mgh-2015

(1)

OIT 262: Operations

Class 7: Process Improvement in Health Care

Yonatan Gur

Graduate School of Business

Stanford University

(2)

Plan for Today

Reminder: Littlefield begins Wed. 4/29, 1:00pm

- Register your teams (username, password)

- Plan on meeting before 4/29 to strategize

- Inventory Management review session on 4/29

I. Process Flow at Mass General

II. Inventory Build-up at Bottleneck

III. Waiting Times at Non-Bottleneck Steps

IV. Recommendations

(3)

Goals for the MGH Case

Capstone Case in

Process Analysis Segment

• How can operations help deliver

health care/services more

efficiently?

• How can process and queueing

analysis help inform managerial

decisions and strategies?

(4)

Massachusetts General Hospital

• Mass General Hospital

–

Founded 1811

–

950 beds, 4.6 million square feet

–

#2 hospital in US (2014-2015, US News)

consistently 1

st

_{or 2}

nd

_{in recent years}

• Anesthesia Department

–

Birthplace of anesthesia

–

278 physicians, 198 nurses

–

Supports patients before, during, and after

surgery

• Pre Admission Testing Area (PATA)

–

Responsible for outpatients (43% of surgeries)

–

Evaluate safety of anesthesia before surgery

–

Inform patient

(5)

PATA Problems

Dr. Peter Slavin

President, Massachusetts General Hospital

Dear Dr. Slavin,

Last week I brought my mother into the Pre-Admission Testing Area. We live in Vermont, almost 3 hours away, and had to make a special trip for this appointment, which her oncologist, Dr. Paul Schneider, said was necessary to ensure a safe and successful surgery.

When we arrived at the clinic, the waiting room was so full, it was five minutes before my mother and I could get two seats together. We sat there for a full half-hour before they sent us back to get her blood pressure reading. We then waited back in the waiting room for another 45 minutes before being moved to an exam room. It was 20 minutes before a nurse finally came in and she mostly just asked questions I had already answered on a form provided by the front desk. After the nurse left, it was almost another half-hour before the doctor finally came in and he also asked many of the same questions. The

providers were very nice and apologetic, but of the almost 4 hours we spent in the clinic, only 1½ hours of that was actually face time with anyone!

Even more aggravating, while my mother was in surgery this morning, two families in the waiting room said their

relatives never even had to have a PATA appointment. One even had the same condition as my mother so I’m not sure why our PATA visit was even necessary.

I brought my mom from out-of-state because we were told that Mass General provides the best care in all of New England, maybe even the country, but that’s not at all what we experienced. I sincerely hope that we can expect more from our next visit to MGH.

~ Claire Bradley Rutland, VT

Average patient visit length: 3h15 min

Face time with providers: 1h20 min

Provider OT, frustrated patients & providers

“The providers were very nice and apologetic, but of the almost

4 hours we spent in the clinic, only 1½ hours

of that was actually face time with anyone!”

PATA can only see 65% of surgical out-patients

Cancelled or delayed surgeries

“two families in the waiting room said their relatives never even

had to have a PATA appointment. One even had the same

condition as my mother”

(6)

(7)

PATA Floorplan

Exam Room 1

Lab (Blood

work) Lab (Vitals + EKG) Office of the Nursing Director Closet Bathroom Bathroom Chart Room Kitchen (Charge Nurse Station)

PATA Floor Plan

Front Desk Su pp ly C lo se t Exam Room 2 Exam Room 3 Exam Room 5 Exam Room 4 Exam Room 7 Exam Room 6 Exam Room 8 Exam Room 9 Exam Room 10 Exam Room 11 Exam Room 12 EKG Bed Cabinets/Counter Top Computer Station

(8)

Exam Room 1

Lab (Blood

PATA Floor Plan

Front Desk Su pp ly C lo se t Exam Room 2 Exam Room 3 Exam Room 5 Exam Room 4 Exam Room 7 Exam Room 6 Exam Room 8 Exam Room 9 Exam Room 10 Exam Room 11 Exam Room 12 EKG Bed Cabinets/Counter Top Computer Station

(9)

Lab for Vitals and EKG

Exam Room 1

Lab (Blood

work) Lab (Vitals + EKG) Office of the Nursing Director Closet Bathroom Bathroom Chart Room Kitchen (Charge Nurse Station)

PATA Floor Plan

(10)

Charge Nurse Station

Exam Room 1

Lab (Blood

work) Lab (Vitals + EKG) Office of the Nursing Director Closet Bathroom Bathroom Chart Room Kitchen

(Charge Nurse Station)

PATA Floor Plan

(11)

Exam Room 1

Lab (Blood

PATA Floor Plan

Front Desk Su pp ly C lo se t Exam Room 2 Exam Room 3 Exam Room 5 Exam Room 4 Exam Room 7 Exam Room 6 Exam Room 8 Exam Room 9 Exam Room 10 Exam Room 11 Exam Room 12 EKG Bed Cabinets/Counter Top Computer Station

Exam Room

(12)

Chart Room

Exam Room 1

Lab (Blood

work) Lab (Vitals + EKG) Office of the Nursing Director Closet Bathroom Bathroom Chart Room Kitchen (Charge Nurse Station)

PATA Floor Plan

(13)

Lab (Blood Work)

Exam Room 1

Lab (Blood

work) Lab (Vitals + EKG)

Office of the Nursing Director Closet Bathroom Bathroom Chart Room Kitchen (Charge Nurse Station)

PATA Floor Plan

(14)

PATA Process Flow Diagram and Capacity

Wait

Check-in

Vitals + EKG

_{in Lab}

(Waiting Room)

RN Visit

MD Visit

Wait

Patients

arrive

Capacity = 30 pts/hr RN Chart Review RN Chart Write-Up RN Visit 27 min/pt 5 min/pt 11 min/pt (Waiting Room + Exam Room)

Wait

(Exam Room) MD Chart Review MD Chart Write-Up MP Visit 37 min/pt 10 min/pt 17 min/pt

Wait

Blood Work

in Lab

(Waiting Room)

Check-out

Patients

Leave

Capacity =12 pt/hr (lunch: 6 pt/hr) Capacity = 7 pt/hr (lunch: 2.8 pt/hr) Provider path Provider path Capacity = 30 pt/hr (lunch: 20 pt/hr) Capacity = 60 pt/hr

Arrival rate = 8 pts/hr 7am-12 and 2-3pm Arrival rate = 4 pts/hr 12-2pm (lunch)

Queue 1 Queue 2

Queue 3

Queue 4

Capacity = 7.5 pt/hr (lunch: 3.75 pt/hr)

(15)

Calculating PATA Process Capacities

• Check-in

–

Service time = 2 min/pt

–

Service rate = 30 pt/h

–

m = 1 attendant

–

Capacity = 30 pts/hr

• Vitals + EKG in Lab

–

Service time =10 min/pt

–

Service rate = 6 pt/h

–

m = 2 technicians

–

Capacity = 12 pt/hr

–

(lunch: 6 pt/hr)

• RN Visit

–

Service time =43 min/pt

• Chart review = 5 min/pt

• Visit with patient = 27 min/pt

• Chart write-up = 11 min/pt

–

Service rate = 1.4 pt/h

–

m = 5 nurses

–

Capacity = 7 pt/hr

–

(lunch: 2.8 pt/hr)

• MD Visit

–

Service time = 64 min/pt

• Chart review = 10 min/pt

• Visit with patient = 37 min/pt

• Chart write-up = 17 min/pt

–

Service rate = 0.94 pt/h

–

m = 8 MDs

–

Capacity = 7.5 pt/hr

–

(lunch: 3.75 pt/hr)

• Blood Work in Lab

–

Service time = 6 min/pt

–

Service rate = 10 pt/h

–

m = 3 technicians

–

Capacity = 30 pt/hr

–

(lunch: 20 pt/hr)

• Check-out

–

Service time = 1 min/pt

–

Service rate = 60 pt/h

–

m=1 attendant

(16)

PATA Process Capacities

Non-Lunch

Lunch

Step

Service Time

(min/pt)

Service Rate

(pts/hr)

Employees

# of

Capacity

(pts/hr)

Employees

# of

Capacity

(pts/hr)

Check-in

2

30

1

30

1

30 Vitals + EKG in Lab

10

6

2

12

1

6 RN Visit

43

1.40

5

7

2

2.8 MD Visit

64

0.94

8

7.5

4

3.75 Blood Work in Lab

6

10

3

30

2

20 Check-out

1

60

1

60

1

60

(17)

PATA Process Flow Diagram and Capacity

Wait

Check-in

Vitals + EKG

_{in Lab}

(Waiting Room)

RN Visit

MD Visit

Wait

Patients

arrive

Wait

Blood Work

in Lab

(Waiting Room)

Check-out

Patients

Leave

Queue 1 Queue 2 Queue 3 Queue 4 Capacity = 7.5 pt/hr (lunch: 3.75 pt/hr)

Bottleneck 

(18)

What Does Patient Flow Look Like

• Before the RN step…

–

Patients can flow through at the arrival rate until the waiting

room is full (like in Cranberry)

–

No info on waiting room capacity… let’s just assume it’s “large”

and never fills up (those who can’t find a seat just stand)

–

Then flow rate at steps before the RN is 8 pts/hr in non-lunch

times, 4 pts/hr during lunch

• At the RN step and afterwards…

–

RN capacity limits flow

–

So flow rate = 7 pts/hr in non-lunch times, 2.8 pts/hr during

(19)

Utilization Analysis

Non-Lunch

Lunch

Step

Flow Rate

(pts/hr)

Employees

# of

Capacity

(pts/hr)

Util.

Flow Rate

(pts/hr)

# of Employees

Capacity

(pts/hr)

Util.

Check-in

8

1

30

0.27

4

1

30

0.13 Vitals + EKG in Lab

8

2

12

0.67

4

1

6

0.67 RN Visit

7

5

7

1.00

2.8

2

2.8

1.00 MD Visit

7

8

7.5

0.93

2.8

4

3.75

0.74 Blood Work in Lab

7

3

30

0.23

2.8

2

20

0.14

(20)

Time Out: What’s Going on in This Process…

• RNs are overloaded

–

Like in Cranberry, they build up a backlog of work

–

Will work through backlog after patients stop arriving

–

Can analyze using inventory build-up diagrams

• Other stations have utilizations < 1

–

Waiting times in front of other stations will be driven by

randomness in arrivals/processing

–

Can analyze using queueing tools

• 

PATA has waiting times driven both by too little capacity (at RN

station) and randomness (at other stations)

(21)

Analyzing Inventory Buildup at the RN Station

• Capacity

–

7 am – 12 pm: 7 pts/hr

–

12 pm – 2 pm: 2.8 pts/hr

–

2 pm – end of day: 7 pts/hr

• Arrivals

–

7 am – 12 pm: 8 pts/hr

–

12 pm – 2 pm: 4 pts/hr

–

2 pm – 3 pm: 8 pts/hr

–

After 3 pm: 0 pts/hr

Backlog Accumulates at…

7 am – 12 pm: 1 pt/hr

12 pm – 2 pm: 1.2 pts/hr

2 pm – 3 pm: 1 pt/hr

(22)

Inventory Buildup at RN Station

4:12 pm Peak: 8.4 patients at 3 pm

(23)

Average Inventory at RN Station

Time

Length (hours) Start Inv End Inv Avg Inv

7 am - 12 pm

5

0

5

2.5 12 pm - 2 pm

2

5

7.4

6.2 2 pm - 3 pm

1

7.4

8.4

7.9 3 pm - 4:12 pm

1.2

8.4

0

4.2 Grand Average

4.11 Avg Patients Waiting = 4.11

Comment: Another way is by:

Avg Patients Waiting = AUC/b

If was triangle, we would get

0.5hb/b = 0.5*h = 4.2

(24)

Average Patient Waiting Time

• Average flow rate (out of RN queue)

–

7 patients/hr from 7 am to 12 pm (5 hours)

–

2.8 patients/hr from 12 pm to 2 pm (2 hours)

–

7 patients/hr from 2 pm to 4:12 pm (2.2 hours)

–

Weighted average = 6.1 patients/hr

• Average patient waiting time

–

Little’s Law: Inv = Flow Rate x Flow Time

–

Flow Time = Inv/Flow Rate

–



Waiting Time = (4.11 pts) / (6.1 pts/hr) = 0.67 hrs = 40 min

Average patient waits 40 minutes at RN station

Comment: another way is by AUC/(number of patients)

(25)

What Have We Done?

• RNs are overloaded

–

Like in Cranberry, they build up a backlog of work

–

Will work through backlog after patients stop arriving

–

Can analyze using inventory build-up diagrams

–



40 minutes of waiting time at RN

• Other stations have utilizations < 1

–

Waiting times in front of other stations will be driven by

randomness in arrivals/processing

–

Can analyze using queueing tools

• 

PATA has waiting times driven both by too little capacity (at RN

station) and randomness (at other station)

(26)

What’s Next?

• RNs are overloaded

–

Like in Cranberry, they build up a backlog of work

–

Will work through backlog after patients stop arriving

–

Can analyze using inventory build-up diagrams

–



40 minutes of waiting time at RN

• Other stations have utilizations < 1

–

Waiting times in front of other stations will be driven by

randomness in arrivals/processing

–

Can analyze using queueing tools

• 

PATA has waiting times driven both by too little capacity (at RN

station) and randomness (at other station)

(27)

PATA Process Flow Diagram and Capacity

Wait

Check-in

Vitals + EKG

_{in Lab}

(Waiting Room)

RN Visit

MD Visit

Wait

Patients

arrive

Wait

Blood Work

in Lab

(Waiting Room)

Check-out

Patients

Leave

Queue 1

Queue 2

Avg Wait Time = 40 min

Queue 3

Queue 4

Capacity = 7.5 pt/hr (lunch: 3.75 pt/hr)

(28)

Waiting at Vitals + EKG (Queue 1, 9 am to 12 pm only)

• Arrivals

–

Arrival rate = 1/a = 8 patients/hr

–

Average Interarrival time = a = 60/8 = 7.5 minutes

–

Std Dev of Interarrival Times from 9 am to 12 pm (Fig 2a) = 8.9 min

–

CV

_a

= Std Dev/Mean = 8.9/7.5 = 1.2

• Service

–

Average Processing Time = 10 min (case p. 10)

–

Std Dev of Processing Time = 3.5 min (case p. 10 footnote 9)

–

CV

_p

= Std Dev/Mean = 3.5/10 = 0.35

–

Number of Technicians/Stations = m = 2

• 

u = p/ma = 10/(2x7.5) = 0.667

(29)

Waiting Time Analysis at MD & Blood Work

Vitals + EKG

MD

Blood Work

a [min]

7.5

8.6

8.6 Std Dev a [min]

8.9

1.7

3.4 CV

_a

1.2

0.2

0.4 p [min]

10

64

6 Std Dev p [min]

3.5

29

2 CV

_p

0.35

0.45

0.33 m

2

8

3 u

0.67

0.93

0.23 T

_q

[min]

6.38

11.77

0.02 Queue 1

Queue 3

Queue 4

Arrival rate (1/a) after RN equals the capacity at RN = 7/60 = 1/8.6

Total waiting time from queueing effects = about 18 minutes

(30)

PATA Process Flow Diagram and Total Flow Times

Wait

Check-in

Vitals + EKG

_{in Lab}

(Waiting Room)

RN Visit

MD Visit

Wait

Patients

arrive

Time = 2 min RN Chart Review RN Chart Write-Up RN Visit 27 min/pt 5 min/pt 11 min/pt (Waiting Room + Exam Room)

Wait

Blood Work

in Lab

(Waiting Room)

Check-out

Patients

Leave

Time = 10 min

Total Time = 32 min

Provider path

Time = 6 min Time = 1 min

Queue 1

Avg Wait Time = 6.4 min Avg Wait Time = 40 minQueue 2

Queue 3

Avg Wait Time = 12 min

Queue 4

Average Wait Time = 0.02 min

Total Time = 47 min

(31)

Summary of Initial Analysis

• 1 hour (about 1/3 of total time in system) is non-value added

waiting

• Even more waiting (from patient perspective) during NP/MD

write-up times (value is added during these times but the patient is not

face-to-face with a provider)

• The non-value added waiting may be controllable via the design of

the system

–

Processing capacity/staff

–

Physical capacity/exam rooms

–

Scheduling

• Now that we know what’s driving the non-value added waiting

times, we can see how proposals impact that time…

(32)

Analysis of Taskforce Recommendations

We asked you to analyze three specific recommendations:

• Extend hours to 6:30 pm, increase time between appointments to

45 minutes

• Add an RN

• Add an MD

(33)

Extend Hours to 6:30 pm, Schedule Appointments Every 45 Minutes

• Current System

–

Non-lunch: 4 arrivals every 30

minutes  8 patients/hr

–

Lunch: 2 arrivals every 30 minutes 

4 patients/hr

• Proposal

–

Non-lunch: 4 arrivals every 45

minutes  5.3 patients/hr

–

Lunch: 2 arrivals every 45 minutes 

2.67 patients/hr

• To maintain same number of total arrivals

(56) in 1 day, you actually need to extend

scheduled arrivals from 3 pm to 6:30 pm

(not 6 pm as in the case)

(34)

Slow Down Arrivals: Analysis

Vitals + EKG

NP

MD

Blood Work

a

11.3

11.3 Std Dev a

8.9

11.3

2.3

4.5 CVa

0.8

1.0

0.2

0.4 p

10

43

64

6 Std Dev p

3.5

21

29

2 CVp

0.35

0.49

0.45

0.33 m

2

5

8

3 u

0.44

0.76

0.71

0.18 Tq

1.01

11.26

1.09

0.01 What are the main drawbacks?

• Eliminates build-up at RN  only queueing times remain

- waiting time at RN is not zero!

• Reduces queueing waiting times at other steps (lower

arrival rate)

• Total average waiting time is down from about 1 hour to

less than 15 minutes

(35)

Add a Registered Nurse (RN)

• Current RN utilization is 100%, resulting

in inventory buildup, current average

wait time of 40 minutes

• What happens if we add 1 RN?

–

Assume extra RN at both lunch and

non-lunch

–

RN capacity goes from 7 pts/hr to

8.4 pts/hr during non-lunch

–

RN capacity goes from 2.8 pts/hr to

4.2 pts/hr during lunch

–

Both are more than demand, so 1

extra RN eliminates inventory

build-up due to insufficient capacity

(36)

Add an RN: Analysis

• No more inventory build-up at RN, but now MD is bottleneck

• MD capacity is 7.5 patients/hr, only slightly more than RN

• Can expect inventory buildup at MD step

• Even without deterministic inventory buildup at RN, will still have

queueing delays (utilization still high – around 95%)

(37)

Add an Anesthesiologist (MD)

• Current MD utilization is 93%, resulting

in queueing delays due to randomness,

current average wait time of almost 12

minutes

• What happens if we add 1 MD?

–

Focus on non-lunch (9 am – 12 pm)

time used in queueing analysis

above

–

MD staff goes from m = 8 to m = 9

–

Utilization at MD step goes from

93% to 83%

(38)

Add an MD: Analysis

• Waiting time drops to 2.68 minutes (reduction of less than 10

minutes - at most it could be 12 minutes)

• Anesthesiologists are expensive and this is a minimal decrease in

waiting time

(39)

(40)

MGH Wrap-up

Recall our framing thoughts:

• How can operations help deliver health care

more efficiently?

• How can process and queueing analysis

help inform managerial decisions and

strategies?

How process analysis helps?

• Complicated system to analyze

• Tools allow for solid first order

assessment of the problem and impact

of potential actions

• Further analysis and simulation may be

useful to predict impact more accurately

And this is only the tip of the iceberg!

• Queueing analysis has been improving

the design of health care systems in

many aspects to reduce patient waiting

times at minimal costs

(41)

Process Improvement in Health Care

MGH PATA Redesign

Stanford GSB

April 24, 2015

(42)

PATA Follow-Up

Immediate Actions

1. RN and MD assigned to patient at the same time.

–

Gave MD 20 – 25min to do chart review

–

Eliminated wait time between providers

2. RN’s given a “5-minute patient history rule”

3. No batching of intake forms at front desk

4. Only charge nurse allowed to modify the appointment

schedule/tracking sheet

5. Redesigned card RNs filled out in visit

Reduced patient wait time ~20min

Helped with staff morale and set up clinic for long term strategies

RN Visit

MD Visit

(43)

PATA Follow-Up

1. Phone Program for low acuity, low complexity patients

• RNs collect basic info over phone

+

2min assessment on day of surgery

• Triage grid for surgeons: phone vs clinic

+

lab and EKG orders

2. Single-provider model in clinic with Nurse Practitioners (NPs)

• NPs can do both RN Assessment and anesthesia evaluation

3. Predefined schedule: Patients assigned in advance – 90min visit

4. Web-based intake form for patients to complete in advance

5. RFID tracking of all patient and providers in PATA

2h appt = 30min for labs + 90min appt with NPs

Business plan to redesign entire process

Presented to CEO and funded at

$2.6M over 3 years

• See 100% of patients

• Ensure positive patient experience

• Generate high quality evaluations

Long-Term Actions

(44)

MGH-MIT Collaboration: Work to date

Implemented & Results

OR block reallocation

(elective cases)

OR Open Blocks

(non-elective cases)

Inventory management of surgical

supplies - Part I

Ready to start implementation

Intra-day surgical scheduling

Post-surgery Recovery Area patient flow

Cancer Center

Primary Care Redesign – Rx

Management

New projects

Hospital bed capacity

management

(predictions &

decision support)

Primary Care Redesign –

Provider Scheduling

Critical Care

Non-Oncology Infusion

Surgical Observation

Patient Flow

Optimization

Implemented & Results

In Implementation

(45)

Max Prescribed Wait Time

Non Urgent

24 hours

Urgent

4 hours

Emergent

45 minutes

Non-elective cases = wait list cases

Data source: MGH OR case data. Time frame: July 2009 vs May 2010

~30% patients waited more than

recommended wait time

OR Scheduling of Non-Elective Cases: Symptoms

(46)

OR Scheduling of Non-Elective Cases:

Centralized Open Blocks

Objective:

Increase timely access for waitlist cases

Centralization

Jan – Sept 2012

Two extra blocks

became available

October 2012

Before project

~2.4 Reserved ORs owned

by particular services on

weekdays

Simulation Model

• 5 Open Blocks

• ~30% patients  < 3% pts. wait more than

recommended wait time

• Assumes OR is the

only

limiting factor

Block = {Operating Room, Weekday},

7am – 5pm

(47)

Even with 9% increase in overall case volume, the average wait time decreased by 25%

for all non-elective cases.

OR Scheduling of Non-Elective Cases: Results

Data source: MGH OR case data

Time frame: June – Dec 2011 vs June – Dec 2012

Wait time = Booking time  Into OR

OR Type

Prime Time

_Utilization

Elective

87.2%

86.2%

Non-electives

86.1%

80%

OR Type

Avg # Running

_ORs/day

Elective

45.8

47.3 Non-electives

2.4

2.7 79.3%

_75.3%

65.3%

87.7%

_86.2%

70.0%

0% 20% 40% 60% 80% 100%

Non-Urgent

Urgent

Emergent

% Patients had procedure within designated

wait time

(48)

• Work only on top priority burning problems

• Make sure data

is available to identify the problem

• Understand mutual cultures, organization and community

• Leadership engagement to support implementation

• Analyzing impact of implementation and monitoring results

is a big challenge – Very dynamic environment!

(49)

Q & A

(50)

OIT 262 Roadmap: 18 Sessions of “Operations’ Greatest Hits…”

Class Date Topic Overview

1 4/3 Introduction, basic concepts, Little’s Law

Process Analysis

•Types of processes

•Dimensions of process capability •Process flow diagrams

•Capacity, utilization, inventory

•Uncertainty (randomness) in processes •Bottlenecks

2 4/6 Process Analysis 3 4/10 Process Improvement 4 4/13 Randomness and Waiting Times 5 4/17 Optional Review Session

6 4/20 Batching and the Product-Process Matrix 7 4/24 Process Improvement in Health Care 8 4/27 Supply Chain Inventory Management

Process Improvement and Quality Management

•Process in service industries

•Quality control, Total Quality Management •Just-in-time, Lean Manufacturing

•Six Sigma, Statistical Process Control 9 5/1 Quality and Process Improvement in Health Care

10 5/4 Gur’s Research Lecture 11 5/8 Toyota Production System 12 5/11 Measuring and Quantifying Quality 13 5/15 Quality Management in Luxury Services

14 5/18 Supply Chain Simulation Supply Chain Management

•Order fulfillment and distribution •Postponement

•Quick response/dual response

•Information sharing, contracts, and incentives in supply chains •Fast fashion production

15 5/22 Managing Product Variety in Supply Chains 16 5/27 Forecasting Game, Contracts & Incentives 17 5/29 Leadtime Reduction and Quick Response 18 6/1 Student Presentations & Course Summary