Strengthening Performance Management
through Enhanced Wait Time Reporting
Haim Sechter: Manager, Reporting and Analytics
Jennifer Liu: Team Lead, Reporting and Analytics
Presentation Summary
1. Cancer Care Ontario background
2. Wait Time Reporting
 Current data collection and reporting
 New reporting solution
3. Project delivery, benefits, and lessons learned
4. Next steps
2
Cancer Care Ontario (CCO)
• Ontario government agency
• Drives quality and continuous improvement in cancer,
chronic kidney disease, and access to care for key
health services
o Disease prevention and screening
o Delivery of care
o Patient experience
3
Performance Management and Wait Times
• CCO monitors and manages wait time performance for
various treatments and diagnostic procedures at the provincial
and LHIN levels
• Works with cancer care providers
to continually improve access,
and publically reports
wait times
1.
Data/Information
4. Performance
Management
2. Knowledge
3. Transfer
4
Background: Data Collection
• Systemic and Radiation Wait Time information calculated from
Activity Level Reporting Data (ALR)
14 Regional
Cancer
Programs
Patient
Information
Disease
Information
> 165,000
radiation and
systemic
activity records
Health Care
Provider data
Clinic Visits
Systemic
Treatment
Monthly
submission
frequency
Radiation
Treatment
Minor
Procedures
5
Background: historical reports
6
Problem
Historical radiation and systemic treatment wait time reporting:
1 FTE
• Labour intensive
• Sub-optimal user experience
• Inefficient reporting process:
information available through multiple
channels
• Limited Access to information
• Delayed after monthly submissions
7
Solution
Future radiation and systemic treatment wait time reporting:
• Wait Times reports available through single
web-based application
• Information flows directly from EDW
• Interactive dashboard summaries at various
levels
• Increased functionality through drilling,
custom metrics, report subscriptions, detailed
analysis etc.
8
Merging of
reports
into one
source
9
Interactive
dashboards
10
v
Increased
Functionality
11
Detailed
Analysis
12
Integrated
Data
Quality
13
Project Delivery
Project elapsed tine:
• 24 months elapsed
Actual time:
• 6-8 months
Project team:
• BI Developer, Data Analyst, Data Architect, ETL
Developers, IT Operations, Business Analyst
Project tracking:
• weekly core team meetings
• Weekly working group meetings
14
Benefits
• ½ FTE redirected to advanced
analytics
• Time to delivery reduced by 1
week each month
• Development of decision
support tool
• New report structure appeals to
a variety of audiences
• Easily scalable
15
Challenges
Lessons Learned
• Project too big to run through
operations
• Too difficult to continually
leverage operation resources
• Stakeholders satisfied with
status quo
• Business must be constantly
part of working group
• Difficult to gain organizational
buy-in
• Improve organization
understanding of BI
• Underlying data not fit for BI
reporting
• Proper data infrastructure is
critical
• Strict Project management
approach has to be leveraged
• Too many competing priorities
16
Next Steps
• Provincial view of systemic
wait times
• Improve accuracy of
indicators through
standardization factors
• Integration with automated
scorecard
17