615 Presentation CT Staffing Analysis

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Production system engineering.

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04/22/2023 1

Outline• Introduction• Current workflow• Project goal & methodology• Analytical study• Simulation study• Recommendation• Lessons learned• Acknowledgements• Q&A

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Introduction

• University of Wisconsin Medical Foundation is the clinical practice organization for the faculty physicians of the UW School of Medicine and Public Health

• Provides medical and surgical services– Cancer, Neurology, Radiology, Transplant Services,

Trauma Surgery and many others

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Background

• CT Scanning (X-ray computed tomography)– A three-dimensional image of the inside of an

object is generated from a large series of two-dimensional X-ray images

– 19 different visit types, some requiring up to 4 studies

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Current state

• Two CT scanners available– New scanner with short scanning time– Old scanner for special studies, long scanning time

• Four technicians (14019) available– 1 tech (Tech A) operates the scanner– 2 techs (Tech B) escort and assist patients– 1 tech (Tech C) focus on scheduling, operates

the old scanner when necessary

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CT workflow

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IV

Reformatting

Image Review

Scanning

Preparation

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Project goal and performance measures

• Goal– Recommend cost-saving changes for the CT

scanning process through improving the staffing model to accommodate existing and future CT workflow

• Performance Measures– Study Length of Stay (LOS)

• LOS1 for new scanner• LOS2 for old scanner

– Technician utilization

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• PDCA (Plan, Do, Act, Check)• PSE (Parallel serial line with Exponential machines

with resources)

Process modeling Data collection Modeling and

analysis

ValidationWhat-If and sensitivity analysis

Recommendations

3 4 5

Methodology

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Analytical model

• Assisting patient changing and delivering paperwork not considered

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Simulation model

New scanner

Old scanner

Techs

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Input/Output

Arrival Wait IV Wait Preparation Scanning Image

ReviewImage

Reformat

Length of Stay (LOS)

Data point identification:

Cycle time for tech

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Data collection

• Method– Time study for 1 week– Average service times obtained from time study– Demand information obtained from patient visit

record of February and March

Time Point Optinal Staff Time Note1 Tech gets the patient from the front D 11:40:002 Tech takes patient to changing room v D 11:40:203 Tech picks up the patient from changing room v D 11:45:004 Tech starts IV test and screen v D 11:465 Tech completes IV test and screen v D 11:566 Tech and Patient arrive CT room D 11:56:307 Tech starts IV test and screen -8 Tech completes IV test and screen -9 Tech leaves the CT room and starts scanning K 12:03:00

10 Tech finishes the scanning and waits for image processing K 12:04:0011 Tech reformats the image for the previous patient -12 Tech completes reformatting for the previous patient -13 Reformatted image sent to the computer -14 Tech prints out the paper work to the radiologist -15 Image loading complete K 12:07:0016 Tech takes the patient out from CT room D 12:08:0017 Tech reformats the image for the current patient v K 12:07:3018 Tech completes reformatting for the current patient v K 17:08:4019 Reformatted image sent v K 12:11:0020 Tech prints out the paperwork to the radiologist -21 Tech sets up the CT machine for the next patient D 12:08-12:09

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Data analysis

• 44 patients observed• 75% of patients required IV• 36% of studies required manual image reformat• Inter-arrival time

– New scanner:20 min

• Avg. service time‐ IV time: 4 min‐ Preparation time: 1.6 min‐ Scanning time: 2.6 min‐ Image review time:3.6 min‐ Image reformat time: 3.2 min

• LOS1: 10.84 min

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• Errors are below 10%. Both models are validated.

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Model validation & analytical results

Performance

measure

Analytical

Simulation

Collected data

Error (Analytica

l)

Error (Simulatio

n)

LOS1 (min) 11.64 11.33 10.84 7.38% 4.52%

Resource utilization Analytical Simulation

LOS1(min) 11.64 11.33

LOS2(min) 23.29 22.94

Utilization A (%)

40.0940.07

Utilization B (%)

19.1419.05

Utilization C(%)

4.714.48

Observation:Low utilization of

Tech B & C

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Simulation model

• Purpose– Perform what-if analysis to evaluate the impact of

• Different staffing settings• Increasing demand

– Conduct sensitivity analysis to identify bottleneck operation

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What-If analysis

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• Staffing– Scenario 1: 1 A, 1 B, 1 C– Scenario 2: 1 A, 1 B

• Demand– Scenario 3: Increase demand by 10%– Scenario 4: Increase demand by 20%

• Combination– Scenario 5: combination of Scenario 1 and 4– Scenario 6: combination of Scenario 2 and 4

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What-If (Staffing, Scenario 1)

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• 1 A, 1 B, 1 C: reduce the number of Tech B from 2 to 1 From To Impact

LOS1 (min) 11.33 11.56 1.98%

LOS2 (min) 22.94 23.13 0.85%

Utilization A (%) 40.07 40.07 0

Utilization B (%) 19.05 38.10 19.05

Utilization C (%) 4.48 4.48 0

• Impact on LOS is minimal• Utilization of Tech B doubles• Loss of flexibility, two scanners cannot be used at

the same time

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4 5

What-If (Staffing, Scenario 2)

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• 1 A,1 B: Reduce the number of Tech B from 2 to 1; replace Tech C with a lower level scheduling assistant From To Impact

LOS1 (min) 11.33 12.08 6.57%

LOS2 (min) 22.94 23.63 3.05%

Utilization A (%) 40.07 44.55 4.48

Utilization B (%) 19.05 38.10 19.05

Utilization C (%) 4.48 0 -4.48

• LOS1 increases by less than 7%• Staff utilization increases

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4 5

What-If (Demand, Scenario 3, 4)

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• Original demand• Increased demand: 10% (Scenario 3), 20% (Scenario

4)Original demand Scenario 3 Scenario 4

LOS1 (min) 11.33 11.64 12.28

LOS2 (min) 22.94 23.55 23.55

Utilization A (%) 40.07 44.37 49.83

Utilization B (%) 19.05 21.06 23.64

Utilization C (%) 4.48 4.89 5.57

• LOS increases by less than 9%• Staff utilization remains below 50%• System is capable of handling higher demand in the

future

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4 5

What-If (Combination, Scenario 5)

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• Scenario 5: Reduce the number of Tech B from 2 to 1; increase demand by 20%

From To Impact

LOS1 (min) 11.33 13.31 17.45%

LOS2 (min) 22.94 24.50 6.79%

Utilization A (%) 40.07 49.83 9.76

Utilization B (%) 19.05 47.28 28.23

Utilization C (%) 4.48 5.57 1.09

• LOS1 increases more than 15%• Utilization of Tech A and B close to 50%

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4 5

What-If (Combination, Scenario 6)

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• Scenario 6: Reduce the number of Tech B from 2 to 1; replace Tech C with a lower level scheduling assistant; increase demand by 20%

From To Impact

LOS1 (min) 11.33 14.65 19.27%

LOS2 (min) 22.94 26.24 11.44%

Utilization A (%) 40.07 55.40 15.33

Utilization B (%) 19.05 47.28 28.23

Utilization C (%) 4.48 0.00 -4.48

• LOS1 increases more than 19%• Utilization of Tech A over 50%

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4 5

Sensitivity analysis – Bottleneck identification

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• Reduce service times by 10%– Scenario 1: IV time– Scenario 2: Preparation time– Scenario 3: Scanning time– Scenario 4: Image review time– Scenario 5: Image reformat time

Measure\Scenario 1 2 3 4 5

LOS1 (min) 10.97 11.16 11.03 10.89 11.31

LOS2 (min) 22.82 22.92 22.32 22.06 23.10

Utilization A (%) 39.63 39.99 38.70 38.20 39.43

Utilization B (%) 18.35 18.71 18.40 19.12 19.12

Utilization C (%) 4.47 4.48 4.36 4.31 4.45

Bottleneck operation

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4 5

Final scenario

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• Reduce image review time by 50% by upgrading computer motherboard

• Combined with What-If Scenario 6 – 1 A, 1 B; increase demand by 20%

What-If Scenario 6 To Impact

LOS1 (min) 14.65 10.65 -27.30%

LOS2 (min) 26.24 19.32 -26.37%

Utilization A (%) 55.40 42.85 -12.55

Utilization B (%) 47.28 47.44 0.16

Utilization C (%) 0.00 0.00 0.00

• LOS can be reduced significantly

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Cost savings

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Current Scenario

Improved Scenario

# $/year # $/year

Tech 14019 4 220,000 1 55,000

Tech 14010 - - 1 45,000

Scheduling Assistant - - 1 35,000

Total 4 220,000 3 135,000

Salary savings = 85,000 $/year

Total saving (including overhead, benefit) = 161,500 $/year

Note: Salary information obtained from www.salary.com

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Conclusions

• Low staff utilization in current system

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3 4

Reduce the number of Tech B to 1, and replace Tech C with a lower level scheduling assistant

Slight impact on LOS, staff utilization increases

But if demand increases 20%LOS increases significantly

Reduce image review time by half LOS decreases from 14.6 to 10.65 (min)

Potential total cost savings of $161,500/year can be achieved

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Lessons Learned

• Top management support is important• Need of understanding the system:

continuous learning • Difficulties to obtain data: unavailable,

hard to collect

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4 5

Questions?

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