4. Rwanda – Country Overview
Population2010 10.62M
Size – Land Mass 26.3 M KM2
10.15M Mi2
Gross National Income $5.54B
(GNI) 2010
GNI Per Capita (PPP $1,110
International $) 2009
Health Care Spending Per $102
Capita2009 (PPP International $)
Health Care spending as % 9%
of GDP
Life Expectancy(m/f) 57/60
Poverty Rate 56.9%
Source: WHO (http://www.who.int/countries/rwa/en/index.html), World Bank Country Report
(http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/AFRICAEXT/RWANDAEXTN/0,,menuPK:368741~pagePK:141132~piPK:1411
09~theSitePK:368651,00.html) 3
5. Health care delivery in Rwanda utilizes
progressive specialization
Community Health Health Centers
Workers • Level of Care: Nurses
• Level of Care: Volunteers • Area Served: Sector
• Area Served: Village
Referral Hospitals District Hospitals
• Level of Care: Specialists • Level of Care: Doctors
• Area Served: Nationwide • Area Served: District
4
6. Overview of Ruli District Hospital
Health Centers 7 – In District
Served 6 - 8 – Out of District
Catchment Size 94,0931
Departments Outpatient Consultation
Emergency Services
In-patient hospitalization
Maternity
Ophthalmology
Dentistry
Mental Health
Patients per year ~9,000 Outpatient
Consultations
Source: 1) Bossart,et al “Solving Ruli District Hospital’s Referral System Challenges” March 2011 5
7. Project Overview
Project Goals, Objectives and
Methodology
Background Project Overview Assessment Recommendations Next Steps
8. Goal to increase efficiency at Hospital
and Health Center through data usage
Hospital
• Improve operational efficiency of administrative and support staff
• Increase availability and use of patient referral and appointment data
• Provide technology solutions for manual processes to ease impact of Ministry of
Health and other reporting requirements
• Reorganize administrative responsibilities to maintain consistency of patient
interactions
Health Centers
• Eliminate duplicative and ineffective processes
Patients
• Improve quality of care
• Reduce wait times
7
Background Project Overview Assessment Recommendations Next Steps
9. Interviews, data analysis and process
mapping were foundation of analysis
Staff Interviews
• Interviewed hospital and health center personnel to develop understanding of patient and information flows,
as well as staff roles, responsibilities, and common challenges
Data Analysis
• Analyzed patient arrival and appointment data to determine how effective the existing system has been at
predicting patient arrivals
Process Mapping
• Created diagrams to illustrate each stage of patient and information flows
Observation
• Observed Hospital registration desk to better understand underlying patient registration data and to audit
appointment data
Patient Interviews
• Interviewed patients to validate information received from staff interviews
8
Background Project Overview Assessment Recommendations Next Steps
10. Patients currently arrive at Hospital through one
of three channels
Patient Flow
Health Center Referral to Hospital WITH Appointment
Health Center
Health Center Health Center Patient visits
Patient visits
refers patient sets Hospital on
Health Center
Hospital to Hospital appointment scheduled date
Hospital Follow-up Referral to Hospital WITHOUT Appointment
Hospital Hospital treats
Patient visits
Patient visits patient and
Hospital on
Hospital gives referral for
unknown date
follow-up visit
Hospital Follow-up Referral to Hospital WITH set date but WITHOUT Appointment
Hospital Hospital treats Doctor sets date for Patient visits Hospital
patient and gives follow-up visit but on scheduled
Patient visits Hospital
referral for follow-up doesn’t set formal date, date unknown
visit appointment to Hospital
9
Background Project Overview Assessment Recommendations Next Steps
11. Assessment of Current Process
Analysis of Appointment, Registration
and Reporting Processes
Background Project Overview Assessment Recommendations Next Steps
12. Referral process has solid foundation,
opportunities not fully exploited
S Strengths W Weaknesses
• Increased communication • Slow data collection
between health center and hospital • Some patient segments are not
• Strong foundation for data captured
exchange • Inter-departmental data sharing
• Creates awareness with is low
constituents
O Opportunities T Threats
• More complete data capture can • Better implementation, and thus
reduce arrival variability service, at other district hospitals
• Faster analysis and reporting with Reduced referrals and revenue
electronic data management for Ruli
• Increases to staff efficiency • Changes to MoH mandates
through planned work limiting Ruli’s ability to comply
11
Background Project Overview Assessment Recommendations Next Steps
13. Referral and registration assessment
uncovered systemic issues
Areas Investigated Issues Identified
Referral Data Inconsistent Data
Collection
Collection and
• Referrals logged via phone
Usage and email
Registration Inefficient Processes
Process and Data • Maintenance of electronic
Collection and paper records
Registration Lack of Data Usage
• Infrequent communication of
Reporting Process appointments logged
12
Background Project Overview Assessment Recommendations Next Steps
14. Inconsistent data collection results in
many exceptions to standard process
Existing Appointment Book Existing Registration Book
• Appointment data is collected in • Registration data is captured in
multiple formats multiple log books
– Phone Calls – Uncertain benefit for separate first time maternity
– Emails patients log
• Data format varies by health center
– Not all referring Health Centers set appointments • Registration is not provided with
• Follow-up patient appointments are patient information prior to patient
not currently tracked arriving at registration window
13
Background Project Overview Assessment Recommendations Next Steps
15. Reduced data usage due to hard copy
records and inconsistent processes
• Handwritten logs reduce opportunities for data
analysis
– Analysis requires manual review and is more prone to
error
• Inter-departmental data sharing is complicated
by need to review and utilize log simultaneously
– Paper logs limit total users to one
• Patient files may become split if patient ID is
unknown by returning patient
– Older patient records may become orphaned utilizing
limited storage space while not providing benefit
14
Background Project Overview Assessment Recommendations Next Steps
16. Process inefficiencies create potential
for errors and reduce effectiveness
• Referral data is currently tracked on a scratch
pad, electronic log and handwritten log
– No calendar is kept for number of total appointments
• Instances of inaccurate numbering due to illegible
handwriting impacting reporting
– Multiple numbering errors were found during review
of registration logs
• Registration and consultation do not have access
to accurate and timely patient
projections, limiting work planning
15
Background Project Overview Assessment Recommendations Next Steps
17. Appointment system accounts for low
proportion of arriving patients
120
100
80
60
40
20
0
8/2/2012
10/2/2012
14/2/2012
16/2/2012
20/2/2012
22/2/2012
24/2/2012
28/2/2012
3/1/2012
3/5/2012
3/7/2012
3/9/2012
15/3/2012
19/3/2012
21/3/2012
23/3/2012
27/3/2012
29/3/2012
Total Outpatient Arrivals 13/3/2012
Appointments
16
Background Project Overview Assessment Recommendations Next Steps
18. Appointment system has not reduced
volatility in outpatient arrivals
45.0 50.0
40.0 45.0
40.0
35.0
35.0
30.0 30.0
25.0 25.0
20.0 20.0
15.0
15.0
10.0
10.0 5.0
5.0 -
- Before Current
Before Current Lower 25th Percentile of Arrivals
Daily Average St. Dev. Upper 25th Percentile of Arrivals
Before is the period from January 3rd, 2011 through the implementation of the referral system in February 2012. Current is
February 2012 through present. 17
Background Project Overview Assessment Recommendations Next Steps
19. On April 3rd, majority of referred
patients arrived without appointments
In/Out of Patient
Type Number Percentage Health Center Patient Referrals
District Appointments
Coko In 2 1
Had referral only 39 78%
Gasgara Out 4 0
Had referral and
10 20%
appointment Gitega Out 2 0
Chronic or follow-up 1 2% Kabuga Out 1 0
Kayenzi Out 2 0
Total 50 100%
Muhondo In 7 0
Nyabikenke Out 7 1
• In district Health Centers Nyange In 1 1
had appointments 36% of Ruli In 10 5
Rushashi In 2 2
the time (9 of 25)
Rutonde Out 1 0
• Out of district Health Rwahi Out 1 0
Centers had appointments Rwankuba In 3 0
6% of the time (1 of 18) Unknown N/A 6 0
Total 49 10
Patient arrival data gathered at registration on April 3rd, 2012. Six patients’ registrations were unobserved. 18
Background Project Overview Assessment Recommendations Next Steps
21. Recommendations range from quick
wins to long-term strategy
Short-Term
• Changes that can be enacted in the next six months
• Form the basis for mid-range and long-term strategic vision for data
usage
Mid-Range
• Modifications in the one to three year time horizon
• Build upon foundation of short-term recommendations
• May require capital allocations
Long-Term
• Guiding principles for data management at the Health Center and
Hospital levels
• May require support from, and modification of, MoH directives
20
Background Project Overview Assessment Recommendations Next Steps
22. Changes to the Health Center and
Hospital appointment process
Proposed Changes: Immediate Intended Impact
Action
Collect all data points during Information is available sooner
phone call and actionable
Elimination of unused data points
Modify data points collected speeds data transfer
More natural fit for work
Shift appointment setting to
task, easier inclusion of follow-up
registration
visits
Add doctor scheduled follow-ups Provides more complete view of
to appointment log total patient volume
Eliminates duplicative
Shift to electronic log only effort, increases analytical
capability
Modify feedback loop with Health Increased ability to communicate
Center need to see physician
21
Background Project Overview Assessment Recommendations Next Steps
23. Electronic logs improves efficiency and
analytical capabilities
Electronic Appointment Log Electronic Registration Log
• Benefits include: • Benefits include:
– Returning patients’ files can be pulled day prior to – Reduction in errors caused by illegible entries
their appointment to reduce registration time – Ability to recover lost patient IDs
– Reduction in variability as appointment log will – Stored information can be used to perform
account for greater percentage of analysis of patient trends
appointments, allowing for improved scheduling – Increased speed in data entry and month end
reporting
– Improved system for tracking which referred
patients arrived at Hospital – Hospital can track which Health Centers refer
patients without scheduling appointments
22
Background Project Overview Assessment Recommendations Next Steps
24. Changes to data storage and usage
Proposed Changes: Intended Impact
Mid/Long-Term Action
More complete patient records,
Centralize patient paper records easier retrieval of entire patient
history
Easier retrieval of patient
Move to electronic patient
history, increased analytics, eases
records
reporting process
Increased inter-departmental
Develop central hospital
information sharing and
information network
reporting
23
Background Project Overview Assessment Recommendations Next Steps
25. Key Takeaways
Have Record doctor
Move Utilize digital
registration scheduled
appointment logs for
desk pull next- follow-up
setting to registration
day referrals in
registration and
appointment appointment
desk appointments
files log
24
Background Project Overview Assessment Recommendations Next Steps
26. Next Steps
Implementation steps for immediate
recommendations
Background Project Overview Assessment Recommendations Next Steps
27. Modifications to appointment setting
process
• Modify data collection to mirror excel template (provided)
• Switch appointment recording to registration
• Include doctor scheduled follow-ups in appointment log
– Doctors to stack patient files in separate piles to indicate which
require follow-up appointments
• Registration enters relevant data onto appointment log from patient
files, prior to refiling
• Health Center appointments
– Phone call
• Exchange all data points via standardized phone call
– Email
• Discard email from Health Center to Hospital
26
Background Project Overview Assessment Recommendations Next Steps
28. Modifications to appointment setting
process (cont.)
• Utilize new appointment schedule to reduce variation in
patient arrivals
– Reduce appointment cap on dates known to have high volume
of chronic patients (i.e. last Thursday of each month)
– Use historical data to predict trends in patient arrivals
27
Background Project Overview Assessment Recommendations Next Steps
29. Modifications to appointment
feedback loop
• Shift appointment attendance check to registration
– Aligns with shift of appointment setting
– Natural fit due to dependency on registration logs
• Change from negative check (i.e. marked if missed) to
positive verification (i.e. marked when registered)
• Email appointment log to all Health Centers on Friday
of each week
– Alerts Health Centers to missed appointments
• Investigate reasons for non-conforming Health Centers
and reiterate the importance of their cooperation
28
Background Project Overview Assessment Recommendations Next Steps
30. Modifications to registration and filing
process
• Capture all pertinent patient information in
electronic registration log (excel templates
provided)
– Electronic data in same format as paper logs, with the
addition of maternity flag, Health Center and
appointment verification
• Retrieve patient files for next day appointments
at end of each day
• Communicate following day’s appointment
schedule (volume and illnesses) to Head Nurse
for use in staffing
29
Background Project Overview Assessment Recommendations Next Steps
32. Outpatients registered per day
90
80
70
60
50
40
30
20
10
0
3/7/2011
3/1/2012
3/1/2011
3/2/2011
3/3/2011
3/4/2011
3/5/2011
3/6/2011
3/8/2011
3/9/2011
3/10/2011
3/11/2011
3/12/2011
3/2/2012
3/3/2012
3/4/2012
Above data excludes the last Thursday of every month. 31
Background Project Overview Assessment Recommendations Next Steps
33. Outpatient arrivals
Daily averages for outpatient Monthly averages for outpatient
arrivals arrivals
45 50
40 45
35 40
35
30 30
25 25
20 20
15 15
10 10
5
5 0
-
Above data from January 3rd, 2011 through present, excluding last Thursday of every month and holidays. 32
Background Project Overview Assessment Recommendations Next Steps
34. Outpatient arrivals on last Thursday of
each month
120
100
80
60
40
20
0
33
Background Project Overview Assessment Recommendations Next Steps
35. Current appointment data from Health
Centers
Appointments by Health Center Days after referral call that
appointment is set
In/Out of
Health Center Appointments Percentage Days Appointments Percentage
District
Coko In 92 16% 0 23 4%
Gasagara Out 44 7%
1 486 82%
Muhondo In 51 9%
2 56 9%
Nyabikenke Out 98 17%
3 15 3%
Nyange In 47 8%
4 6 1%
Rukura In 16 3%
5 0 0%
Ruli In 125 21%
6 1 0%
Rushashi In 51 9%
Rwankuba In 66 11% N/A 3 1%
Total 590 100% Total 590 100%
34
Background Project Overview Assessment Recommendations Next Steps
36. Per week appointments made by
Health Centers
Health In/Out of
30/1/2012 6/2/2012 13/2/2012 20/2/2012 27/2/2012 5/3/2012 12/3/2012 19/3/2012 26/3/2012 Total
Center District
Coko In 0 5 10 9 14 19 13 10 12 79
Gasagara Out 0 5 2 14 8 2 9 4 0 43
Muhondo In 0 5 6 13 7 1 5 7 7 39
Nyabikenke Out 0 13 13 23 3 10 4 16 16 78
Nyange In 0 12 9 5 0 8 4 3 6 41
Rukura In 1 2 2 5 3 0 0 3 0 13
Ruli In 1 19 26 29 19 14 10 3 4 121
Rushashi In 2 4 11 5 7 8 9 1 4 46
Rwankuba In 8 18 6 6 8 7 7 6 0 60
Total 12 83 85 109 69 69 61 53 49 590
35
Background Project Overview Assessment Recommendations Next Steps
