SIMUL8 Healthcare Designing New Spaces and Processes

7/30/2019 SIMUL8 Healthcare Designing New Spaces and Processes

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Improving Healthcare

WorkshopBrittany Hagedorn


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SIMUL8 Corporation | SIMUL8.com | [email protected]

Introductions

Brittany Hagedorn is SIMUL8s new Healthcare Lead for North America.

Brittanys 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.


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Clinical Quality and

Patient Safety

Management

Consulting

IntroductionsMy 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 intoexecutive 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.


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Agenda

I. Project OverviewII. Results

III. Recommendations

IV. Discussion & Next Steps


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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 teams

request was for an Excel

analysis that wouldproduce:

An average number

of patients.

An average number

of beds.

After discussions, we

recommended a project

charter for a simulationthat would produce:

The range for the

expected number of

beds.

Identification of anydownstream effects.


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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

PrepSurgery

Post-

Surgery

Recovery

Home

Observation

Return to

Unit

Entry Points Post-Surgical Routing


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Project Overview Model BuildingThis process translated into a SIMUL8 model quickly, but there was

some additional work to build the OR schedule into the simulation.

Entry

PointsResources

Post-

surgical

routing


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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


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-

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 weretransferred 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 bedsper day, without insight into daily

variation or downstream effects.


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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

Volume14,000 15,000 16,000

Days with

Delayed

Surgeries

67% 77% 82%

Number of

Delayed

Surgeries

6 daily 9 daily 10 daily

Number of

ObservationPatients 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 increaseif the growth target is reached.


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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

3035

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


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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 RoomsRepurpose 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 PolicyThe plan for the new unit was to allow patients families to remain in

their patients prep room during the surgery, and return the patient to the same location for

recovery.

3. Observation Patient PolicyModify 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.


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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.


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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

Floor2 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 distributeobservation patients more evenly throughout the week.


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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.


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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

10.377%

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

%ofDayswithDe

lays

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.


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Lessons Learned

OVERALL PROJECT

Unexpected findings On several occasions, the analysis results did not turnout 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 notalways 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


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Discussion and Questions

Great ideas need landing gear as well as wings. C. D. Jackson


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Next Steps

If you enjoyed todays discussion, please join us inSeptember 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!


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Appendix Additional Analysis


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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


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Patient Delay Durations

4

18

58

15

26

44

30 3233

0

5

10

15

20

25

30

35

40

45

50

0

5

10

15

20

25

Monday Tuesday Wednesday Thursday Friday

AverageTimeinQueue(min)

PatientsthatW

aitedforPrep


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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.A

verageDuration

BlockTime

Utilization


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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

SIMUL8 Healthcare Designing New Spaces and Processes

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