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Establishing and implementing an information management framework
Informatics leading practices
Presented to: National Healthcare Leadership Conference
Prepared by: Julian Martalog, Director – ATC Informatics
Matthew Hodge, Chief Medical Information Officer
Date: June 7, 2011
Overview
� Informatics at CCO
� Informatics leading practices
� Real world examples at CCO
� Benefits of using a framework approach
3
CCO’s performance improvement approach
1ESTABLISH TARGETS
Set performance goals and outcomes for areas of focus (e.g. cancer, surgical wait times, chronic kidney disease, etc.)
2MEASURE PERFORMANCE
Define, agree upon and implement standard, provincial measures of performance
3MONITOR PERFORMANCE
Review current state performance on a regular basis
4ANALYZE AND INTERPRET INFORMATION
Analyze and interpret data to identify areas for improvement
5INTERVENE TO IMPROVE PERFORMANCE
Implement targeted interventions (e.g. process improvements, reorganization, redistribution, policy changes, resource allocation) to improve performance
6ASSESS IMPACT ON PERFORMANCE
Measure and evaluate interventions to determine impact, and course correct as necessary.
1. Establish
Targets
2. M
easure
Performance
3. Monitor
Perfo
rmance
4. Analyze and
Interpret
Information
5. Intervene to
Improve
Performance
6. Assess
Impact on
Perfo
rmance
PERFORMANCE
IMPROVEMENT
1. Establish
Targets
2. M
easure
Performance
3. Monitor
Perfo
rmance
4. Analyze and
Interpret
Information
5. Intervene to
Improve
Performance
6. Assess
Impact on
Perfo
rmance
PERFORMANCE
IMPROVEMENT
Our Clients & Stakeholders
Clinical Programs
Internal CCO External
Pop. Studies & Surveillance
Provincial Planning
Performance Management
Regional Cancer Programs
Cancer Quality Council of Ontario
Hospitals & Providers
Public Health Units
MoHLTC
Researchers
LHINs
Screening & Prevention
Finance National (CPAC, STC, PHAC)
Informatics leading practices
Data Standards & Acquisition
Develop minimum data set (MDS) linked to KPIs and other key business processes
Align with and leverage existing data standards
Create data sharing agreements to acquire data from other agencies
Develop interim data collection system to meet urgent information needs
Develop stakeholder engagement strategy and documentation
Identify opportunities to collect once, use many times
Data Mgmt & Governance
Data access and request policies and procedures linked to privacy principles
Provincial operational team supporting data collection
Focus on front end business validation rules
Independent data certification committee for public reporting
Define and assign data steward role
Facility-specific lead for data management
Data Quality
Develop data quality improvement plan and integrate into overall R&A plan
Use data quality framework to guide activities
Data compliance matrix and escalation linked to data quality framework
Conduct initial assessment of reportable data
Multi-layered data quality improvement approach
Cultivate data supply chain to share DQ responsibilities through education sessions
Reporting &Analytics
Develop informatics vision and reporting plan (including stakeholder needs assessment)
Delivery model includes “push” (std reports) and pull (self-serve)
Project performance and track actuals against targets
Develop business intelligence strategy and action plan
Regular review and feedback on performance from clinical lead
Develop investigative analytics to support pgm mgmt & research
Simple, user-friendly public reporting
PerformanceMeasurement
KPIs meet defined criteria (e.g. linked to objectives, data of high quality, etc.)
Plot KPIs on quality measurement framework
Performance improvement program (case studies, data reviews, etc.)
Data modeling linked to performance management
Return on investment analysis
Balanced scorecard developed for leadership team and executive sponsor
Business intelligence dashboard
Informatics leading practices
Data Standards & Acquisition
Develop minimum data set (MDS) linked to KPIs and other key business processes
Align with and leverage existing data standards
Create data sharing agreements to acquire data from other agencies
Develop interim data collection system to meet urgent information needs
Develop stakeholder engagement strategy and documentation
Identify opportunities to collect once, use many times
Wait Times Upload Tool
Interim data collection system developed in ~2weeks
Data used to meet urgent needs for contract allocation and public reporting purposes
Data used to model additional incremental funding ($108M in year funding)
System replaced by Wait Time Information System (WTIS)
Informatics leading practices
Data Mgmt & Governance
Data access and request policies and procedures linked to privacy principles
Provincial operational team supporting data collection
Focus on front end business validation rules
Independent data certification committee for public reporting
Define and assign data steward role
Facility-specific lead for data management
CHIEFData Steward
Data Steward
Business Lead
Support Role
• Strategic oversight of data stewardship function• Ensures regulatory, privacy and information sharing policies are followed
• Monitor data access• Ensure data quality• Subject matter expert
• Identify data quality priorities• Business ownership of Data Sharing Agreements• Business requirements & endorsement of standards changes
• IT and EDW operations• Security & privacy• Data quality• Data architecture
Informatics leading practices
Data Standards & Acquisition
Develop minimum data set (MDS) linked to KPIs and other key business processes
Align with and leverage existing data standards
Create data sharing agreements to acquire data from other agencies
Develop interim data collection system to meet urgent information needs
Develop stakeholder engagement strategy and documentation
Identify opportunities to collect once, use many times
Data Mgmt & Governance
Data access and request policies and procedures linked to privacy principles
Provincial operational team supporting data collection
Focus on front end business validation rules
Independent data certification committee for public reporting
Define and assign data steward role
Facility-specific lead for data management
Data Quality
Develop data quality improvement plan and integrate into overall R&A plan
Use data quality framework to guide activities
Data compliance matrix and escalation linked to data quality framework
Conduct initial assessment of reportable data
Multi-layered data quality improvement approach
Cultivate data supply chain to share DQ responsibilities through education sessions
Key
Determinants
Elements
Components
Timeliness ReliabilityValidity Usability
Freshness
Currency
ConsistencyAccuracy
Comprehensiveness
Completeness
Relevance
Accessibility
Interpretability
Standardization
Historical Comparability
Linkability
Precision
Informatics leading practices
Reporting &Analytics
Develop informatics vision and reporting plan (including stakeholder needs assessment)
Delivery model includes “push” (std reports) and pull (self-serve)
Project performance and track actuals against targets
Develop business intelligence strategy and action plan
Regular review and feedback on performance from clinical lead
Develop investigative analytics to support pgm mgmt & research
Simple, user-friendly public reporting
Cancer Surgery
90th Percentile Wait Time
March YTD 2007/08 vs 2008/09
0 10 20 30 40 50 60 70 80 90 100
South West
Toronto Central PMH
Champlain
South East
Central West and Miss. Halton
Waterloo Wellington
Central East
North East
Hamilton NHB
North Simcoe Muskoka
Toronto Central Odette
Central
North West
Erie St. Clair
PROVINCE
Days
Target for 2008/09
Apr - Mar 2007/08
Apr - Mar 2008/0955Provincial Target
Cancer Surgery Wait Times in the Province
Decision-to-Operate to Operation
for All Priority Categories and All Specialties
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
2006-07 2007-08 2008-09
Perc
en
t o
f P
atien
ts
40
50
60
70
80
90
100
Days
% of Patients Treated Within Target
90th Percentile
Target for % of Patients w/in Target
Target for 90th Percentile
Informatics leading practices
Data Standards & Acquisition
Develop minimum data set (MDS) linked to KPIs and other key business processes
Align with and leverage existing data standards
Create data sharing agreements to acquire data from other agencies
Develop interim data collection system to meet urgent information needs
Develop stakeholder engagement strategy and documentation
Identify opportunities to collect once, use many times
PerformanceMeasurement
KPIs meet defined criteria (e.g. linked to objectives, data of high quality, etc.)
Plot KPIs on quality measurement framework
Performance improvement program (case studies, data reviews, etc.)
Data modeling linked to performance management
Return on investment analysis
Balanced scorecard developed for leadership team and executive sponsor
Business intelligence dashboard
Quality
Dimensions ����
Patient Journey
����
Safe Effective Accessible Responsive Efficient Equitable Integrated
Prevention
Screening
Diagnosis
Treatment
Recovery
Palliative & end-
of-life care
Surveillance Cancer incidence, mortality, survival, prevalence
Population Health Risk factors & socio-demographic factors
Structure/Process/Outcome
Macro/Meso/Micro
Informatics leading practices
Data Standards & Acquisition
Develop minimum data set (MDS) linked to KPIs and other key business processes
Align with and leverage existing data standards
Create data sharing agreements to acquire data from other agencies
Develop interim data collection system to meet urgent information needs
Develop stakeholder engagement strategy and documentation
Identify opportunities to collect once, use many times
Data Mgmt & Governance
Data access and request policies and procedures linked to privacy principles
Provincial operational team supporting data collection
Focus on front end business validation rules
Independent data certification committee for public reporting
Define and assign data steward role
Facility-specific lead for data management
Data Quality
Develop data quality improvement plan and integrate into overall R&A plan
Use data quality framework to guide activities
Data compliance matrix and escalation linked to data quality framework
Conduct initial assessment of reportable data
Multi-layered data quality improvement approach
Cultivate data supply chain to share DQ responsibilities through education sessions
Reporting &Analytics
Develop informatics vision and reporting plan (including stakeholder needs assessment)
Delivery model includes “push” (std reports) and pull (self-serve)
Project performance and track actuals against targets
Develop business intelligence strategy and action plan
Regular review and feedback on performance from clinical lead
Develop investigative analytics to support pgm mgmt & research
Simple, user-friendly public reporting
PerformanceMeasurement
KPIs meet defined criteria (e.g. linked to objectives, data of high quality, etc.)
Plot KPIs on quality measurement framework
Performance improvement program (case studies, data reviews, etc.)
Data modeling linked to performance management
Return on investment analysis
Balanced scorecard developed for leadership team and executive sponsor
Business intelligence dashboard
Informatics leading practices in action: real world examples at CCO
Chronic Kidney Disease (ORN)
CKD leading practices and associated year of implementation
Develop minimum
data set (MDS) linked
to KPIs and other key
business processes
Align with and
leverage existing
data standards
Create data sharing
agreements to
acquire data from
other agencies
Develop interim data
collection system to
meet urgent information
needs
Develop
stakeholder
engagement
strategy and
documentation
Identify
opportunities to
collect once, use
many times
Y1 Y1 Y1 Y1 Y1 Y1
Develop data quality
improvement plan
and integrate into
overall R&A plan
Use data quality
framework to
guide activities
Data compliance
matrix and
escalation linked to
data quality
framework
Conduct initial
assessment of
reportable data
Multi-layered data
quality
improvement
approach
Cultivate data
supply chain to
share DQ
responsibilities via
education sessions
Y2 Y1 Y1 Y1 Y1 Y2Develop informatics
vision and reporting
plan (including
stakeholder needs
assessment)
Delivery model
includes “push”
(std reports) and
pull (self-serve)
Project
performance
and track
actuals against
targets
Develop
business
intelligence
strategy and
action plan
Regular review
and feedback on
performance
from clinical
lead
Develop
investigative
analytics to
support program
mgmt & research
Simple, user-
friendly
public
reporting
Y2 Y3 Y2 Y2 Y2 Y3 Y2KPIs meet defined
criteria (e.g. linked
to objectives, data of
high quality, etc.)
Plot KPIs on
quality
measurement
framework
Performance
improvement
program (case
studies, data
reviews, etc.)
Data
modeling
linked to
performance
management
Return on
investment
analysis
Balanced
scorecard
developed for
leadership team
and executive
sponsor
Business
intelligence
dashboard
Y1 Y1 Y3 Y2 Y2 Y2 Y3
Data Standards &Acquisitions
Y1: fiscal year Oct’09 - Mar’10; Y2: fiscal year Apr ‘10 – Mar ’11; Y3: fiscal year Apr ‘11 – Mar ‘12
Data Quality
Reporting & Analytics (R&A)
Performance Measurement
Data access and
request policies and
procedures linked to
privacy principles
Provincial
operational team
supporting data
collection
Focus on front end
business validation
rules
Independent data
certification committee
for public reporting
Facility-specific
lead for data
management
Define and assign
data steward role
Y1 Y3 Y1 Y2 Y2 Y1
Data Mgmt & Governance
CKD leading practices and associated year of implementation
Develop minimum
data set (MDS) linked
to KPIs and other key
business processes
Align with and
leverage existing
data standards
Create data sharing
agreements to
acquire data from
other agencies
Develop interim data
collection system to
meet urgent information
needs
Develop
stakeholder
engagement
strategy and
documentation
Identify
opportunities to
collect once, use
many times
Y1 Y1 Y1 Y1 Y1 Y1
Use data quality
framework to
guide activities
Data compliance
matrix and
escalation linked to
data quality
framework
Conduct initial
assessment of
reportable data
Multi-layered data
quality
improvement
approach
Y1 Y1 Y1 Y1
KPIs meet defined
criteria (e.g. linked
to objectives, data of
high quality, etc.)
Plot KPIs on
quality
measurement
framework
Y1 Y1
Data Standards &Acquisitions
Y1: fiscal year Oct’09 - Mar’10; Y2: fiscal year Apr ‘10 – Mar ’11; Y3: fiscal year Apr ‘11 – Mar ‘12
Reporting & Analytics (R&A)
Performance Measurement
Data Quality
Data access and
request policies and
procedures linked to
privacy principles
Y1
Define and assign
data steward role
Y1
Focus on front end
business validation
rules
Y1
Data Mgmt & Governance
Benefits of using a framework
� Approach is business-driven, non-technical and
readily understood by a variety of stakeholders
� Clear linkage of business objectives, information
requirements, and data acquisition approach
� Ensures data quality is not an afterthought
� Repeatable process with tangible deliverables
� Facilitates alignment with corporate standards,
processes and policies
Questions?
Contact Information
Julian MartalogDirector, Access to Care Informatics
(416) 217-1230
Matthew Hodge
Chief Medical Information Officer