operating room design - mayo case study

Re-engineering Operating Room Flow to Improve Operational Performance

C. Daniel Smith, MD

Institute for Healthcare OptimizationHospital Efficiency Seminar

July 25, 2013

1

•No financial or other relationship with any product or treatment discussed in this talk

Conflict of Interest / Disclosure

Today’s Goals/Objectives

• Case study on variability management

• Results – objective & subjective

• Personal reflections on do’s and don’ts

• Q&A

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Case Study Setting

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Mayo Clinic Culture

• Fully integrated healthcare practice

• Physician lead, consensus based, committee structured

• All physicians salaried*

• No productivity-based financial compensation adjustment*

• Academic practice with expectations of productivity in all “three shields”*

5

Mayo Clinic Florida

• 214 bed hospital (21 ORs, 28 ICUs) and outpatient practice within a single complex/campus – opened in 2008

• 11,900 admissions/ year: 55% surgical

• 12,000 operations/year – complex case mix (e.g., 150 liver transplants, 1,200 NS, 900 GISurg)

• 443,500 outpatient visits annually

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Mayo Clinic Florida - Demographics

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Four surrounding counties 47,000

Rest of Florida 23,000

Southeastern USA 11,000

Rest of USA 7,000

International 1,000

Total 89,000

Average age - 59

55% Medicare 2010 OM – 6.7%

Mayo Clinic Florida - Staff

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Patient Care Research Total

Staff physicians and scientists 307 41 348

Residents, fellows, students 239 85 324

Administrative and allied health 2,918 146 3,064

Total 3,464 272 3,736

CEO Imperatives

• Increase capacity with existing resources

• Leverage organization’s core values (needs of patient come first, teamwork)

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• Aggressive growth and expansion (“30% increase in volume by 2012”)

• Focus on optimization

• “Just do it”

• Rapid cycle change

Healthcare Delivery Goals

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To provide the right care

To the right patient

At the right time


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At the right time *Outcomes, Safety, Service


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At the right time

VARIABILITY IS THE ENEMY

*Outcomes, Safety, Service

Variability in Healthcare

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Clinical Variability

Professional Variability

Flow Variability


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Flow Variability

Natural Variability – a result of naturally

occurring processes; uncontrollable


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Flow Variability

Natural Variability – a result of naturally

occurring processes; uncontrollable

Artificial Variability – a function of man made

decisions; controllable

Variability Management in Our ORs

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Variability in Operating Rooms

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Day-to-Day

Within-Day


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• Peaks and valleys in day-to-day volume of surgical cases

• “No one wants to operate on Monday or Friday”

Day-to-Day

Within-Day


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• Peaks and valleys in day-to-day volume of surgical cases

• “No one wants to operate on Monday or Friday”

Day-to-Day

• On the day of surgery, changes to the OR schedule and resource allocation

• Emergencies, add-ons, delays, etc.

Within-Day

Day-to-Day Variability

Day-to-Day Variability

StaffingHospital censusSupply chain…..

Within-Day Variability – Light Day

Within-Day Variability – Busy Day

Within-Day Variability

No.

Roo

m C

hang

esElective Rooms on Day of Surgery

2009-2010

8%

Within-Day Variability

No.

Roo

m C

hang

esElective Rooms on Day of Surgery

2009-2010

8%

SafetyPatient satisfactionTeamwork….

• Prime time OR utilization <65%

• 15 FTEs of overtime every pay period

• Low surgeon and staff satisfaction with OR management and efficiencies

• Concern about absence of specialty specific teams

• Frequent disruptions of elective cases by transplants and urgent cases

• “Whatever / Whenever” culture

“Our ORs Are A Mess”

Benefit of Managing Variability

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Patient – predictability, reliability, safety

Organization – optimize resources & throughput

Departments – collaboration, more resources

Individuals – predictable lives, teams, “flow”

Ben

efits

Operating Room Redesign

Collaboration with IHO

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OR Variability Management Team

• Chair, Surgical Committee & Chair, Department of Surgery

• Vice Chair, Surgical Committee & Chair, Department of Anesthesiology

• Chair, Department of Ophthalmology

• Administrator, Surgery & Procedural Operations

• Director, Surgical Services

• Director, Systems and Procedures

• Financial Analyst November 2009 meets 2X weekly

Consulting with Institute for Healthcare Optimization (IHO)MVP

Managing Variability Program

MVP – Key Principles

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• Separate and isolate Urgent/Emergent (Natural Variation) from Elective (Artificial Variation) surgical cases

• Effect a predictable and consistent daily elective surgical schedule

• Minimize the number of changes to the elective surgical schedule on the day of surgery

• Optimize the ability for teams to remain intact and surgeons to work with their primary teams

Goals for MVP

• Increase throughput / utilization

• Decrease overtime

• Assure access for urgent/emergent cases

• Assure predictable and reliable elective schedule

• Optimize room flow and efficiencies

• Reduce and limit the number of same-day changes to the elective surgical schedule

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Case Type Ave Waiting Time

A 11 minutes

B 12 minutes

C 13 minutes

D 15 minutes

E 18 minutes

Urgent/Emergent

Work-Ins ElectiveNo of Rooms: 2

Includes: Unscheduled A-E + H/L Tx Cases + prior night Es

Includes: NS, CTS, GS, Ortho C&A Work-Ins + Abdominal TxAve Room Utilization: 64.9%Ave Room Utilization:

33.5%

No of Rooms: 2

Target Utilization: 80.0%

“Opportunity”: + 1,370 Cases

No of Rooms: 15

Elective Rooms 7:30-5:00

Work-in Rooms 7:30-7:00

Staffing Model Approved

Re-Engineered ORs – Initial Plan

Within 48 hours

Urgent / Emergent (3)

Elective (16)Scheduled

Natural Variability

Artificial Variability

401 402 403 404 102 103 104 105 405 406 407 408 409 410 411 412 414 415

2000

1900

1800

1700

1600

1500

1400

1300

1200

1100

1000

0900

0800

0700

EE

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EE

EEEE

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401 402 404 102 103 104 105 403 405 406 407 408 409 410 411 412 414 415

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MVP Results – Time Frame

• Baseline Data

Whatever, Whenever

Nov 1, 2010 – Oct 31, 2011

Nov 1, 2009 – Oct 31, 2010

Nov 1, 2011 – Jul 31,

2012

Aug 1, 2012 – Present

Pre-MVP MVP 1 MVP II MVP III

• Full implementation

Highest compliance

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Journal of the American College of SurgeonsVolume 216, Issue 4 , Pages 559-568, April 2013

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Pre- MVP MVP I % ChangeSurgical Cases 11,874 12,367 4%Surgical Minutes 1,757,008 1,844,479 5%OR Utilization (19 Room Model) 61% 64% 5%Number of Overtime FTE's (average) 7.4 5.4 -27%Staff Turnover (highest to most recent) 20.3% 11.5% -43%Daily Case Volume Variation 55.24 44.06 -20%Daily Surgery Minutes Variation 6,531 5,124 -22%Elective Room Changes (Average/Mon) 80 25 -69%Elective Room Changes (%) 8% 2% -70%

Salary Dollars (Adjusted for Salary Increases) Total $12,607,061 $13,395,997 6% Monthly $1,045,942 $1,115,646 7%

Cost/Case $1,062 $1,070 0%Cost/Minute of Surgery $7.18 $7.26 1%

Staff Turnover Cost (millions) $2.47 $1.40 -43%Overtime Cost Savings $111,488

Total OR Net Revenue (Fee Increase adjusted) $93,929,569 $98,686,963 5%

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44239334429524619714899501

11000

10000

9000

8000

7000

6000

5000

4000

3000

2000

Observation

Indiv

idual V

alu

e

_X=6132

_X=6244

UCL=9397UCL=8806

LCL=2866

LCL=3682

Pre-MVP MVP

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2

22

1

2

11

11

1

22

2

1

Total Patient In Room Minutes per Day (Work-In & Elective)

44239334429524619714899501

70

60

50

40

30

20

10

Observation

Indiv

idual V

alu

e

_X=42.98

_X=43.33

UCL=70.60

UCL=65.36

LCL=15.36

LCL=21.30

Pre-MVP MVP

2

2

2

1

2

1

2

22

Cases per Day by MVP Staging (Work-In & Elective)

↓ 20%

Daily Case Volume Variation

↓ 22%

Daily Surgery Minutes Variation

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No.

Roo

m C

hang

es

Number of Changed Elective Rooms on Day of Surgery

#3 GOAL: Assure predictable and reliable elective schedule#6 GOAL: Reduce and limit the number of same-day changes to the elective schedule

↓ 69%

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No.

Roo

m C

hang

es

Number of Changed Elective Rooms on Day of Surgery

#3 GOAL: Assure predictable and reliable elective schedule#6 GOAL: Reduce and limit the number of same-day changes to the elective schedule

↓ 69%

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↓ 43%

415414412411410409408407406405404403402401106105104103102

1:30

1:24

1:18

1:12

1:06

1:00

0:54

0:48

0:42

0:36

0:30

0:24

0:18

Room

WO

-WI

95% CI for the MeanInterval Plot of WO-WI same specialty -October 2010

415414412411410409408407406405404403402106105104103102

1:40

1:30

1:20

1:10

1:00

0:50

0:40

0:30

0:20

Room

WO

-WI

95% CI for the MeanInterval Plot of WO-WI same specialty-Jan 2011

OR Redesign - Summary

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• Resulted in more cases and more minutes of surgery

• More of the surgical volume done during prime time

• Added staff without increasing cost/case

• Access to OR for transplants and other urgent/emergent cases not compromised

• The number of same day changes to the elective schedule decreased significantly

• Substantial cost savings without compromise in outcomes

OR Redesign - Concerns/Barriers

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• Too restrictive

• Hurts the high volume, two-room surgeon

• Decisions not transparent

• Clinic was negatively impacted

• Small groups couldn’t be immediately available for urgent / emergent cases

• Doing cases at the end of clinic and before 7:00 was not very appealing

• Open prime time availability was being wasted

MVP Concerns/Barriers

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“ I say a prayer everyday that I don’t have to finish my career working under MVP”

Anonymous Surgical Chair

The Divine Barrier

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Pre-MVP

Nov 1, 2010 – Oct 31, 2011

MVP 1

Nov 1, 2009 – Oct 31, 2010

Nov 1, 2011 – Jul 31,

2012

MVP II

• Baseline Data

Whatever, Whenever


MVP III


Highest compliance

52


Pre-MVP

Nov 1, 2010 – Oct 31, 2011

MVP 1

Nov 1, 2009 – Oct 31, 2010

Nov 1, 2011 – Jul 31,

2012

MVP II

• Baseline Data

Whatever, Whenever


MVP III


Highest compliance

• Opened rooms

• Over allocated Block Time

Minimal compliance

53


Pre-MVP

Nov 1, 2010 – Oct 31, 2011

MVP 1

Nov 1, 2009 – Oct 31, 2010

Nov 1, 2011 – Jul 31,

2012

MVP II

• Baseline Data

Whatever, Whenever


MVP III


Highest compliance

• Opened rooms

• Over allocated Block Time

Minimal compliance

• Lifted overtime pre-approval

• Block sharing

• Block give back

Urgent/emergent

isolation only

4137332925211713951

14

12

10

8

6

4

2

Observation

Indiv

idual V

alu

e

_X=6.43

_X=7.48

_X=8.85 _

X=7.89

UCL=11.11UCL=10.61

UCL=12.67 UCL=12.53

LCL=1.75

LCL=4.35LCL=5.03

LCL=3.24

Pre-MVP MVP I MVP II MVP III

1

Not General Surgery

4137332925211713951

40

30

20

10

0

ObservationIn

div

idual V

alu

e

_X=16.64

_X=10.78

_X=14.13 _

X=11.16

UCL=37.84

UCL=24.31UCL=25.72

UCL=20.50

LCL=-4.56LCL=-2.76

LCL=2.53 LCL=1.81

Pre-MVP MVP I MVP II MVP III

General Surgery

MVP – Engagement &Sustainability

Variability

MVP – Engagement &Sustainability

Not General Surgery

General Surgery (maintained

MVP)

Overtime

OR Redesign – Lessons Learned

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• Separating surgical patient flows makes sense – decrease cost / increase quality

• Fully understand and appreciate the culture and model of your practice

• Ensure proper resources are in place and functional prior to redesign implementation

• Accurate data collection is vital

• Redesign efforts should become part of your “existence”….not simply a project with a defined beginning and ending

OR Redesign – Lessons Learned

• Establish reporting metrics/tools and management metrics/tools

• Communicate, communicate, communicate – identify the key stakeholders early and at a minimum establish neutrality regarding redesign

• Plan to change the program – be careful to not change too early and don’t be too rigid (yes I mean this)

• You CAN NOT over-estimate the potential disruptive behavior this might stimulate – leaders need to create a safe place and way to support each other

Hazards of Leading of Change

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“And one should bear in mind that there is nothing more

difficult to execute, nor more dubious of success, nor

more dangerous to administer than to introduce a new

order to things; for he who introduces it has all those

who profit from the old order as his enemies; and he

has only lukewarm allies in all those who might profit

from the new. ”

from Niccolo Machiavelli's "The Prince"

operating room design - mayo case study

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