iHT² Health IT Summit New York - Cancer Care Ontario Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care" Presentation "Transforming Data into Meaningful Information to Support Improved Patient Care" Learning Objectives: ∙ Understand the information imperative for Cancer Care Ontario (CCO), one of the largest provincial health organizations in Canada, as it supports population-based care co-ordination and administration for 3 clinical domains in the province of Ontario: cancer care, renal care, and access to care ∙ Learn how the organization built the Informatics Centre of Excellence to better enable the acquisition, management, reporting, and analysis of one of the broadest and richest data sets in the country ∙ Discuss concrete examples of how CCO has used leading-edge analytic techniques to drive health system performance. Vickie Welch Director, Informatics Centre of Excellence Cancer Care Ontario Hakim Lakhani Director, Reporting and Analytics, Informatics Centre of Excellence Cancer Care Ontario

1. Transforming Data into Meaningful
Information to Support Improved
Patient Care
Presented By:
Vickie Welch, Director, Informatics Centre of Excellence
Hakim Lakhani, Director, Reporting and Analytics, Informatics Centre of Excellence

2. Agenda
Ontario in Context: Basics & the Healthcare System
CCO in Context: Managing the Chronic Patient Journey and Access to Care
Breadth & Scope of Data and our Users
Informatics Centre of Excellence: Formation & Highlights

3. Ontario in Context: Ontario vs. New York
3
13.51 million Population 19.57 million
415,598 m² Area 54,556 m²
211 Hospitals 204
14 Cancer Centres 6 (NCI)

4. Ontario’s Healthcare System
4
14 Cancer Centres211 Hospitals
Local Health Integration
Networks (LHINs)
Mixed Public - Private System
Funding:
Public – Ontario Health
Insurance Plan
Delivery:
Private not-for-profit
Private for- profit
• Healthcare is funded by the provinces which
are responsible for setting overall direction
and delivering care

5. Chronic Patient Journey & CCO
5

6. Oversees over 1 billion
in healthcare dollars
Implements
healthcare IM/IT
Transfers new research
into clinical practice
Focuses on quality
improvements and standards
Cancer Services
Ontario Renal
Network
Access to Care

7. As of 2009, an estimated 320,000 Ontarians were diagnosed with
cancer in the previous 10 years
65,000 new cases per year
1 million people screened for cancer yearly
115 hospitals performing cancer surgery
78 hospitals performing chemotherapy
15 hospitals performing radiation therapy
670+ Oncologists
Cancer in Ontario

8. 8
26 programs Administering total 91 locations
Approximately 10,000 people in Ontario are receiving dialysis
Of these, 77% go to centres and 23% dialyze at home
In 2010, 537 kidney transplants were performed
1108 CKD patients on a waiting list to receive a kidney transplant
CKD costs the province $586 million/year
Chronic Kidney Disease in Ontario

9. OR
SETP
Diagnostic Imaging
Wait Time
MRI/CT
ER Wait Time
Leave ER
Emergency
Room
Wait 3
ALC Wait Time
Wait 4
Acute Care
Post-Acute
Care
(Rehab, CCC, LTC, etc)
Post-Acute
Care
(CCC, LTC, etc)
Home Care
Wait 1 Wait 2
Surgical Wait Time
Primary
Care
Provider
Specialist
SETP
OR
ER Wait Time
Leave ER
Emergency
Room
Focus
Area
ER/ALC Information Strategy
Surgery & DI
Wait Time Strategy
Surgical Efficiency
Diagnostic Imaging
Wait Time
MRI/CT
Wait 1 Wait 2
Surgical Wait Time
Primary
Care
Provider
Specialist
ER ALC
Wait 3
ALC Wait Time
Wait 4
Acute Care
Post-Acute
Care
(Rehab, CCC, LTC, etc)
Post-Acute
Care
(CCC, LTC, etc)
Home CareOR
Access to Care in Ontario

10. 10
Ontario Health System Challenges
Pervasiveness of
disease
Value For
Money
Accountability

11. 11
11
11
Analytics can help…

12. Analytics - Managing the System
Health System
Information
Quality &
Continuous
Improvement
Program
Implementation
Standards &
Best Practices
Service
Planning &
Access to Care
Funding &
Sustainability
Research &
Innovation

13. Radiation,
Surgical and
Systemic
Treatment
Diagnostic
Assessment
Programs
ColonCancerCheck
&
Integrated Screening
Symptom
Management
Follow-up
Surveillance
Palliative
Care
Imaging,
Pathology &
Laboratory
Programs
Disease Pathway
Management
Chronic Patient Journey & CCO

14. Informatics Centre of Excellence
Our Objectives:
To build an Informatics Centre of
Excellence that will…
o Be closer to the customer
o Be more efficient
o Provide Value added services
o Have the right skills for the right
jobs
Through improved …
o Organizational Design
o Skills
o Processes
o Tools/technologies
Customer
Intimacy
Operational
Excellence
Product
Leadership

15. Our Aim - Transformation
Aspirational (35%)
• New or limited users of
analytics
• Focused on analytics at
point-of-need
• Turn to analytics for ways
to cut costs
Experienced (48%)
• Established users of analytics
• Seeking to grow revenue with focus on
cost efficiencies
• Seeking to expand ability to share
information and insights
Transformed (16%)
• Analytic use is cultural norm
• Highest levels of analytics prowess and experience
• Seeking targeted revenue growth
• Feel the most pressure to do more with analytics
Source: Analytics: The New Path to Value, a joint MIT Sloan
Management Review and IBM Institute of Business Value study.
Copyright © Massachusetts Institute of Technology 2010. Sample
size Healthcare n= 116

16. Transformation Priorities
16
• Customer IntimacyPriority #1
• Data ManagementPriority #2
• Talent ManagementPriority #3
• Process ImprovementPriority #4
• Performance ManagementPriority #5

17. 17
Ontario Renal Network Cancer Access to Care
Strategic Analytics & Funding and Financial Analytics Teams
Informatics Centre of Excellence
Organizational Model
REPORTING AND ANALYTICS
Data Acquisition Data Architecture Data Governance
ENTERPRISE DATA MANAGEMENT
BUSINESS OFFICE

18. 18
Analytic SpacesEnterprise Data
Management
Data Acquisition Presentation
CCO
Governance
Privacy and Security
Data Stewardship
Informatics Centre of Excellence
Functional Model
Transforming Health Data Into
Meaningful Information

19. 19
19
Organizations
OCR
140+
Data Sets
180+
Terabytes of data
WTIS
NACRS
DAD

20. 20
20
DE-IDENTIFICATION
DATA QUALITY
STANDARDIZED INBOUND AND OUTBOUND FLOWS
OPERATIONS
APPLICATION
INTERFACES
DATA GOVERNANCE
FUTURE STATE ARCHITECTURE
DATA ARCHITECTURE
DATA WHAREHOUSE ANALYTICAL VIEWS
INCREMENTAL GROWTH
Data available in an optimized structure for reporting
Data Quality is understood and documented
A single version of truth exists
Data stewards know their data domain
Consistent data definition is in place
End users are able to access information products in a
self serve manner based on their level of need
EDM Capabilities Enabled

21. 21
21

22. Measuring Performance – The Spectrum
Provincial Level
Outcome Indicators
Provincial Level
Driver Indicators
Regional Indicators
Health Professional Level Indicators
Big
Dots
Little
Dots
CQCO Adapted from Heenan, M. Khan, & Binkley, D. (2010). “From boardroom to bedside: How to define and measure hospital quality.” Healthcare Quarterly,
13(1): 55-60.
Cancer
System
Quality Index
(CSQI)
Quarterly
Regional
Performance
Scorecard
CCO Special
Reports/
Program
Reports
Screening
Activity Reports
by Primary Care
Provider
Surgeon
Scorecard

23. Analytics to Improve System Performance

24. 24

25. 25
Data Sources : *Y2005-2006 - CCO Pathology Audits; Y2008-2010 PIMS, ePATH
Prepared by: Cancer Care Ontario, Informatics
Sample
2005*
2006*
2007
2008
2009
2010
PositiveMargin(%)
0
10
20
30
40
50
60
70
80
90
100
Radical Prostatectomies
% Positive surgical margin (PSM) rate for Radical Prostatectomies for pT2 patients in Ontario
CCO Program Target 2008/09: 25%
A Quality Improvement Example:
CCO’s Performance Improvement Cycle in Action
Developed
best
practice
guidelines

26. Analytics to Improve Regional Performance
Regional Cancer Centre Performance Scorecard
SYMP-
TOM
MGMT
DAP
Apr 2012-
Mar 2013
RCC
Non-
RCC
RCC
Non-
RCC
RCC
Non-
RCC
RCC
Non-
RCC
Province ▲ ▲ ▲ 100% ▲ ▼ ▲ 100% ▲ ▲ ▲ 100% ▲ ▲ ▼ 100% ▲ ▲ ▲
C Central ▲ ▲ ▲ 3% ▲ NA NA 5% ▲ ▲ 11% ▼ 9% ▼ NA ▲ ▼ 1 1 0
C Waterloo Wellington ▼ 4% ▼ NA ▲ ▲ 5% 4% ▲ ▼ 6% ▼ ▲ NA ▲ ▲ 3 2 1
C Central East ▲ 5% ▼ ▼ ▲ 11% ▼ NA ▲ 3% ▲ ▼ 15% ▲ NA 12 3 -1
C Erie St. Clair ▲ ▲ 3% ▲ NA NA 3% ▼ 3% ▲ ▼ 5% ▼ NA ▲ 1 4 0
C
Central West &
Mississauga Halton
▼ ▼ ▼ 6% NA NA 5% ▲ ▲ 12% ▼ ▼ 3% ▲ NA 9 5 0
C North West 2% ▲ NA ▲ ▼ 3% NA 1% ▲ ▼ 3% ▲ NA ▲ NA NA 8 6 0
A Toronto Central South ▲ ▲ 21% ▲ NA NA ▲ 15% ▼ ▲ ▲ 21% 3% ▲ NA ▲ 6 7 3
C North East ▼ ▲ ▼ ▼ 5% ▲ ▲ 4% NA 3% ▲ ▲ ▼ 5% ▲ ▼ 5 8 0
A South West ▲ ▲ ▼ 9% ▲ NA ▼ 9% ▲ ▲ 10% ▲ ▼ 8% ▼ NA ▼ 10 9 0
A Toronto Central North 14% ▲ NA NA ▲ 11% ▲ 8% ▲ 1% ▲ NA 13 10 -3
A Champlain ▲ ▲ ▲ ▲ 10% ▲ 10% ▲ 10% ▼ 14% ▼ ▲ NA NA 7 11 2
C North Simcoe Muskoka ▼ ▼ ▲ 3% ▼ NA ▲ 4% NA 2% ▲ ▲ ▲ 2% ▼ NA 10 12 2
A
Hamilton Niagara
Haldimand Brant
▲ ▼ ▼ 11% ▼ 9% ▲ ▲ 11% ▼ ▲ ▼ 17% ▼ ▲ ▲ 14 13 -2
A South East ▼ ▲ ▼ 5% NA NA 5% NA 3% ▲ ▲ ▲ 8% ▲ NA 4 14 -2
Change
from
Previous
QuarterCON(C)
RSTP
Level 1
&2
Overall
Provincial
Rank
WT
Family
History
WT- Ref
toDiag
(Lung)
Data
Quality
ESAS
Apr
2012-
Mar
2013
SYSTEMIC
WT=Apr 2012-Mar 2013
Vol =Apr 2012-Mar 2013
RADIATION
WT=Apr 2012-Mar 2013
Vol =Apr 2012-Mar 2013
IMRT
SURGERY
WT=Apr 2012-Mar 2013
Vol =Apr 2012-Mar 2013
Vol
(cases)
COLONOSCOPY
WT=Apr 2012-Mar 2013
Vol =Apr 2012-Mar 2013
WT
+FOBT
WTRef-Con
(% w/in14 days)
WTCon-Tr
(% w/in28 days)
WT
(% w/intarget)
WTRef-
Con
(% w/in
14
days)
Vol
(C1R)
% of
Budget
Vol
Vol
(C1S)
Region
WTRTT-
Tr
(% w/in
target)
CHPCA
*NURSING PROGRAM
As of September 30, 2012
*MCC
Q3
RCC
Non-
RCC
Patient
Experience
(AOPSS)
Apr 2012 - Sept
2012
CON(C)
RSTP
Level 3
Emtional Support
Vol
% of
Budget
Vol
% of
Budget
Vol
% of
Budget
Vol

27. 27
Analytics to Improve Local Performance
Emergency Room Length of Stay Segment Dashboard

28. 28
Analytics to Improve Local Performance
Emergency Room Length of Stay
350,000
370,000
390,000
410,000
430,000
450,000
470,000
490,000
Apr08
Jun08
Aug08
Oct08
Dec08
Feb09
Apr09
Jun09
Aug09
Oct09
Dec09
Feb10
Apr10
Jun10
Aug10
Oct10
Dec10
Feb11
Apr11
Jun11
Aug11
Oct11
Dec11
Feb12
Apr12
Jun12
Aug12
Oct12
Dec12
Feb13
Apr13
Jun13
ERVolume
Volumes
Wait Times
Emergency
Department
Volumes
Emergency
Department
Length of
Stay

29. Analytics to Improve Provider Performance
Screening Activity Report
29

30. 0
50
100
150
200
250
300
350
400
450
KneeReplacementWaitTime-90thPercentile/days
Dec '12 90th Percentile Wait Time
LHIN Target
LHINS #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14
Advanced Analytics in Action:
Hip and Knee Surgical Capacity Planning
LHINs need an Integrated Orthopedic Capacity Plan (IOCP) for next two fiscal years to
meet their 90th percentile wait time targets for joint replacement surgery.
30
Regions Ministry
IOCP
Targets
Demand?
Supply?
Performance?

31. Advanced Analytics in Action:
Hip & Knee Surgical Capacity Planning - Model
31
Real Time Surgical
Wait List Data
Surgical Demand
Forecast
Surgical Arrival
Dynamics
Surgery Activity
Data
Surgery Dynamics
Regional Hip and
Knee Surgery
Queuing Model
Regional Surgical
Waitlist
Performance Model
Surgical Volume
Forecast
What-If
Analysis
model given
to the LHINs

32. Advanced Analytics in Action
Capacity Allocation to Improve Access to Care
32

33. Can we improve patient care and reduce
health system costs?
55% of Cost 45% of Cost
10% of Patients 90% of Patients
33
Advanced Analytics in Action:
High Intensity Inpatient Users

34. Could we have predicted high cost
patients when they started dialysis?
34

35. into a Machine Learning
algorithms to compute
joint probabilities
to identify predictor variables of
high intensity acute hospital users within the
first year of starting dialysis
Ontario Renal
Reporting System
Inpatient Records
(DAD)
Ambulatory Records
(NACRS)
Pre-Dialysis YearDialysis Incident Day
Fed 80
Input
Variables

36. Dialysis
crash start
Inpatient admissions
in pre-dialysis year
Serum albumin
at dialysis start
Emergency visits in
pre-dialysis year
Inpatient admissions
in pre-dialysis quarter
Followed by Nephrologist
before dialysis
Creatinine at
dialysis start
Clinical
Screening
Policy
Analysis

37. Aspirational (35%)
• New of limited users of analytics
• Focused on analytics at point-of-need
• Turn to analytics for ways to cut costs
Experienced (48%)
• Established users of analytics
• Seeking to grow revenue with focus on cost
efficiencies
• Seeking to expand ability share information
and insights
Transformed (16%)
• Analytic use is cultural norm
• Highest levels of analytics prowess and
experienced
• Seeking targeted revenue growth
• Feel the most pressure to do more with
analytics
Our Aim - Transformation
Cancer
Care
Ontario
Source: Analytics: The New Path to Value, a joint MIT Sloan
Management Review and IBM Institute of Business Value
study. Copyright © Massachusetts Institute of Technology
2010. Sample size Healthcare n= 116

38. 38
On the Horizon
System-Wide
Analytics
• Funding
Reform
• HealthLinks
Opportunities
• Networking
across the
health system
• Strategic
Analytics
Advisory Panel
Continuous
Improvement
• Improved
analytics
process
• Increased
partner
involvement
• Talent
management

39. Questions?