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
1
Courtesy of Ali S. Kamil, Dmitriy E. Lyan, Nicole Yap, and MIT Student.Used with permission.
Agenda
•
•
•
•
•
•
2
About LVPEI
Opportunity, challenges, and approach
General observations
Analysis and recommendations
Next steps
Appendix
LVPEI is a non-profit organization focused on the delivery of eye
care to patients at all levels of the economic pyramid.
Hyderabad Campus
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
© LV Prasad Eye Institute. All rights reserved. This content is excluded from our Creative
Commons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/
3
Services offered:
• Comprehensive patient care
• Clinical research
• Sight enhancement and
rehabilitation
• Community eye health
• Education
• Product development
LVPEI Eye Health Pyramid
The GHL team at MIT Sloan was engaged to identify bottlenecks
and causes of high patient service time in the LVPEI outpatient
department (OPD).
Challenges
•
•
High patient service times
High provider fatigue due to high patient volume and extended hour of service
Opportunities
•
•
Reduce service time without compromising LVPEI’s high standard of quality care
Increase capacity without compromising LVPEI’s high standard of quality care
Team Approach
4
Pre-trip (January to March)
On-site (Mid- to late- March)
• 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
• 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
Post-trip (April to May)
• 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
GENERAL OBSERVATIONS
5
Patient pathways varied significantly depending on the clinic,
and on the type of patient and appointment.
Consultation
Work-up
Retina
Check-In
Retina
Consultation
Dilation
30 minutes
Yes
No
Dilation
required?
Yes
Dilation
30 minutes
Cornea
Consultation
Check-out
Investigations units
are subject to their
own process flow
management
practices.
Investigation
required?
No
Start
Cornea
Retina
Investigation
Yes
Work-up
6
Investigation
Cornea
Investigation
End
Additionally, several combinations of factors impact patient
service time.
Hospital-Specific Factors
• Commitment to training medical staff
• Patient volume vs.. hospital capacity
Scheduling-Specific Factors
• Doctor-specified appointment and walk-in templates
• Administrator's adherence to doctor-specified
appointment templates
• Real-time prioritization of patients
Clinic-Specific Factors
• 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
Patient-Specific Factors
• Lack of awareness of appointment-based system
• Bias for early morning arrival
• High volume of late arrivals and no shows
7
There is little discrepancy in patient service time between nonpaying and general patients, but high variability ranging from two
to four hours.
Priority
Level
G
NP
Patient
Count
Clinic 1
Clinic 2
Clinic 3
Clinic 4
Average
Service Time
182
2:44
4:08
2:17
3:26
3:12
56
2:55
3:35
2:51
4:01
3:29
Average patient
service times for
general and nonpaying patients
differed by only 17
minutes.
Service Time Variability (All Days)
9:36
Service Time (h:mm)
8:24
Mean (μ) – 3h 15m
SD (σ) – 1h 37m
7:12
6:00
σ
4:48
μ
3:36
2:24
-σ
0:00
8
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
1:12
Walk-in patients have higher variability in service times compared to
patients with appointments.
Clinic 1 Clinic 2 Clinic 3 Clinic 4
6:21
4
5:01
28
4:38
8
5:04
20
Walk-in patient service
time is higher than aptbased patients
Average
Service Time
Total
Patient
Count
5:04
60
86% of walk-ins
arrive before 12pm
Walk-in Patient Arrival Time vs.. Service Time
18
16
14
12
10
8
6
4
2
0
16
11
10
7
8
8
2
1
2
0
1
Walk-in Patient Service Time Variability (all days)
10:36
Mean (μ) – 5h 4m
SD (σ) – 2h 3m
9:24
8:12
σ
7:00
5:48
μ
4:36
-σ
3:24
2:12
1:00
9
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
0
ANALYSIS &
RECOMMENDATIONS
10
Require doctors to adhere to appointment based system
and encourage on-time arrivals.
18
Key Observations
16
• 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
Clinic 1
Clinic 2
Clinic 3
Clinic 4
2h 22 m
3h 58m
2h 4m
3h 38m
1h 17 m
1h 33m
1h 22m
1h 23m
• 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
2:52
10
2:24
2:27
2:22
2:06
8
1:55
6
1:26
4
0:57
1
0
17
12
10
5
5
Arrival Time
Service Time
4:10
3:35
3:38
3:31
3:10
15
2:50
2:06
5
3
6
2
0:00
20
0
0
0:00
10
Recommendations
3:50
3:21
3:21
3:09
25
Service Times for On-time Patients
Clinic 1
3:47
12
0
St. Dev
3:52
14
2
Avg. Service Time
4:19
6
21
23
19
10
0:28
0:00
4:48
4:19
Clinic 4 3:50
3:21
2:52
2:44
2:23 2:24
1:55
1:26
0:57
0:28
3
2
0:00
>4 3 hours 2 hours 1 hour On-time 1 hour 2 hours 3 hours 4>
hours early early early 30min late
late
late
hours
early
window
late
Encourage the use of appointment system, while simultaneously
employing strategies to better manage walk-in patients.
Walk-in Survey Results Summary
Survey Findings
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
• 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
12
•
•
•
•
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
Identify factors contributing to decreasing service times in
the late afternoon.
Apt. Based Patients Arrivals & Service Times (all clinics)
40
# of Patients
35
3:57
3:52
3:36
30
25
3:28
3:01
2:45
2:35
20
15
26
10
31
36
16
5
0
3:29
3:23
28
32
33
2:19
2:06
33
19
6
3
Average Service Time (8am – 1pm) – 3h 30m
Key Observations
• Average service time decreases with time of day
• Appointment-based patients arrival time has normal
distribution
4:19
3:50
3:21
2:52
2:24
1:55
1:34 1:26
0:57
0:28
1
0:00
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
Ideas to Consider
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.
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
4:22
Apt. Based Patients Arrivals & Service Times (all clinics)
3:53
3:24
2:55
2:26
1:58
1:29
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
Service Pressure vs.. Time per Patient
Time per Patient
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
14
10am
1pm
Time of Day
4pm
Monitor practitioner fatigue in latter half of workday, as high
pressure to serve customers can lead to increased errors
and reduced service quality.
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
Perception of
Long Wait Times
at LVPEI
Rate of Change in
Perception about Wait
Times
+
Perception Buildup Time
Walkin Patients
Apt. Based Patients
+
LVPEI Patient
Backlog
+
Patient Arrival Rate
Target Wait Time
Per Patient
Pressure Buildup
Actual Service Time
+
-
+
-
Current Wait Time Per
Patient based on
Backlog
+
-
Standard Workday
Time Remaining
in Workday
-
Total LVPEI Staff
B
+
Required Service Time
-
Patient Check-out Rate
+
+
Workday
R
B
Service Pressure
+
Impact of Fatigue on
Cross Consultations
Errors Created
Error Fraction
+
Reduced Time Per Task
-
Impact of Fatigue
on Error Fraction
Time Per Patient
Fatigue/Burnout
<Workday>
Burnout Rate
+
Avg. Workday for
Past Month
Fatigue Buildup time
Ideas to Consider
• Adherence to apt. based system and reducing
number of walk-in patients
• Consider provider/staff rotation between highpressure clinics and regular clinics
• Identify rework and errors created by time of day
15
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
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
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
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
Remove annual post-surgery follow-up requirement and divert
patients to comprehensive clinic for ongoing long follow-ups
Observations
•
•
•
•
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.
Insights
Surgical Case
Fraction
NS Patient
Dep Rate
Total
Addressable
Market
Adoption
Rate
LVPEI
Non-Surgical
Patients
+
Surgery Rate
B
Hotel California
+
New Patient
Arrival Rate
+
LVPEI OPD
Patient
Backlog
Patient
Service
Rt.
Follow Up Patient
Arrival Rt
17
-
Follow Up
Appointments
• 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.
Ideas to Consider
• 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
+
New Patient
Appointment Slots
LVPEI Post
Surgical
Patients
SG Patient
Dep Rate
+
NEXT STEPS
18
Additional studies and modeling exercises will build a
comprehensive understanding of the factors contributing to
patient service time in the OPD.
Future Studies
•
•
•
•
•
•
•
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
Simulation Models
• Additional data collection needed to quantify key relationships
• LVPEI’s patient flow system for cornea and retina clinics
19
QUESTIONS?
20
APPENDIX
21
Monitor practitioner fatigue in latter half of workday, as high
pressure to serve customers can lead to increased errors
and reduced service quality.
System Dynamics Service Pressure Loop
Perception of
Long Wait Times
at LVPEI
Walkin Patients
Rate of Change in
Perception about Wait
Times
+
Perception Buildup Time
Apt. Based Patients
+
LVPEI Patient
Backlog
+
Patient Arrival Rate
Pressure Buildup
+
Actual Service Time
+
-
Required Service Time
-
+
-
Current Wait Time Per
Patient based on
Backlog
+
-
Standard Workday
Time Remaining
in Workday
-
Total LVPEI Staff
B
Target Wait Time
Per Patient
Patient Check-out Rate
+
+
Workday
R
B
Service Pressure
+
Impact of Fatigue on
Cross Consultations
Errors Created
Error Fraction
+
Reduced Time Per Task
-
Impact of Fatigue
on Error Fraction
Time Per Patient
<Workday>
Fatigue/Burnout
Burnout Rate
22
Fatigue Buildup time
+
Avg. Workday for
Past Month
Summary Of Approximate Patient
Waiting Times
Day
Type
Appointment
Clinic 1
Walk In
New
Follow Up
Appointment
Clinic 2
Walk In
New
Follow Up
Appointment
Clinic 3
Walk In
New
Follow Up
Appointment
Clinic4
23
Walk In
New
Follow Up
Workup Waiting Diagnostic Total Service
Time
Waiting Time Waiting Time
1:28
1:47
1:54
1:26
2:33
3:21
3:42
2:19
2:04
5:16
3:55
2:01
2:29
3:16
2:27
3:32
2:40
1:31
2:37
1:59
2:54
3:42
4:02
2:47
1:05
4:07
1:57
1:29
0:46
2:07
1:39
1:12
3:32
3:45
0:36
1:40
2:37
3:45
2:57
0:24
0:52
2:09
0:18
2:41
4:03
3:16
3:37
Number Of
Patients
Percentage
of Walkins
Percentage
of New
Patients
57
7%
14%
97
30%
26%
72
11%
14%
113
18%
22%
Overall Patient
Average Service Time
6:00
4:48
Service Time
3:36
Appointments
Walk Ins
New
Follow Ups
2:24
1:12
0:00
22:48
21:36
Clinic 1
24
Clinic 2
Clinic 3
Clinic 4
Overall Patient
Check-in to Dilation Average Service Time
4:48
4:19
Service Time
3:50
3:21
2:52
Appointments
Walk Ins
New
Follow Ups
2:24
1:55
1:26
0:57
0:28
0:00
25
Clinic 1
Clinic 2
Clinic 3
Clinic 4
Overall Patient
Arrival Rates
16
14
12
10
Clinic 1
Clinic 2
Clinic 3
Clinic 4
8
6
4
2
0
26
8:00
9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00
Patient Arrival & Service
Completion Rates
Arrival and Service Completion Rates
@ Clinic 1
14
12
Patients/Hour
10
8
Arrival Rates
Service Rates
6
4
2
0
27
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
Patient Arrival & Service
Completion Rates
Arrival and Service Completion Rates @ Clinic 2
25
Patients/Hour
20
15
Arrival Rates
Service Rates
10
5
0
28
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
Patient Arrival & Service
Completion Rates
Arrival and Service Completion Rates @ Clinic 3
14
12
Patients/Hour
10
8
Arrival Rate
Service Rate
6
4
2
0
29
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
Appointment-based Patient
Patient Type (G,NP, S, SS) & Service Time
200
182
180
3:29
3:28
3:24
Service Time
3:21
140
120
3:14
3:12
100
3:07
80
3:02
56
3:00
60
40
31
2:52
5
2:45
G
NP
Patient Types
30
160
S
Service Times
SS
20
0
Number of Patients
3:36
Appointment-based Patient
Distribution Early/Late/On-Time Arrivals
Arrivals (Total)
On
Time
28%
NA
0%
Early
Arrivals
46%
Late
Arrivals
26%
Clinic 2
Clinic 1
Late
Arrival
s
23%
31
Early
Arrival
s
58%
NA
0%
NA
0%
NA
0%
On
Time
19%
Clinic 3
On
Time
30%
Late
Arrivals
15%
Early
Arrivals
55%
Early
Arrivals
40%
On
Time
35%
Late
Arrivals
25%
Clinic 4
NA
0%
On
Time
25%
Late
Arrivals
36%
Early
Arrivals
39%
Appointment-based Patient
Arrival & Service Time (Clinic 1)
18
4:19
17
3:52
16
3:50
3:47
14
3:21
3:21
3:09
12
2:52
10
10
2:27
2:24
2:10
2:06
8
6
5
1:26
5
4
2
0
32
1:55
0:57
2
1
0
>4 hours
early
0:00
3 hours early2 hours early 1 hour early
0
On-time
30min
window
0:00
1 hour late 2 hours late 3 hours late 4> hours late
0:28
0:00
Service Time
Patient #
12
Appointment-based Patient
Arrival & Service Time (Clinic 2)
25
6:00
20
20
4:05
3:58
15
Patient #
3:27
4:48
4:07
3:58
3:31
14
10
10
2:24
1:50
7
6
3:36
6
5
1:12
2
1
0
33
0
>4 hours
early
3 hours
early
2 hours
early
1 hour
early
On-time 1 hour late
30min
window
2 hours
late
0:00
3 hours
late
4> hours
late
0:00
Service Time
5:05
Appointment-based Patient
Arrival & Service Time (Clinic 3)
25
6:00
22
5:00
4:48
19
3:36
Patient #
15
2:50
2:35
10
2:24
2:04
6
8
1:57
1:31
5
5
1:17
1:12
2
1
0
34
0
0:00
>4 hours
early
0
0:00
3 hours
early
2 hours
early
1 hour early
On-time
30min
window
1 hour late 2 hours late 3 hours late 4> hours
late
0:00
Service Time
20
Appointment-based Patient
Arrival & Service Time (Clinic 4)
25
4:48
23
20
3:35
4:19
4:10
21
3:31
3:38
3:50
19
3:21
15
Patients #
2:50
2:52
2:44
2:23
2:06
10
10
6
1:55
1:26
6
5
0:57
3
3
2
0
35
2:24
>4 hours
early
3 hours
early
2 hours
early
1 hour
early
On-time 1 hour late
30min
window
2 hours
late
3 hours
late
4> hours
late
0:28
0:00
Service Time
3:10
Walk-in Patient
Arrival & Service Time
10:48
18
9:25
9:36
8:24
16
14
7:35
12
6:17
6:14
6:00
10
4:48
4:10
4:22
8
4:05
3:36
6
2:37
2:24
4
1:12
2
0:00
7
16
11
10
8
8
2
Walkin Arrival Time vs Srvc Time
36
2:32
0:00
1
2
Service Time
0:00
0
1
0:00
0
0
Patient #
Service Time
7:12
Appointment-based Patient
Arrival vs. Appointment Time Variability
0.50
0.40
Early
Difference b/w Check-in and Apt Times
0.30
0.20
0.10
-
0
50
100
150
200
(0.10)
(0.20)
Late
(0.30)
(0.40)
37
Day 1
Day 2
Day 3
Day 4
250
300
350
Appointment-based Patient
Service Time Variability for On-Time (Clinic 1)
6:00
Service Time (h:mm)
4:48
3:36
2:24
1:12
0:00
38
9:38
9:54
10:47
11:29
11:29
12:01
12:28
13:11
13:11
14:43
Appointment-based Patient
Service Time Variability for On-Time (Clinic 2)
9:36
8:24
Service Time (h:mm)
7:12
6:00
4:48
3:36
2:24
1:12
0:00
39
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
Appointment-based Patient
Service Time Variability for On-Time (Clinic 3)
6:00
Service Time (h:mm)
4:48
3:36
2:24
1:12
0:00
40
9:22 10:07 10:42 10:48 10:49 10:49 11:10 11:27 11:10 11:38 11:36 12:00 12:17 13:25 14:10 14:05 14:18 15:01 15:21 15:44 16:00 16:07
Appointment-based Patient
Service Time Variability for On-Time (Clinic 4)
6:00
Service Time (h:mm)
4:48
3:36
2:24
1:12
0:00
41
Team Profiles
42
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
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.
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.
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 marketbased 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.
MIT OpenCourseWare
http://ocw.mit.edu
15.S07 GlobalHealth Lab
Spring 2013
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