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.
Text of Dr Jeremy Veillard: High Use in the Health Sector in Canada, 30 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 1
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: 5 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 6 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 Ontarios 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 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 Healths 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 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 10 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 11 12. Canadian Institute for Health Information Data Linkage Projects: 12 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 13 14. 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 15. 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 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) 16 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 17 18. 18 Thank you!