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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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.

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

• 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

Provincial Examples

Data Linkage Projects:

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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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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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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)

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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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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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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Canadian Institute for Health

Information

Data Linkage Projects:

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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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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

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

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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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!