SIMUL8 Healthcare: Designing New Spaces and Processes with simulation

Improving Healthcare

Workshop

Brittany Hagedorn

SIMUL8 Corporation | SIMUL8.com | [email protected]

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


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0.50

1.00

1.50

2.00

2.50

3.00

Monday Tuesday Wednesday Thursday Friday

Pat

ien

ts p

er D

ay

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

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40

45

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Nu

mb

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in U

se

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

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No Limit 42 hours 40 hours 38 hours 36 hours 30 hours 24 hours

% o

f D

ays w

ith

Dela

ys

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of

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o F

loo

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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%

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90%

40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

% D

ays

wit

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Number of Pre/Post Beds


Patient Delay Durations

4

18

5 8

15

26

44

30 32 33

0

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Ave

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min

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th

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d f

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Pre

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Block Time Utilization

66% 70%

73% 75% 76% 80%

87%

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7.00

0%

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CVS Other Uro Gyn Gen NOS ENT

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vs

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ura

tio

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Blo

ck

Tim

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Block Time Utilization

77% 77% 74% 69%

76%

0%

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70%

80%

90%

Uti

lizati

on

Min

Avg

Max

66% 70%

73% 75% 76% 80%

87%

0%

10%

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100%

CVS Other Uro Gyn Gen NOS ENT

Uti

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Max

Date post:	22-Apr-2015
Category:	Technology
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SIMUL8 Healthcare: Designing New Spaces and Processes with simulation

Technology