In this slideshow, Dr Jeremy Veillard, Vice President, Research and Analysis, Canadian Institute for Health Information, describes how data is used in Canadian health care, describing a number of data linkage projects. Dr Jeremy Veillard spoke at the Nuffield Trust event: The future of the hospital, in June 2014.

1. High Use in the Health Sector in
Canada: The Art of the Possible
(or how to make the best use of
data linkage)
Jeremy Veillard, PhD
Vice-President, Research and Analysis
Canadian Institute for Health Information
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2. Canadian Institute for Health Information
• Independent, not-for-profit corporation
• 30 health databases and registries
• Our vision:
– Better data. Better decisions. Healthier Canadians
• Our mandate:
– To lead the development and maintenance of
comprehensive and integrated health information
that enables sound policy and effective health
system management that improve health and
health care.

3. Health Care in Canada
• 70/30 split public/private funding
• Public funding includes universal coverage of
physicians and hospital care
• Mixed public-private payment for some services
such as drugs, long term care, eye care
• Most health system delivery occurs at provincial and
territorial levels
• Overarching support for health care at federal level

4. • A priority issue across the country
• Two Approaches:
• Operational: identification of specific individuals to
manage their “high use” and provide better care
• Conceptual: identification of the types of people who are
high users and their characteristics to inform preventative
programs design
• Varied but congruent approaches to analysis and
measurement
– Improved understanding of high use and its dimensions
– Transitions into and out of high use
High Users in Canada

5. Provincial Examples
Data Linkage Projects:
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6. Ontario
Institute for Clinical Evaluative Sciences (ICES)
• Steward of publicly funded data in the province of
Ontario (population 13.5 million)
• Expertise in de-identifying, managing and analyzing
large administrative datasets
• Linked data repository
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7. Ontario high use studies
• University of Toronto/ICES
– 1% of population accounts for 34% of health expenditures
– 5% of population accounts for ~66%
– Identifies high user profiles
• Public Health Ontario/ICES
– Linked health care administrative data for Ontario’s adult
respondents to Canadian Community Health Survey
– Population perspective to prevent high use before health
declines and high resource-utilization patterns begin
• University of Toronto/ICES
– Study of children who are high healthcare resource utilizers
– Examines and profiles top 1% and 5%
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8. Source: Wodchis and Guilcher, 2012
1%
34%
5%
66%
10%
79%
50%
99%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Ontario Population Health Expenditure
Figure 3. Health Care Cost Concentration:
Distribution of health expenditure for the Ontario population,
by magnitude of expenditure, 2007
$33,335
$6,216
$3,041
$181
Expenditure
Threshold
(2007 Dollars)

9. British Columbia
• Population Data BC
– De-identified, longitudinal data on 4.4. million BC residents
– Data can be linked to each other and to external data sets
across sectors: health, education, ECD, & workplace
• Ministry of Health’s Blue Matrix
– Big Data database that summarizes information about
health status, chronic conditions, socio-demographics and
health care service utilization for each BC resident over 10
years
– Analysis of retrospective trajectories enables identification
of risk/prediction of high use
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10. Alberta
• Alberta Health Services can estimate costs to the
health system of every AB resident
– Model incorporates acute care, emergency, ambulatory,
specialist, long term and primary care costs
• Top 5% grouped into six profiles at risk of high use:
– Frail elderly
– Complex older adults
– Reproductive health
– Complex infants/toddlers
– High needs youth
– High needs children
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11. Manitoba
Manitoba Centre for Health Policy
• 100+ linkable data sets including, administrative,
survey and clinical health databases and justice and
education databases
• Frequent users of Emergency Departments
– Mental health predominant issue for highest users
• Patient types with high use of hospitals
– 0.33% of MB residents received 45% of hospital care
– Developed model to predict risk of hospitalization
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12. Canadian Institute for Health
Information
Data Linkage Projects:
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13. Hospitalization At Risk Prediction (HARP)
• Concept: to identify patients with high risk of hospitalization
at Primary Health Care (PHC) settings for early
interventions
• No PHC data, only inpatient and outpatient hospital data
• Multiple regression to estimate the relationship between
patient characteristics and risk for future hospitalization
• Variables in three categories:
– Patient demographic and community characteristics
– Patient disease and condition
– Patient encounters with the hospital system
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14. HARP model
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• Score for each patient to predict the risk of next
readmission within 30-day and 15-month. The
threshold of the score can be set by the user
• 5 factors (Simple model): Age, Discharge dispositions,
Hospitalizations (prior 6 months), ED visits (prior 6
months), Select Case Mix Groups
• 10 factors (Complex model): + Comorbidities,
Resource intensity level, Admission through ED,
Longer list of CMGs, Select interventions

15. Population Risk Adjusted Grouper
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• Link person-level clinical and financial data across
health sectors to risk-stratify population
• Will link hospital, residential care, physician billing,
drugs (seniors), mental health, home care data
• Comprehensive person profile integrates diagnoses,
functional impairments and demographics
• Predicted cost, utilization and risk profiles at person
and population level

16. High Risk Patient Prediction
• Identify distinct types of high risk individuals
– First episode (PHC, social determinants to predict risk of
trajectory into high use)
– Continued high use (hospital, residential and home nursing care
data to estimate risk of ongoing high use)
• Identify high risk groups with variable trajectories,
amenable to early intervention
• Integrate PRAG clinical profile into HARP framework
• Incorporate social determinants predictive of trajectory into
high use (Statistics Canada, Toronto health equity data)
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17. Conclusions
• Data linkage is instrumental to understanding pathways into
and out of high use
• Linkage needs to be judicious, focussed on specific
questions and respectful of privacy
• Linkage across sectors can identify individuals with high
need for services in areas beyond health, informing
“upstream” interventions
– E.g. linking health and justice data can illuminate experiences of
individuals with mental health issues
• Data linkage a method to answer a research question
– Not an end in itself
– Has to be commensurate with potential gains
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Thank you!