SIMUL8 Healthcare: Designing New Spaces and Processes with simulation

Improving Healthcare
Workshop
Brittany Hagedorn

SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
Introductions
Brittany Hagedorn is SIMUL8’s new Healthcare Lead for North America.
Brittany’s mission is to promote the use of process
simulation and related tools within healthcare.
The role will include:
1. Supporting existing users.
2. Publicizing the great work already being done.
3. Fostering growth of the simulation community.
4. Pioneering new applications within healthcare.
5. Developing tools and training.

Clinical Quality and
Patient Safety
Management
Consulting
Introductions
My experience has been in project-oriented roles, first as a Six Sigma Black
Belt within a hospital system, then as an external consultant. Through these
roles, I have had the privilege to work on a wide variety of challenges.
Lean and Six Sigma
(Process Improvement)
My favorite projects include:
• Reducing the lead time for pediatric sedated procedures from six weeks to seven days.
• Addressing bottlenecks in nursing workflows.
• Eliminating 70% of duplicative “double checks” for physician documentation.
• Constructing a clinical quality scorecard that could be easily managed and integrated into
executive compensation.
• Developing a primary care compensation plan for 150+ physicians to incentivize their
transition toward a value-based, accountable clinical care model.
• Creating an integration strategy for a newly formed cardiology medical group.
• Building a business case for post-acute care services.
• Supporting preventable harm interventions.

Agenda
I. Project Overview
II. Results
III. Recommendations
IV. Discussion & Next Steps

Project Overview – Goals
A local hospital was constructing a new bed tower. They wanted to know often
they would need a medical/surgical bed for post-surgical observation patients.
We recommended
a simulation.
The executive team’s
request was for an Excel
analysis that would
produce:
• An average number
of patients.
• An average number
of beds.
After discussions, we
recommended a project
charter for a simulation
that would produce:
• The range for the
expected number of
beds.
• Identification of any
downstream effects.

Project Overview – Process
The process to be modeled was fairly simple, with a few routing decisions.
Each step had a variable time duration, which included both random
variation and patient-specific factors such as specialty and acuity.
Inpatients
Outpatients
Add-ons
Pre-Surgery
Prep
Surgery
Post-
Surgery
Recovery
Home
Observation
Return to
Unit
Entry Points Post-Surgical Routing

Project Overview – Model Building
This process translated into a SIMUL8 model quickly, but there was
some additional work to build the OR schedule into the simulation.
Entry
Points
Resources
Post-
surgical
routing

Project Overview – Excel Interface
By utilizing a unique identifier for each patient entering the simulation, we
obtained individual-level data and results that were like-real-life.
Patient MRN
Characteristics Scheduled Actual
Time Stamps

-
0.50
1.00
1.50
2.00
2.50
3.00
Monday Tuesday Wednesday Thursday Friday
PatientsperDay
Results – Patient Volumes
The model assumed a continuation of current policy, which meant that
observation patients would remain in the pre/post surgical suite until discharged
or the end of the day. At the end of the day, all remaining patients were
transferred to an inpatient unit, which results in longer stays and increased costs.
Observation Patients to Floor per Day
• With current policies, there would be
fewer than two patients per day needing
placement at the end of the day.
• As a result, additional inpatient beds
dedicated to observation patients would
not be needed.
Note: The variability by day of the week was due to the surgeon
specialty mix.
Excel analysis resulted in 1.3 beds
per day, without insight into daily
variation or downstream effects.

Results – Unexpected Findings
However, by using a simulation, we were able to capture additional performance
metrics, which suggested that there may be other potential issues.
FY 2013 FY 2018
Maximum
Schedule
Annual Patient
Volume
14,000 15,000 16,000
Days with
Delayed
Surgeries
67% 77% 82%
Number of
Delayed
Surgeries
6 daily 9 daily 10 daily
Number of
Observation
Patients to Floor
1.3 daily 1.5 daily 2.1 daily
Additional Performance Metrics
• The simulation queues showed that many
patients were seeing delayed surgery starts.
• With current state processes and policies,
this would happen on over 65% of days.
• When delays did occur, it would affect on
average 6 patients per day.
In addition, the frequency and
duration of delays will increase
if the growth target is reached.

Results – Operational Implications
Delayed surgeries are caused by a bed shortage, which prevents patients
from being prepped for their procedure on time. This directly affect
profitability, either in foregone revenue or increased staffing costs.
0
5
10
15
20
25
30
35
40
45
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
NumberinUse
Hour of the Day
Note: The second surge in O.R. volumes depicts delayed patients finally
getting through pre-op into surgery.
Observation patients remain in
Pre/Post Unit
Not enough bed capacity for
arriving patients
Delayed prep causes delayed
surgery start times
Patients are cancelled or staff
must work overtime
Example Day – Effect of Bed Shortage
Pre/Post Beds
O.R. Rooms
Maximum Bed Capacity

Recommendations – Alternatives
Given this information, the natural question is – how do we fix it?
There were three alternative solutions that were simulated, in order to measure the real impact
that implementation would have.
1. Pre-Admission Testing Rooms – Repurpose the four pre-admission testing rooms that were
adjacent to the pre/post suite. These could be retrofitted before construction was complete
as recovery spaces.
2. Family Waiting Policy – The plan for the new unit was to allow patients’ families to remain in
their patient’s prep room during the surgery, and return the patient to the same location for
recovery.
3. Observation Patient Policy – Modify the policy to indicate that observation patients should be
moved to an inpatient unit if they will be staying for longer than a pre-determined threshold.

Recommendations – Voting Results
Please Vote – Which alternative was the most effective?
A. Reclaim 4 pre-admission testing rooms.
B. Ask families to move to the waiting room during
surgery.
C. Move observation patients to inpatient beds after
surgery.

Recommendations – Best Technical
Modifying the family waiting policy was the most effective at balancing
the needs of the inpatient units and operating rooms.
Family Remains in Pre/Post
Room during Surgery
Family Moves to Another
Location during Surgery
% Days with Delays 77% 45%
# of Patients Delayed 10 daily / 2,647 annual 1 daily / 287 annual
# Observation Patients to
Floor
2 daily / 417 annual 0 annual
• The change in policy would minimize the number of delayed cases and eliminate the need
for inpatient beds to house observation patients, releasing bed capacity for other uses.
• Additional improvement could be made by modifying the O.R. block schedule to distribute
observation patients more evenly throughout the week.

Recommendations – Voting Results
Due to other factors, this alternative was not implemented.
Please Vote – Which was the primary barrier?
A. The solution was too technically complex to implement.
B. We did not have the right executives in the room to be
able to make the policy decision.
C. There were other programs being implemented that
were perceived to be in conflict.
D. Political divisions created barriers to buy-in.

Recommendations – Trade-Offs
Ultimately, it was institutional concern about Value Based Purchasing
(which rewards hospitals for patient satisfaction scores) that drove the
decision to modify the observation patient policy instead.
1.5
3.4
4.3
5.4
6.7
9.3
10.3
77%
45%
32%
20%
13%
5%
2%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0.0
2.0
4.0
6.0
8.0
10.0
12.0
No Limit 42 hours 40 hours 38 hours 36 hours 30 hours 24 hours
%ofDayswithDelays
NumberofPatientstoFloor
Policy Cut-Off Point
Daily Obs to Floor % Days with Shortage
The Ultimate Trade-Off
• The trade-off was a decision for the
executive team.
• As more observation patients were
moved to inpatient units, the number
of delays dropped dramatically.
• Ultimately, the policy was modified so
that every observation patient was
moved to an inpatient unit after
surgery.
The other factor to
consider is the impact
on E.R. throughput.

Lessons Learned
OVERALL PROJECT
• Unexpected findings – On several occasions, the analysis results did not turn
out as expected. Eventually, we discovered that the simulation was operating
correctly – but the process was not operating as it had been described.
• Scope creep – The scope of the project grew several times, as we uncovered
additional questions that needed to be answered.
• Stakeholder buy-in – Changing policy presents challenges, depending on the
stakeholders and their entrenched beliefs. The best technical solution will not
always be implemented.
RELATED TO DESIGN
• Rules of Thumb – Architecture and construction teams often rely on industry
standards when designing physical spaces, such as “four beds per OR”. But
every situation is unique and this approach results in over/under-built spaces.
• Earlier is Better – Simulation is helpful at any stage of the process, but to
reduce costs, earlier is always better. If we had completed this analysis a few
months earlier, we would not have needed to redo several rounds of
architectural plans, which prevented us from considering several alternatives.
A few last thoughts…

Discussion and Questions
“ Great ideas need landing gear as well as wings.” – C. D. Jackson

Next Steps
If you enjoyed today’s discussion, please join us in
September for the next workshop!
Are you facing complex processes and an overwhelming
amount of work to do? Suggest a future topic!
Join the simulation community by connecting with us on
LinkedIn, Twitter, or on our website at SIMUL8.com!

Appendix – Additional Analysis

Alternative 1 Impacts
77%
70%
65%
58%
49%
43%
35%
29%
22%
17%
12%
7% 6% 4% 3%2%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
%DayswithShortage
Number of Pre/Post Beds

Patient Delay Durations
4
18
5
8
15
26
44
30 32
33
0
5
10
15
20
25
30
35
40
45
50
0
5
10
15
20
25
Monday Tuesday Wednesday Thursday Friday
AverageTimeinQueue(min)
PatientsthatWaitedforPrep

Block Time Utilization
66%
70%
73% 75% 76%
80%
87%
-
1.00
2.00
3.00
4.00
5.00
6.00
7.00
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
CVS Other Uro Gyn Gen NOS ENT
RatioBlockvs.AverageDuration
BlockTimeUtilization

Block Time Utilization
77% 77% 74%
69%
76%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Utilization
Min
Avg
Max
66%
70%
73% 75% 76%
80%
87%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
CVS Other Uro Gyn Gen NOS ENT
Utilization
Min
Avg
Max

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