LV Prasad Eye Institute Final Presentation · LV Prasad Eye Institute Final Presentation . Ali...

LV Prasad Eye Institute Final Presentation Ali Kamil, Dmitriy Lyan, Nicole Yap, MIT Student MIT Sloan School of Management | Global Health Lab May 8, 2013

Courtesy of Ali S. Kamil, Dmitriy E. Lyan, Nicole Yap, and MIT Student. Used with permission.1

Agenda

• About LVPEI • Opportunity, challenges, and approach • General observations • Analysis and recommendations • Next steps • Appendix

2

LVPEI is a non-profit organization focused on the delivery of eye care to patients at all levels of the economic pyramid.

Services offered:

• Comprehensive patient care • Clinical research • Sight enhancement and

rehabilitation • Community eye health • Education • Product development

LVPEI Eye Health Pyramid Centre of Excellence:

• Provides outpatient services to 200,000 people

• Performs 25,000 surgeries • Trains 250 professionals at all

levels of eye care • Provides low vision services to

3,000 people

Hyderabad Campus

© LV Prasad Eye Institute. All rights reserved. This content is excluded from our CreativeCommons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/.3

http://ocw.mit.edu/help/faq-fair-use/

The GHL team at MIT Sloan was engaged to identify bottlenecks and causes of high patient service time in the LVPEI outpatient department (OPD).

• Reduce service time without compromising LVPEI’s high standard of quality care • Increase capacity without compromising LVPEI’s high standard of quality care

Opportunities

• Engaged Sashi Mohan, Head of Operations, and Raja Narayan, Head of Clinical Services at LVPEI, over Skype

• We interviewed key personnel at hospitals in the Boston area, including operations leaders at Massachusetts General Hospital (MGH) and practitioners at Massachusetts Eye and Ear Infirmary (MEEI) and Mount Auburn Hospital

Team Approach

• Conducted time and motion studies in two cornea and two retina OPD clinics, collecting timestamps on the flow of patient folders, and noting management practices

• Interviewed faculty ophthalmologists and optometrists, and OPD scheduling administrator

• Conducted patient surveys at the walk-in counter

• Ran statistical analyses to quantitatively identify relationships between different variables and patient service times

• Compared findings to our interviews and observations of management practices

• Derived recommendations for addressing systemic causes of increases in patient service times in the OPD

Pre-trip (January to March) On-site (Mid- to late- March) Post-trip (April to May)

• High patient service times • High provider fatigue due to high patient volume and extended hour of service

Challenges

4

GENERAL OBSERVATIONS

5

Ret

ina

Cor

nea

Check-In Work-up Consultation Investigation Check-out

Patient pathways varied significantly depending on the clinic, and on the type of patient and appointment.

Start

Dilation required?

30 minutes

30 minutes

Work-up

Dilation

Cornea Consultation

Dilation

End

No

Yes

No

Yes

Cornea Investigation

Yes

Retina Consultation

Investigation required?

Retina Investigation

Investigations units are subject to their own process flow management practices.

6

Additionally, several combinations of factors impact patient service time.

• Management of patient folders and staff • # of Fellows, Optometrists, and Facilitators • Skill levels of staff • Size and layout of clinics • Anticipated vs.. actual patient volume • Types and variety of patients that can be seen • Need for diagnostics

• Lack of awareness of appointment-based system • Bias for early morning arrival • High volume of late arrivals and no shows

• Doctor-specified appointment and walk-in templates • Administrator's adherence to doctor-specified

appointment templates • Real-time prioritization of patients

• Commitment to training medical staff • Patient volume vs.. hospital capacity

Hospital-Specific Factors

Scheduling-Specific Factors

Clinic-Specific Factors

Patient-Specific Factors

7

0:00

1:12

2:24

3:36

4:48

6:00

7:12

8:24

9:36

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

116

121

126

131

136

141

146

151

156

161

166

171

176

181

186

191

196

201

206

211

216

221

226

231

236

241

246

251

256

261

266

271

Serv

ice T

ime (

h:m

m)

There is little discrepancy in patient service time between non-paying and general patients, but high variability ranging from two to four hours.

Priority

Level Patient

Count Clinic 1 Clinic 2 Clinic 3 Clinic 4 Average

Service Time

G 182 2:44 4:08 2:17 3:26 3:12

NP 56 2:55 3:35 2:51 4:01 3:29

Average patient service times for general and non-

paying patients differed by only 17

minutes.

SD (σ) – 1h 37m Mean (μ) – 3h 15m

μ

σ

-σ

Service Time Variability (All Days)

8

Walk-in patients have higher variability in service times compared to patients with appointments.

1:00

2:12

3:24

4:36

5:48

7:00

8:12

9:24

10:36

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

SD (σ) – 2h 3m Mean (μ) – 5h 4m

μ

σ

-σ

Clinic 1 Clinic 2 Clinic 3 Clinic 4 Average

Service Time

Total

Patient

Count

6:21 5:01 4:38 5:04 5:04 60

4 28 8 20 7

16

11 10

8 8

2 1

2

0 1

0 02468

1012141618

Walk-in patient service time is higher than apt-

based patients 86% of walk-ins arrive before 12pm

Walk-in Patient Service Time Variability (all days)

Walk-in Patient Arrival Time vs.. Service Time

9

ANALYSIS &

RECOMMENDATIONS

10

3 6 6 21 23 19 10 3 2

2:06

2:50

3:35 3:31 3:38

4:10

3:10

2:44 2:23

0

5

10

15

20

25

>4hoursearly

3 hoursearly

2 hoursearly

1 hourearly

On-time30min

window

1 hourlate

2 hourslate

3 hourslate

4>hourslate

0:000:280:571:261:552:242:523:213:504:194:48

Require doctors to adhere to appointment based system and encourage on-time arrivals.

Service Times for On-time Patients

Clinic 1 Clinic 2 Clinic 3 Clinic 4

Avg. Service Time 2h 22 m 3h 58m 2h 4m 3h 38m

St. Dev 1h 17 m 1h 33m 1h 22m 1h 23m

1 0 17 12 10 5 5 0 2

3:52

0:00

3:47

3:09

2:22

3:21

2:27

0:00

2:06

0

2

4

6

8

10

12

14

16

18

0:00

0:28

0:57

1:26

1:55

2:24

2:52

3:21

3:50

4:19

Arrival Time Service Time

Clinic 4

Clinic 1 Key Observations

• Only 28% of all apt. based patients arrived on time • Clinics adhering to apt. based system achieved shorter

service times • Clinics 1 and 3 adhered to apt. based system and penalized

patients for early (<30m) or late arrivals (>30m) • Clinics 2 and 4 did not actively adhere to apt. system and

had significantly higher service times for on-time patients

Recommendations

• Require doctors to adhere to apt. based system • Prioritize patients based on their appointment times and

not check-in times • Educate patients about apt. based system and

encourage adherence

11

Encourage the use of appointment system, while simultaneously employing strategies to better manage walk-in patients.

Walk-in Survey Results Summary

40 patients surveyed in total

• 41% of patients had tried unsuccessfully to make an appointment; 50% of these were because the requested appointment time was unavailable

• 80% of patients who did not make appointments were unaware of the option

Survey Findings

• In general, awareness of the appointment option is low

• Patients choose the walk-in option because the next available appointment is too far away

• The majority of walk-in patients are new to LVPEI

Interview Findings

• Doctor scheduling for walk-in patients by time and type is often not adhered to due to over demand and incorrect triage

• Unexpected walk-ins are disruptive to the patient flow, but doctors have no choice but to accommodate

• Incorrect triage results in re-routing patients to other clinics and increased service time • Walk-in patients often have primary care concerns that do not require specialized attention, or ask to

see a specific doctor unnecessarily

Ideas to Consider

• Better promote appointment system, especially among new patients • Designate general doctors for walk-in clinic to reduce specialist time on general cases • Require referral letters for new patients asking to see a specific doctor • Enforce ophthalmologist-set guidelines for appointment booking at the walk-in counter

12

Identify factors contributing to decreasing service times in the late afternoon.

Key Observations

• Average service time decreases with time of day • Appointment-based patients arrival time has normal

distribution

3

16

26 31

36

28 32 33 33

19

6 1

2:45

3:52 3:36

3:23 3:29

3:57

3:01

3:28

2:35 2:19

2:06

1:34

0:000:280:571:261:552:242:523:213:504:19

05

10152025303540

# o

f P

ati

en

ts

Apt. Based Patients Arrivals & Service Times (all clinics)

Average Service Time (8am – 1pm) – 3h 30m Average Service Time (1pm – 7pm) – 2h 30m

Potential Factors to Consider

• Providers work more efficiently towards the end of the day • Patients that do not require diagnostics are stacked later in

the day • Reduced number of walk-ins in the latter half of the day

Closely observe the behavioral patterns of providers during the later half of the day. If positive behavior is identified, this practice should be replicated during the rest of the day.

Ideas to Consider

13

Monitor practitioner fatigue in latter half of workday, as high pressure to serve customers can lead to increased errors and reduced service quality.

Interview Findings

• Error rate of providers rises throughout the day for both optometrists and ophthalmologists

• After 4:00PM, doctors begin to observe fatigue in their teams

• After 4:00PM, doctors begin to observe work being completed in a hurry

Ideas to Consider

• Closely observe the error rate that is created at any given time

• Closely observe the frequency of re-work over a given time period

• Determine the cause of the decline in service times during latter half of the day

Key Observations

• Patients who arrive later in the day and patients who arrive significantly late for their appointments tend to experience lower service times.

• Average service time decreases with time of day • With time of day, providers and staff tend to get fatigue

and are prone to mistakes/errors

1:00

1:29

1:58

2:26

2:55

3:24

3:53

4:22Apt. Based Patients Arrivals & Service Times (all clinics)

Time of Day 1pm 4pm 10am

Time per Patient

Service Pressure vs.. Time per Patient

14


Insights

• Workday is scheduled for 8am – 5:30pm. Providers observed working until 7/8pm to service all patients

• High patient backlog increases pressure on LVPEI providers to service all patients in a given day

• Latter part of the day has been observed (via interviews) to increased fatigue and errors in service

• High pressure situation coupled with long workdays will lead to high turnover of staff

Ideas to Consider

• Adherence to apt. based system and reducing number of walk-in patients

• Consider provider/staff rotation between high-pressure clinics and regular clinics

• Identify rework and errors created by time of day

Modeling Next Steps

• Consider long term impact to quality of service and reputation due to high service times and errors/rework

• Identify impacts to staffing and turnover due to high pressure environment

• Consider competitor/alternate emergence scenario

Actual Service TimeRequired Service Time

Target Wait TimePer Patient

-

Service Pressure

Workday

-

-

Time Per Patient

-

LVPEI PatientBacklog

+

Patient Arrival Rate Patient Check-out Rate

+

-

Current Wait Time PerPatient based on

Backlog

Total LVPEI Staff

+

-

B

Pressure Buildup

B

Reduced Time Per Task

R

Errors Created

Time Remainingin Workday

-

Walkin PatientsApt. Based Patients

+

+

Perception ofLong Wait Times

at LVPEIRate of Change inPerception about Wait

Times

-

Error Fraction-

+

+

Perception Buildup Time-

Standard Workday

+

Avg. Workday forPast Month

Burnout Rate

Fatigue Buildup time

<Workday>

Impact of Fatigueon Error Fraction

+

+

Fatigue/Burnout

Impact of Fatigue onCross Consultations

+

+

15

Identify and encourage best cross-consultation management practices

Relevant Clinic Observations

• Cross-consultation cases comprise a non-negligible percentage in each clinic: 10 to 15%

• 3 out of 4 clinics employed practices to manage and integrate cross-consultation cases into existing patient flow

• Management of cross-consultation cases differed across clinics • Passive cross-consultation management was disruptive to regular patient flow

Ideas to Consider

• Conscious management of cross-consultation patients in each clinic • Identification of good cross-consultation management practices • Closer observation of the decision-making process behind the need for cross-consultation • Guidelines for providers on the necessity for cross-consultation

Sample Cross-Consultation Management Practices

• Fixed time allocation: 15 minutes every 2 hours for cross-consultations and short follow-ups • Real-time prioritization: integration and prioritization of cross-consultations with existing patients • Prioritization by check-in: prioritization of cross-consultation patients according to check-in time

16

Remove annual post-surgery follow-up requirement and divert patients to comprehensive clinic for ongoing long follow-ups

• 60-70% of doctor’s appointment templates are dedicated to seeing new patients • 20% of all patients seen across the four days of study are new patients. • Providers perform over 500 surgeries a year • All patients are requested to come back for follow-ups at least once a year regardless of the need.

• Continuing with the policy of requiring patients to come back for simple follow ups exhausts LVPEI doctors’

capacity to serve new patients. • Dedicating more of providers’ time to follow up patients

reduces opportunities to learn from diverse and complex cases.

• Ongoing reduction in time available to see new patients limits LVPEI’s ability to realize its vision to reach all

those in need.

• Removing the requirement for all patients to come in for yearly follow-ups post-surgery

• Transitioning fully recovered patients to comprehensive clinic for ongoing long follow-ups

LVPEI OPDPatientBacklog Patient

ServiceRt.

LVPEINon-Surgical

Patients

Surgical CaseFraction

B

Hotel California

AdoptionRate

NS PatientDep Rate

Surgery Rate

+ LVPEI PostSurgicalPatients

SG PatientDep Rate

Follow Up PatientArrival Rt

+

TotalAddressable

Market

New PatientAppointment Slots

Follow UpAppointments

-

+

New PatientArrival Rate

+

+

Observations

Insights

Ideas to Consider

17

NEXT STEPS

18

Additional studies and modeling exercises will build a comprehensive understanding of the factors contributing to patient service time in the OPD.

• Time and motion studies that include cross consultation patients • Time and motion studies on cornea diagnostics • Patient flow of patients before they get to clinics • Triage process at the walk-in counter • Patients returning to LVPEI due to incorrect diagnosis • Effectiveness of short-term recommendations • Identification of best practices in clinic management

Future Studies

• Additional data collection needed to quantify key relationships • LVPEI’s patient flow system for cornea and retina clinics

Simulation Models

19

QUESTIONS?

20

APPENDIX

21


System Dynamics Service Pressure Loop

Actual Service TimeRequired Service Time

Target Wait TimePer Patient

-

Service Pressure

Workday

-

-

Time Per Patient

-

LVPEI PatientBacklog

+

Patient Arrival Rate Patient Check-out Rate

+

-

Current Wait Time PerPatient based on

Backlog

Total LVPEI Staff

+

-

B

Pressure Buildup

B

Reduced Time Per Task

R

Errors Created

Time Remainingin Workday

-

Walkin PatientsApt. Based Patients

+

+

Perception ofLong Wait Times

at LVPEIRate of Change inPerception about Wait

Times

-

Error Fraction-

+

+

Perception Buildup Time-

Standard Workday

+

Avg. Workday forPast Month

Burnout Rate

Fatigue Buildup time

<Workday>

Impact of Fatigueon Error Fraction

+

+

Fatigue/Burnout

Impact of Fatigue onCross Consultations

+

+

22

Summary Of Approximate Patient Waiting Times

Day Type Workup Waiting Time

Diagnostic Waiting Time

Total Service Waiting Time

Number Of Patients

Percentage of Walkins

Percentage of New Patients

Clinic 1

Appointment 1:28 2:33 2:04

57 7% 14% Walk In 1:47 3:21 5:16

New 1:54 3:42 3:55

Follow Up 1:26 2:19 2:01

Clinic 2


97 30% 26% Walk In 3:16 1:31 3:42

New 2:27 2:37 4:02

Follow Up 3:32 1:59 2:47

Clinic 3


72 11% 14% Walk In 4:07 3:32

New 1:57 2:07 3:45

Follow Up 1:29 1:39 0:36

Clinic4


113 18% 22% Walk In 2:37 0:52 4:03

New 3:45 2:09 3:16

Follow Up 2:57 0:18 3:37

23

Overall Patient Average Service Time

21:36

22:48

0:00

1:12

2:24

3:36

4:48

6:00


Serv

ice T

ime

AppointmentsWalk InsNewFollow Ups

24

Overall Patient Check-in to Dilation Average Service Time

0:00

0:28

0:57

1:26

1:55

2:24

2:52

3:21

3:50

4:19

4:48


Serv

ice T

ime

AppointmentsWalk InsNewFollow Ups

25

Overall Patient Arrival Rates

0

2

4

6

8

10

12

14

16

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00

Clinic 1Clinic 2Clinic 3Clinic 4

26

Patient Arrival & Service Completion Rates

0

2

4

6

8

10

12

14

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00

Pati

en

ts/H

ou

r

Arrival and Service Completion Rates @ Clinic 1

Arrival Rates

Service Rates

27


0

5

10

15

20

25

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00

Pati

en

ts/H

ou

r


Arrival Rates

Service Rates

28


0

2

4

6

8

10

12

14

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00

Pati

en

ts/H

ou

r


Arrival Rate

Service Rate

29

Appointment-based Patient Patient Type (G,NP, S, SS) & Service Time

182

56

31

5

3:12

3:29

3:02

3:24

0

20

40

60

80

100

120

140

160

180

200

2:45

2:52

3:00

3:07

3:14

3:21

3:28

3:36

G NP S SS

Nu

mb

er

of

Pati

en

ts

Serv

ice T

ime

Patient Types Service Times

30

Appointment-based Patient Distribution Early/Late/On-Time Arrivals

Early Arrivals

46%

Late Arrivals

26%

On Time 28%

NA 0%

Arrivals (Total)

Early Arrival

s 58%

Late Arrival

s 23%

On Time 19%

NA 0%

Clinic 1

Early Arrivals

55%

Late Arrivals

15%

On Time 30%

NA 0%

Clinic 2

Early Arrivals

40%

Late Arrivals

25%

On Time 35%

NA 0%

Clinic 3

Early Arrivals

39%

Late Arrivals

36%

On Time 25%

NA 0%

Clinic 4

31

Appointment-based Patient Arrival & Service Time (Clinic 1)

1

0

17

12

10

5 5

0

2

3:52

0:00

3:47

3:09

2:10

3:21

2:27

0:00

2:06

0

2

4

6

8

10

12

14

16

18

>4 hoursearly

3 hours early2 hours early 1 hour early On-time30min

window

1 hour late 2 hours late 3 hours late 4> hours late0:00

0:28

0:57

1:26

1:55

2:24

2:52

3:21

3:50

4:19

Pati

en

t #

Serv

ice T

ime

32


6 6

10

14

20

7

2

0

1

5:05

3:58

3:27

4:05 3:58 4:07

1:50

0:00

3:31

0

5

10

15

20

25

>4 hoursearly

3 hoursearly

2 hoursearly

1 hourearly

On-time30min

window

1 hour late 2 hourslate

3 hourslate

4> hourslate

0:00

1:12

2:24

3:36

4:48

6:00

Pati

en

t #

Serv

ice T

ime

33


0 0

6

19

22

8

5

1

2

0:00 0:00

2:35 2:50

2:04

1:31

1:57

5:00

1:17

0

5

10

15

20

25

>4 hoursearly

3 hoursearly

2 hoursearly

1 hour early On-time30min

window

1 hour late 2 hours late 3 hours late 4> hourslate

0:00

1:12

2:24

3:36

4:48

6:00

Pati

en

t #

Serv

ice T

ime

34


3

6 6

21

23

19

10

3

2

2:06

2:50

3:35 3:31 3:38

4:10

3:10

2:44

2:23

0

5

10

15

20

25

>4 hoursearly

3 hoursearly

2 hoursearly

1 hourearly

On-time30min

window

1 hour late 2 hourslate

3 hourslate

4> hourslate

0:00

0:28

0:57

1:26

1:55

2:24

2:52

3:21

3:50

4:19

4:48

Pati

en

ts #

Serv

ice T

ime

35

Walk-in Patient Arrival & Service Time

7 16 11 10 8 8 2 1 2 0 1 0

6:17

7:35

9:25

4:10 4:22

4:05

6:14

0:00

2:37

0:00

2:32

0:00

0

2

4

6

8

10

12

14

16

18

0:00

1:12

2:24

3:36

4:48

6:00

7:12

8:24

9:36

10:48

Pa

tie

nt

#

Se

rvic

e T

ime

Walkin Arrival Time vs Srvc Time Service Time

36

Appointment-based Patient Arrival vs. Appointment Time Variability

0 50 100 150 200 250 300 350

(0.40)

(0.30)

(0.20)

(0.10)

-

0.10

0.20

0.30

0.40

0.50

Dif

fere

nce b

/w C

heck

-in

an

d A

pt

Tim

es

Day 1 Day 2 Day 3 Day 4

Early

Late

37

Appointment-based Patient Service Time Variability for On-Time (Clinic 1)

0:00

1:12

2:24

3:36

4:48

6:00

9:38 9:54 10:47 11:29 11:29 12:01 12:28 13:11 13:11 14:43

Serv

ice T

ime (

h:m

m)

38


0:00

1:12

2:24

3:36

4:48

6:00

7:12

8:24

9:36

7:40 8:04 8:10 8:54 9:18 9:05 9:48 10:12 10:21 10:38 11:10 11:20 12:41 13:15 13:14 13:12 13:52 14:05 14:35 16:02

Serv

ice T

ime (

h:m

m)

39


0:00

1:12

2:24

3:36

4:48

6:00

9:22 10:0710:4210:4810:4910:4911:1011:2711:1011:3811:3612:0012:1713:2514:1014:0514:1815:0115:2115:4416:0016:07

Serv

ice T

ime (

h:m

m)

40


0:00

1:12

2:24

3:36

4:48

6:00

Serv

ice T

ime (

h:m

m)

41

Ali Kamil is a System Design & Management Fellow at MIT Sloan and MIT School of Engineering. Prior to MIT, he spent 6 years in corporate strategy consulting advising leading entertainment, media, and telecom clients.

His research and interests are focused on developing low-cost ICT based innovations for base of pyramid populations in developing and emerging economies. Originally from Pakistan, Ali intends to pursue a career in international development and social entrepreneurship post MIT

Team Profiles

Nicole Yap is an MSc in Management Studies student at MIT Sloan. She has two years of consulting experience, advising large private and public sector clients on their Customer Relationship Management (CRM) strategies.

Her research focuses on the development of market-based policies and approaches that organizations can apply to sustainably reach developing markets. Nicole plans to apply her management consulting background to the development of sustainable global health strategy upon graduation in 2013.

Dmitriy Lyan is a second year SDM student at MIT, where he is specializing in development of performance management systems for shared value focused organizations. In his thesis work he is using system dynamics methodology to explore

performance dynamics in US military behavioral health clinics. Prior to MIT Dmitriy spent 5 years working in investment banking and asset management as well as 2 years in software development industries. He holds an M.S. in Financial Engineering and a B.S. in Computer Engineering. He plans to apply his talents in impact investing and social entrepreneurship.

MIT Student is an MSc in Management Studies student at MIT Sloan. Prior to MIT, she spent 5 years in the financial service industry, marketing multi-asset investment solutions to institutional clients. After graduation, MIT Student hopes to employ management skills to disseminate innovative and affordable interventions designed to empower marginalized individuals in sub-Saharan Africa.

42

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