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
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
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
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
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
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
Appointment 2:29 2:40 2:54
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
Appointment 1:05 0:46 1:12
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
Appointment 1:40 0:24 2:41
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
Clinic 1 Clinic 2 Clinic 3 Clinic 4
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
Clinic 1 Clinic 2 Clinic 3 Clinic 4
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
Patient Arrival & Service Completion Rates
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 and Service Completion Rates @ Clinic 2
Arrival Rates
Service Rates
28
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 3
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
Appointment-based Patient Arrival & Service Time (Clinic 2)
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
Appointment-based Patient Arrival & Service Time (Clinic 3)
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
Appointment-based Patient Arrival & Service Time (Clinic 4)
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
Appointment-based Patient Service Time Variability for On-Time (Clinic 2)
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
Appointment-based Patient Service Time Variability for On-Time (Clinic 3)
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
Appointment-based Patient Service Time Variability for On-Time (Clinic 4)
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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