently, patients at Iowa Methodist Medical Center’s ED and ICU are waiting for a telemetry bed to become available. Patients who are either waiting for a cath procedure or have just had one, occupy nearly 50% of the available telemetry beds, many of whom do not require inpatient level of care. Inadequate, non-transparent cath lab operations and poor schedule adherence were signaled as reasons for why many patients are unnecessarily bedded. Objectives: Understand EMR usage; Scheduling / Reporting Capabilities Develop Sustainable Operations Metrics (KPI’s) Use Discrete Event Simulation to Model Various Improvement Scenarios Track impact to patient access and usage of inpatient telemetry beds Methods/Approach: : Methods/Approach Used: Understanding EMRs:24+ hours of observations of the practical use of our EMR for scheduling, documenting, and reporting. Participated in vendor meetings and webinars on current and alternative systems. Developing Metrics: pulled EMR data and developed 6 key metrics + a process for the ongoing delivery of charts and graphs for use by cath lab leadership. Discrete Event Simulation and Impact to Operations: Data was pulled and validated from EMRs, and Arena® DES was used to model over 20 scenarios for changes. Outputs were captured, compared, and selected for implementation. Results/Findings: : The following scenarios modeled using discrete event simulation had desired impacts: Limit outpatient elective cases to 3 on Mondays and Fridays Increase same day discharges of PCI patients Improve amount of cath proceduralists arriving at 7:00AM (or 7:30AM/8:00AM) Provide 8 hours of capacity to perform catheterizations for urgent patients and inpatients on the weekend Work to develop a process of sending same day elective outpatients to other underutilized cath lab within health system (<10 minutes away) Items 1-3 reduce inpatient bed usage by 5 per day; all 5 together result in a reduction of 8 occupied telemetry beds. Conclusion/Practical Implications: : Our analysis demonstrated the value of understanding workflow and information flow for making improvements. Information leads to transparency and ongoing reporting of performance is essential. Using simulation to model change allows for making large operational changes in a safe, virtual environment. We saw the downstream effects of our changes on the hospital overall with the potential to greatly impact telemetry bed usage and patient flow at UnityPoint Health – Des Moines.

1. Clogged Arteries: Discrete Event Simulation
Reduces Holds for Telemetry Beds

2. Overview
 Background / Nature of The Problem
 Work Breakdown & Goals
1) Understand EMR Capabilities
2) Develop Operations Metrics
3) Model our Operations (Arena®)
 Volume Forecast Modeling
 Conclusions
 Process Management Planning
4) Increase Same Day Discharges
2

3. Introduction
3
• UnityPoint Health –
• Des Moines
– 3 Hospitals
– 1 of 9 Affiliates
– IA, IL, WI

4. Introduction
4
• UnityPoint Health –
• Des Moines
– 3 Hospitals
– 1 of 9 Affiliates
– IA, IL, WI
DSM
IOW
A

5. Introduction
• Industrial Engineers
• Nurses
• Project Manager
• Lead by Asst. VP Process
Improvement and Engineering,
Valerie Boelman, BSIE, LBC,
CSSGB
5

6. Background: IMMC Cath Lab
 2018 IMMC Cath Lab Renovations
 5th Cath Lab (3rd Cath Lab w Vasc. Capabilities)
 Patient Flow Issues at IMMC
 22 beds on N3/N4 currently used
 Many Unnecessarily Bedded
 83% of Delays Related to Provider Scheduling
 Room Turnover: 55 Minutes
 Utilization of Rooms: 31% to 83%
6

7. Nature of the Problem
 Scheduling practices, policies, and outcomes as to
patient start and end time vary significantly and
are not predictable. This variation is leading to
long patient wait times and inefficient operations. A
lack of adequate nursing staff was also identified
and addressed in recent months.
 Apollo as a scheduling and documentation EMR
was found to be lacking in the ability to easily
represent the intended caseload of patients, as
well as reliably track data that must be collected to
run operations metrics reports.
7

8. Work Breakdown and Goals
8
1
2
3
4

9. (1) Understand EMR and Reporting
• Hybrid of Apollo
and EPIC
– Disparity
between Apollo
and other
Products
– Data Match-up
for Analysis
Difficult
– Operational
Metrics
Unavailable
9
Criteria Cupid Apollo
Documentation Completed In One System
 
Order Entry
 
Scheduling
 Have to Schedule
separately in Apollo and
Epic
Procedure Documentation*
 
Structured Reporting for Cardiology
  Very Difficult
Charging
 Unsure
Analytical Reporting with Dashboard Metrics
(Robust) if Buy Additional
Package
Supports a Variety of Procedural Workflows
 Not as Robust
(Cannot document
Peripheral Vascular in
Apollo)
 Echo
 Ultrasound Vascular
 Cardiac Cath
 Stress Testing
 EP Documentation
 Peripheral Vascular
Interfaces are Available

Patient Engagement
 
Automated Registry Data
 
Daily Schedule
 (Unable to Print)
Integrates with Cardio PACS
 
PAT Workflow
 
Status Board for Lobby
 Not Currently Set Up

10. (1) Understand EMR and Reporting
• Hybrid of Apollo and
EPIC
– Disparity between
Apollo and other
Products
– Data Match-up for
Analysis Difficult
– Operational Metrics
Unavailable
10
784 Vascular
Room doesn t contain
IMMC
2328 Caths 2328 Caths
616 EPs
168 CVS
616 EPs
168 CVS
178 Caths
108 EPs
202 Caths
Matched data includes both
operational timestamps, provider
names, and procedure types from
Apollo, but also Made Date data
for every case through EPIC,
whereas Apollo data alone was
unreliable in matching
appointments to Schedule log
creation dates.
50 CVS

11. (2) Develop Operations Metrics
1) First Case Start Times
1) By Date and By Room
2) Minimum Case Start Time
3) No Urgent, No Emergent
2) Last Case Start Times
1) By Date and By Room
2) Maximum Case Start Time
3) No Urgent, No Emergent
1) One report - INCLUDE URGENT
AND EMERGENT
3) First Turnover Time
1) By Room, Only First Turnover of Day
2) Minimum Case Start Time
3) Second Case must be Same Physician as
First Case
4) To aggregate (average), Only use cases
where TOT <1 Hour
4) Room Utilization –7AM and 5PM
1) Removed Cases with either No In Room time
or no Case end time.
2) If a case started at midnight and went over to
next day, it is counted in the day started.
3) If Case End is before In room, but Case end is
greater than Case Start, use Case Start as In
Room time.
4) If Case End - In Room or Case End - Case
Start > 0.5 (12 hours), make static 1.5 Hours
5) If Room is CVS Recovery, add 15 minutes
between cases, otherwise add 30.
1) If room had 6 cases per day, n=6;
TOT = (n-1)*30 or (n-1)*15
5) Patient Wait Time
1) Case Start Time – EPIC Check-In Time (EPIC
Appointment Report)
6) Vascular Cases Completed by Day
11
Lawrence Prunty, Apollo Analyst, Runs
Apollo OperationsReport and Saves to
Shared Drive by 5th
of Month for
PreviousMonth
Shelby Neel, PI Coordinator, Uses Tool
to Generate Metrics (as defined) and
create Graphs, placing them in PPT
Presentation and Emailing to Cath Lab
Leadership by 15th
of Month
Data is used for various presentations
and monitoring
Continue Support until alternate EMR
eliminates need

12. (3) Model Our Operations
12
• Arena® Discrete
Event Simulation
• EPIC and Apollo
Data
• Assumptions and
Constraints
• Verification and
Validation

13. (3) Model Our Operations
13

14. (3) Model Our Operations
14
• Final Outputs
Reported
– Wait Time
– Number on IP
Floors
– Resource
Utilization

15. (3) Model Our Operations
15
v.20 Current State - Model
v.21 Add Lab 5 Weekdays and Evenings/Weekend
v.22
Limit # OPEL Cases on Mondays and Fridays
M=4, F=4
v.23
Limit # OPEL Cases on Mondays and Fridays
M=3, F=3 (“Fill Tuesday, Wednesday, Thursday First”)
v.24
8 Hours of Available Cath Time for Urgent and IP Caths [Sa=4hrs,
Su=4hrs]
v.25 Room 5 accepts Vascular Cases (Cath=M,W,F; Vasc=T,Th)
v. 26
TAVRs
Tuesday or Thursday every other week
v.27 Shared Inpatients Across UPC and TIC
v.28 Emergent and Urgents only cases in Room 5
v. 29
Increase Same Day Discharges for PCI's (IPEL<24=50%,
Urgent=25%)
v.30
All Cath Doctors
Arrive 7AM (“Start Earlier, End Earlier”)
v.
30.5
All Cath Doctors
Arrive 7AM + TAVRs Tuesday or Thursday every other week
v.31
Add Volumes
2020 "Hi 80"
Volume Predictions (approx. 26% ↑) <no TAVR changes)
v.32
Add Volumes
2020 "Hi 90"
Volume Predictions (approx. 39% ↑) <no TAVR changes)
v.33
Same Day OPELs
go to ILH <no TAVR changes)
v.34
Add Volumes
2020 "Hi 80"
Volume Predictions
No Lab 5 (Current State 2017)
(approx. 39% ↑)
v.35
Add Volumes
2020 "Hi 80"
Volume Predictions
Only Add Lab 5
(approx. 39% ↑)
• Current State
Modeled to check for
Validity
• Several Additional
Scenarios Modeled
• Red == Abandoned
in Future Scenarios
• Final 2 Scenarios ==
Increased Future
Volumes

16. (3) Model Our Operations
• Cath Room Utilization
• IP Bed Utilization
– Compared to SDD
16

17. Volume Forecast Modeling
17
Avishek Choudhury, Process Improvement Intern
PhD Student : System Science and Industrial
Engineering (Binghamton University)
​MS in Industrial and Systems Engineering (Texas Tech
University)
Objective: To predict the number of admissions in Cath lab.
Method: Time Series Forecasting using “Auto Regressive
Integrated Moving Average” method.
Tool Used: R (machine learning software)
Data Collection: EPIC database
Time Period: March 2012 to December 2017
Data Type: Daily Data
Missing Value: Missing values were replaced by zero.
(Assuming no patient inflow)
Forecasting time frame: Monthly with Weekly Patient
distribution.
Results:
1. Number of patients on weekends – 0 to 3%
2. Number of patients on weekdays – Friday (22%),
Thursday (20%), Monday to Tuesday (around 18%
each)
3. In recent years (2014 to 2016) most patients
were admitted during the month of May-June and
September-October
Note: Percentage were calculated based on last 5 years
trend and Monthly forecasted data.
Method Explanation:
Auto Regressive Integrated Moving Average (ARIMA) model
requires Time Series data as input which is then
decomposed into three components (see figure 1 below).
• Trend
• Seasonality
• Randomness

18. Additional Cath Lab Inputs –
• The following two plots are the supporting
analysis for the zip code population
prediction.
• Factors considered were : migration,
mortality & fertility rate

19. Migration & Mortality Ratio; Male &
Female Migration

20. Conclusions
• Our results indicate that the following changes
would be advantageous to our proposed metrics
for the IMMC cath lab:
1. Limit outpatient elective cases to 3 on Mondays and
Fridays
2. Work to increase same day discharges according to
prescribed criteria
3. Improve amount of cath proceduralists arriving at
7:00AM (or 7:30AM/8:00AM)
4. Provide 8 hours of capacity to do catheterizations
for urgent patients and inpatients on the weekend
5. Work to develop a process of sending same day
elective outpatients to ILH
20
5th Lab + Items 1,2,3
== 5 Tele Beds Open per Day

21. Process Management Planning
21
PROCESS MAP MONITORING RESPONSE PLAN
Scheduler
(Michelle)
Physician/Office/ Patient
Pre/Intra/Post Op
Nurses
Measurement
Data Collection
Method
Immediate Control
Process
Improvement
Function / Source
Order Placed
Nurse/office calls
Scheduler to setup
Appointment
Prints to printer if
Cardiac
Catheterization
[ITTicket #
INC0969186]
Schedule Cath; (Call
Nurse/Officeifcall
has not already
occurred)
Retain Cath Orders
printed and match
to number of caths
scheduled that week
by Day of Week*
Number ofSame
Day add-on cases*
Scheduler (Michelle)
Scheduler (Michelle)
ManualChecksheet
ManualChecksheet
Project Team
reviews monthly; if
no reduction in add-
on cases after 3
months, eliminate
printing if desired.
Contact IT if orders
not printing
Document
Number of
Outpatient Elective
Caths, Emergent,
Urgent, and IP
scheduled per Day
Scheduler (Michelle)
or other (check to
see ifthis is already
being done)
ManualChecksheet
Traffic Person
Reviews Daily
Project Team
reviews monthly; if
there areconcerns
about the number of
outpatient elective
caths not meeting
Monday and Friday
limits (3-4 OP) or
being too highly
utilized on Tues-
Thurs, adjust M and
F Targets
Patient Arrives Pre-Op Care
Physician Arrives
Procedure
Completed
EPIC
EPIC & Apollo
Pre-Procedure
Nurse
Physician
Post-op Care
Post-Procedure
Nurse
OP Cath with
PCI that Meets
SDD Criteria?
Patient Goes Home
Patient Admitted to
IP Floor
PatientPhysician
Did case start
before 2PM?
YES
Patient may Go
Home or Be
Admitted to IPFloor
NO
YES
NO
Patient
IntraOp
Nurse
Patient Wait Time,
First Case Start, Last
Case Start
Monthly Metrics
Graphs (Shelby)
EPIC Raw Data
(Shelby), Apollo Raw
Data (Lawrence)
Room Utilization,
First Turnover Time,
Number ofVascular
Cases per Day
OPEL Cath Cases
starting Before and
After 2PM
Number ofOPEL
Caths who met
clinical SDD Criteria
Actual Number of
SDD s who met
clinical criteria and
went home
OPEL Cath Cases
who both met
clinical SDD Criteria
and started Before
and After 2PM
Monthly Metrics
Graphs (Shelby)
EPIC Raw Data
(Shelby), Apollo Raw
Data (Lawrence)
Delays Reasons by
Case*
Apollo Delays
Drop-down by
Pre-procedure
Nurses; Graphs by
Shelby
Apollo Delays
Report (Lawrence)
Patient and family
notified of any
impending delays
Monthly Metrics
Graphs (Shelby)
EPIC Raw Data
(Shelby), Apollo Raw
Data (Lawrence)
Targets to be
completed by
January 1, 2019 by
Process
Improvement;
When targets not
met, corrective or
improvement
activity commences
per management
review.
Targets set by
corporate initiative;
When targets not
met, corrective or
improvement
activity commences
per management
review.
Develop Process to
Flag potential SDD s
on schedule each
day
Data Available but Needs Implemented
Data Available but unsureifImplemented
Data Available and Processes are Already Implemented
Data Not Currently Available nor Process Implemented *Once delay reasons have been selected and added to Apollo, and staff has
begun filling in the delay reasons regularly, contact Vanessa Calderon to
havethese added to Monthly Metrics Graphs.
Work with Wendy and Michelleto set up data collection of these metrics;
Save scanned copies to PI Shared Drive and physically save manual tracked
sheets
Ask Wendy and Kelly Drake wheretheMonthly Tracking Sheets arethat
may contain this information already. Ifnot found, collect with Michelle as
with above items. Scan and save to PI Shared drive.
Data Available and Processes are Already Implemented
Data Available but Needs Implemented
Data Available and Processes are Already Implemented
Data Available but Needs Implemented
Data Available but unsureifImplemented
Data Available and Processes are Already Implemented
Data Not Currently Available nor Process Implemented
Data Not Currently Available nor Process Implemented
Process Management Chart:
IMMC Cath Lab Redesign 2018
Created by: Vanessa Calderon, BSIE
Sr. Process Improvement Engineer
Date Created: 7/16/18
Scheduler
LIMIT: 3-4 OPELS
Monday and 3-4
OPELS Friday
7:30am /
8:00am
• Guide
Implementation
Work
• Monitor Project
Specific Changes
• Know when to
“Do Something”
• Direct Future
Improvements

22. (4) Increase Same Day Discharges
22
• Patients holding in ED for Tele Floor
• Tele floors were full
• HOS patients unnecessarily bedded
• Elective cath lab patients complete procedure too
late in the day to go home
• Why???
– Were patients arriving late? No
– Was their recovery time too long? No
– Did they lack criteria for SDD? No
• We had operational issues preventing SDD

23. Next Steps: Project Management
• After recommendations accepted by
leadership, implementation handed off to
PM
• Matt Hof, PMP, overseeing project
implementation
• Leadership kick-off completed and
workgroup is in place
23

24. Thank You
24