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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.
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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
1
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:
5
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
6
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%
7
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
9
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
10
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
11
Canadian Institute for Health
Information
Data Linkage Projects:
12
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
13
HARP model
14
• 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
15
• 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)
16
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
17
18
Thank you!