Genesis Hospital. Surgery Simulation. Curtis Theel, MBA, CSSBB, PMP 2016 ASQ Columbus Spring Conference March 7 th, 2016
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1 Genesis Hospital Surgery Simulation Curtis Theel, MBA, CSSBB, PMP 2016 ASQ Columbus Spring Conference March 7 th, 2016
2 Background information In 2011, Genesis Healthcare System decided to combine 2 separate Hospitals, Good Samaritan and Bethesda The Besthesda Hospital would be renovated and a new 3-story Tower would be added on to it Construction would take place from The 3-story tour would primary house the Emergency Department, Surgical Services, and a Critical Care Unit (CCU) The Problem: How do we determine how large to build our Surgical Suite?
3 GS OR Rooms Current Configuration BH OR Rooms Total OR suites GS = 8 BH = 6 Combined = 14
4 Total OR suites GS = 8 BH = 6 Combined = 14 With zero analysis, administration would default to build 14 OR rooms. Was this the correct decision?
5 GS OR Rooms Current Configuration BH OR Rooms Rm 1: 685 Rm 2: 276 Rm 3: 1089 Rm 1: 309 Rm 2: 513 Rm 3: 481 Rm 4: 1297 Rm 5: 783 Rm 6: 401 Rm 4: 393 Rm 5: 3 Rm 6: 21 Rm 7: 470 Rm 8: 291
6 GS OR Rooms Current Configuration BH OR Rooms Rm 1: 685 X Rm 2: 276 Rm 3: 1089 Rm 1: 309 Rm 2: 513 Rm 3: 481 Rm 4: 1297 Rm 5: 783 Rm 6: 401 Rm 4: 393 X X Rm 5: Rm 6: 3 21 Rm 7: 470 X Rm 8: rooms at BH never used 2 rooms as GS used less than 25% of normal use Rooms currently used = 10 rooms
7 Basic analysis indicated we only use 10 rooms. Is this the correct amount of rooms to build? 2 key questions: How do we validate this? How do we plan for the future? The answer: SIMULATION
8 Model Configuration 3 important steps to developing a simulation model Arrival Patterns Process flow Process times The rest is mechanical and data analysis
9 Model Configuration For arrival patterns and process times: Included complete OR data; separated service lines General Surgery Orthopedics Urology Neurosurgery CVOR Vascular Included complete Cath and EP Lab data; separated by type Cath Lab EP Lab Why differentiate between specialties?
10 Model Configuration *12 Operating Rooms *3 Cath Rooms *1 EP Room Note: For this model, the Hybrid OR was considered a normal-use OR
11 Arrival Distributions
12 Arrival Distributions First Method Arrival distribution by Specialty by Day
13 Arrival Distributions First Method Arrival distribution by Specialty by Day
14 Arrival Distributions Second Method Service Line Day Hour Neuro Monday % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 27.59% 6.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 8.62% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 5.17% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 8.62% 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 5.17% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Neuro % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% # of arrivals
15 Arrival Distributions Second Method Service Line Day Hour General Tuesday % 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 42.11% 22.81% 15.79% 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 33.33% 5.26% 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 15.79% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 42.11% 26.32% 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 28.07% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 35.09% 31.58% 21.05% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 26.32% 33.33% 21.05% 1.75% 1.75% 0.00% 0.00% 0.00% 0.00% General % 47.37% 15.79% 5.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 33.33% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 33.33% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 29.82% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 19.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 10.53% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 10.53% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% # of arrivals
16 Arrival pattern (all service lines) validation: Actual vs Flexsim output Mann-Whitney Test and CI: Actual Arrivals, Flexsim Arrivals Since the p-value is not less than N Median the chosen a level of 0.05, Actual you Arrivals conclude that there is insufficient Flexsim Arrivals evidence to reject H0. Therefore, the data does not support the Point estimate for ETA1-ETA2 is hypothesis that there is a difference 95.0 Percent CI for ETA1-ETA2 is ( , ) between the population medians. W = Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at The test is significant at (adjusted for ties)
17 Arrival pattern (individual service lines) validation: Actual vs Flexsim output Mann-Whitney Test and CI: Ortho Wed Flexsim, Ortho Actual Since the p-value is not less than N Median the chosen a level of 0.05, Ortho you Wed Flexsim conclude that there is insufficient Ortho Actual evidence to reject H0. Therefore, the data does not support the Point estimate for ETA1-ETA2 is hypothesis that there is a difference 95.0 Percent CI for ETA1-ETA2 is (-1.000,1.000) between the population medians. W = Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at The test is significant at (adjusted for ties)
18 Process Times (individual service lines) validation: Actual vs Flexsim output Mann-Whitney Test and CI: Flexsim Gen 3, Actual Gen 3 Since the p-value is not less than N Median the chosen a level of 0.05, Flexsim you Gen conclude that there is insufficient Actual Gen evidence to reject H0. Therefore, the data does not support the hypothesis that there is a difference Point estimate for ETA1-ETA2 is between the population medians Percent CI for ETA1-ETA2 is (-6.47,6.44) W = Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at The test is significant at (adjusted for ties)
19 *Video clip of simulation*
20
21 The Output Replication Number Bed4 Bed5 Bed6 Bed7 Bed8 Bed9 Bed10 Bed11 Bed12 Bed13 Bed14 Bed15 Max Occupied Data= Monday 100
22 OR Monday Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 5% of the day 12.5% = 1 hr 7:30a 3:30p
23 OR Tuesday Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 12% of the day 12.5% = 1 hr 7:30a 3:30p
24 OR Wednesday Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 15% of the day 12.5% = 1 hr 7:30a 3:30p
25 OR Thursday Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 20% of the day 12.5% = 1 hr 7:30a 3:30p
26 OR Friday Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 7% of the day 12.5% = 1 hr 7:30a 3:30p
27 OR Average Day (average of mon-fri usage patterns) =22.7 Percent rooms are concurrently occupied Example: 9 beds are used concurrently for 12% of the day 12.5% = 1 hr 7:30a 3:30p Note: Does include turnover time
28 Simulated OR utilization data by day of week 7:30a 3:30p Note: Does not include turnover time
29 Utilizing simulation for Scenario Analysis
30 10 OR s test Thursday 100 replications
31 OR Average Day (average of mon-fri usage patterns) CONVERTED to projected volumes; rooms removed = Percent rooms are concurrently occupied 93% 69% 43% 23% Add 4.4% volume added to each room Reduce to 9 OR s
32 OR Utilization by volume growth 10% growth Service Line Day Hr Service Line Day Hr General Monday % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General Monday % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 18.97% 41.38% 15.52% 8.62% 0.00% 0.00% 0.00% 0.00% 0.00% General % 20.86% 45.52% 17.07% 9.48% 0.00% 0.00% 0.00% 0.00% 0.00% General % 39.66% 15.52% 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 43.62% 17.07% 11.38% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 41.38% 22.41% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.52% 24.66% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 39.66% 20.69% 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 43.62% 22.76% 11.38% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 46.55% 24.14% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 51.21% 26.55% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 50.00% 15.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 55.00% 17.07% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 37.93% 6.90% 3.45% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 41.72% 7.59% 3.79% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 27.59% 10.34% 0.00% 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% General % 30.34% 11.38% 0.00% 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% General % 22.41% 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 24.66% 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 15.52% 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 17.07% 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 13.79% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 15.17% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 11.38% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 11.38% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 10.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 11.38% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.72% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.90% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 5.17% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 5.69% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 5.17% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 5.69% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General Tuesday % 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General Tuesday % 1.93% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.86% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 3.86% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 1.93% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 42.11% 22.81% 15.79% 1.75% 0.00% 0.00% 0.00% 0.00% 0.00% General % 46.32% 25.09% 17.37% 1.93% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 33.33% 5.26% 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% General % 50.18% 36.67% 5.79% 3.86% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 15.79% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 50.18% 17.37% 9.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 42.11% 26.32% 3.51% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 46.32% 28.95% 3.86% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 45.61% 28.07% 8.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 50.18% 30.88% 9.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 35.09% 31.58% 21.05% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% General % 38.60% 34.74% 23.16% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
33 OR Utilization by volume growth Note: Assumes 12 OR rooms available. Does not include Turnover Time Volume growth Subsequent quarter = 4.4% growth
34 Convincing others
35 Table top simulation completed on longest/busiest day in year Team utilized 10 OR rooms, 1 hybrid room, 1 minor procedure room Day 1 had 6, 4+ hour cases. 34 OR cases total Day 2 had 3, 4+ hour cases. 34 OR cases total Both days caseload fit running 10 hour day
36
37 Future Plan Minor Shell Hybrid standard OR Rooms 1 Hybrid Procedure Room 1 Shelled OR Room for future need
38 OR operational modeling Staff Utilization Determining FTE s for appropriate utilization in Surgery, Cath Lab, EP Lab
39 Service Line Typical Staffing by Case type Staff needed MD Nurse Tech Anes Total General Ortho Vascular Neuro Uro Cath Lab EP Lab *Did not include assistants to the Surgeons that are not hospital employees
40 EP MD EP RN OR MD Cath Tech Cath RN Cath MD EP Tech OR Anes OR Tech OR RN Perfusion Model with max needed staff added to availability
41 Minutes each RN would work per Replication Completed with each job role
42 Minutes converted to Utilization
43 Color coded by percentages and grouped in to 3 categories How many OR RN s were more than 50%, 55%, 60% utilized on average during day?
44 Current vs Simulation Staffing Current staffing Monday Tuesday Wednesday Thursday Friday Combined Techs Combined RNs Simulation staffing A difference of 27.8 FTE s per week Monday Tuesday Wednesday Thursday Friday FlexCombined Techs FlexCombined RNs
45 Conclusion: 10 OR s + 1 Hybrid would sufficiently accommodate current and increased volumes (up to +20% validated) Savings: Construction (hard savings): 2 less OR s = $4M Staffing (soft savings): $1.6M (salary + benefits)
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