unlocking the power of healthcare data
TRANSCRIPT
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HEALTHCARE DATA ANALYTICSUnlocking The Power Of Healthcare Data TOGETHERLisa Lix, PhD, P.Stat.September 30, 2015Cyber Summit Generation D: Data Scientists of Tomorrow
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OutlineWhy together is better
• Where I work and do my research• Examples of healthcare analytics in
action• What lies ahead
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George and Fay Yee Centre for Healthcare Innovation (CHI)• CHI is a partnership between the University of
Manitoba and the Winnipeg Regional Health Authority
• CHI brings together leaders and practitioners from many academic disciplines and areas of practice
• CHI aims to:– improve patient outcomes, – enhance patient experiences, and – improve access to care for Manitobans
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CHI’s PlatformsData
Science
Evaluation
Knowledge Synthesis
Health System
Performance
Clinical Trials
Project Manageme
nt
Knowledge Translation
Patient
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CHI’s Data Science Platform
Our Activities:Research
CollaborationTraining
Clinical Research
Data Group
Biostatistics Group
Bioinformatics and
Computational Biology
Group
Our Vision: To create and integrate diverse types of patient data and develop and apply the best analytic methods to provide new insights about patient outcomes, experiences, and care
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Healthcare Analytics in Action: Provincial
Manitoba Centre for Health Policy Data Quality Framework
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Manitoba Centre for Health Policy Research Data Repository
Population- Based Health
Registry
Social Housing
Education
Healthy Child
Manitoba
Immunization
Medical Services
Lab
Nursing Home
Clinical
ProviderVital Statistics
Emergency Dept.
Health Links
Home Care
Pharmaceuticals
Hospital
Family Services
Income Assistance
Census Data
• Family First
• Healthy Baby
• Intensive Care Unit
• Fetal Alcohol Spectrum Disorder
• Pediatric Diabetes
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Automating Data Quality Assessments
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Healthcare Analytics in Action: National• The Canadian Chronic Disease
Surveillance System (CCDSS)
• The Canadian Network of Observational Drug Effect Studies (CNODES)
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Healthcare falls primarily under the authority of the provinces and territories
The provincial and territorial healthcare systems differ in structure and operation
This results in a patchwork of systems and data resources
The Canadian Healthcare System
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The Canadian Chronic Disease Surveillance System (CCDSS) Established by the Public Health Agency of
Canada (PHAC) as a collaborative initiative amongst the federal, provincial, and territorial governments
Uses health administrative data to estimate chronic disease prevalence/incidence and the related burden on the healthcare system
Adopts a distributed surveillance system model that respects the data custodial responsibilities of the provinces and territories
Provides a standardized pan-Canadian approach to chronic disease surveillance
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Multimorbidity: An Example
Note: The 95% Confidence Intervals shows an estimated range of values which is likely to include the statistic 19 times out of 20. Data Source: Public Health Agency of Canada: using CCDSS data files contributed by the provinces and territories as of August 2015
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Note: The 95% Confidence Intervals shows an estimated range of values which is likely to include the statistic 19 times out of 20
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CCDSS Data
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CCDSS StructureModelPHAC receives input and guidance from provincial/territorial reps under federal/provincial/territorial agreements
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Canadian Network of Observational Drug Effect Studies (CNODES)• Network of over 60 Canadian
pharmacoepidemiologists, biostatisticians, clinicians, clinical pharmacologists, pharmacists, IT professionals, data analysts, and students using linked administrative data in 7 provinces plus UK and US data
• Timely responses to queries from Canadian public stakeholders about drug safety and effectiveness
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CNODES Sites
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CNODES Database Model• Data partners maintain physical control
of their data• Local content experts maintain a close
relationship with the data• Eliminates the need to create, secure,
maintain and manage access to a central, complex data warehouse
• Gives a pan-Canadian meta-analysis “answer” that dramatically increases sample size for rare events, RAPID RESPONSE
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CNODES ProjectIsotretinoin Use Amongst Women of Reproductive Age and the Risk of Pregnancy and Adverse Pregnancy Outcomes
Population basedMulti-province participationPregnancy/Outcomes in isotretinoin usersUS Comparisons
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CNODES Project
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What Lies Ahead?• Data Linkage
– Images– Streaming data from wearable devices– Electronic medical records
• Analyses– Biases– Rare events
• Data Visualization• Formalized Training
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Training in Healthcare Analytics• Strategic:
– Focussed on performance – Strategic thinking and communication skills– Less essential to have skills in the technical, nitty-gritty
details of setting up database systems and defining or selecting algorithms
• Operational: – Training in programming, statistics, mathematics– Skills in implementing systems to probe and interpret
data
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Essential Skills • Constructing data queries• Manipulating data into different
formats or structures• Modeling & analysis• Telling the story of the data
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The Science of Data Quality
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Aim for Smart Data, Not Necessarily Big Data
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