Canadian Institute for Health Information

Measuring Health System Efficiency in Canada: Introducing CIHI’s Program of Work Web Conference Presentation Sara Allin, Senior Researcher, CIHI February 27,

Why Is CIHI Measuring Health System Efficiency? There is widespread recognition that the health system needs to make better use of existing resources and improve value for money Health system managers currently face tight budget constraints Information about variations in efficiency could support provincial health system performance improvement In this work, the health system includes all activities under the jurisdiction of provincial ministries of health 3

Program of Work on Health System Efficiency in Canada: Phase 1 Developed a conceptual model for measuring health system efficiency Summarized results of qualitative research, including a stakeholder dialogue and interviews with senior health system decision-makers Results showcased in CIHI’s technical report Developing a Model for Measuring the Efficiency of the Health System in Canada (released July 2012) 4

Phase 1: Qualitative Research to Develop a Model for Measuring Health System Efficiency Policy ScanElite InterviewsStakeholder Dialogue GoalIdentify the stated objectives of the health system Identify provincial health policy-makers’ views on the inputs to and outcomes of the health system Engage stakeholders in discussion on health system objectives, boundaries and methods Selection Criteria Publicly available documents produced by federal, provincial and territorial governments that address health systems and policies Current or former senior health ministry officials of provincial/ territorial governments Current or former senior decision-makers, health system consultants and senior executives from health care organizations Sample Size17 interviewees from 9 provinces and 2 territories 16 participants from 6 provinces,1 territory and the federal government 5 Note: There was no overlap among interview and dialogue participants.

Program of Work on Health System Efficiency in Canada: Phase 2 Applied the conceptual model to spending and health outcome data at the regional level Objective: To understand what the health system is meant to achieve, i.e., the objective against which we should be measuring efficiency Results showcased in the analytical report Measuring the Level and Determinants of Health System Efficiency in Canada (released April 2014) 6

Summary of the Proposed Conceptual Model to Measure Efficiency (Developed in Phase 1) Factors to explain inefficiency Environmental factors (e.g., socio-economic, demographic characteristics of the regional population) Health system factors (e.g., clinical and operational factors) 7

Phase 2: Research Questions What is the average level of efficiency in Canada’s regional health systems? What factors explain variations in efficiency across the health regions? What are the key data gaps that CIHI could address to improve future empirical analyses of health system efficiency? 8

Next on the Agenda Michel Grignon, McMaster University, will present the methods, data and results of CIHI’s analysis of health system efficiency Martha Burd, B.C. Ministry of Health, will reflect on the findings of the report from the perspective of a decision-making organization 9

Measuring the Level and Determinants of Health System Efficiency in Canada: Summary of Methods and Findings Web Conference Presentation Michel Grignon, Director, Centre for Health Economics and Policy Analysis, and Associate Professor, Department of Economics and Department of Health, Aging and Society, McMaster University February 27,

Methods: Step 1—Data Envelopment Analysis to Calculate Efficiency Calculate point estimates of efficiency using data envelopment analysis (DEA), a descriptive approach to measuring efficiency based on linear programming Apply a statistical outlier detection methodology (Wilson 1993) Bootstrap point estimates to generate robust efficiency estimates (Simar and Wilson 1998) 11

Methods: Step 2—Regression Analysis to Explain Variations in Efficiency Factors affecting efficiency could fall into 3 broad categories 1.Clinical factors: Inappropriate or ineffective care provided, and prevention opportunities that are missed 2.Operational factors: Overly expensive inputs are used 3.Characteristics of the environment Step-wise regression to identify the variables significantly associated with efficiency estimates 12

Data: Summary of Input and Output Data in Sample of 84 Regions Across 10 Provinces InputsSource (Year)MeanRange Hospitals, $ per capitaCanadian MIS Database (2007 to 2009) 1, ,826 Prescription drugs, $ per capitaIMS Brogan (2010) Physicians, $ per capitaNational Physician Database (2007 to 2009) Residential care facilities (RCFs), $ per capita RCF Survey, Statistics Canada (2008) Community nurses, $ per capitaCensus, Statistics Canada (2006) Education (% with high school certificate or more) Canadian Community Health Survey (CCHS), Statistics Canada (2007–2008) Recent immigrants (%)CCHS, Statistics Canada (2007–2008) 3017 Non-Aboriginal (%)Census, Statistics Canada (2006) Output PYLL from treatable causes, before age 80, age standardized Vital statistics, Statistics Canada (2007 to 2009) 1,6661,0672,453 13

Results: Robust Estimates of Efficiency and Sensitivity Analyses Efficiency point estimates from DEA averaged between 0.65 and 0.82 across 7 separate model specifications This means that treatable PYLL could be reduced by 18% to 35% if all regions operated efficiently Efficiency estimates were not sensitive to the age cut-off for defining premature death (75, 80 or 85), or the choice of PYLL versus the standardized mortality rate from treatable causes 14

Results: Contribution of Each Category of Factors Affecting Efficiency Category Variables With Statistically Significant Associations With Efficiency (p<0.05)R2R2 Environmental and Population Characteristics Average income of the population Inequity in the likelihood of visiting a physician 7%–14% Clinical FactorsDaily smoking (%) Physical inactivity (%) Multiple (3 or more) chronic conditions (%) 30-day overall readmission to hospital (rate per 100) 14%–26% Operational FactorsGPs (% of physicians) Alternate level of care length of stay (days) 12%–22% 15 These variables together explain nearly 50% of total variation, leaving half of variation unexplained

Main Data Gaps More precise measures of patient flow –Patient-level data for physicians, prescription drugs, RCFs, nursing was unavailable Community care and public health spending data –CMDB has this, but limited comparability across provinces Indicators of clinical and operational factors that may affect efficiency –Integration and coordination of care, and expanding scopes of practice (e.g., for pharmacists and nurses) 16

Summary of Key Findings Years of life lost from treatable causes of death could be reduced by up to 35% if systems were managed more effectively and if their populations had lower health risks and better health Clinical factors, namely the indicators of successful prevention efforts such as the prevalence of smoking and physical activity, were significant drivers of efficiency after controlling for several key environmental characteristics Operational factors, such as investments in primary care and the appropriate use of hospitals, were also important The unexplained variation in efficiency scores could be driven in part by clinical practice variations and in part by other unmeasured patient and population characteristics 17

Proposed Future Research Undertake case studies of a sample of high-performing regions –E.g., what are some of the decisions that health system leaders have made that have led to good performance in the indicators that are associated with health system efficiency? Collect additional data on clinical and organizational factors that could affect efficiency 18

Measuring Health System Efficiency in Canada: Reflections From B.C. Web Conference Presentation Martha Burd, B.C. Ministry of Health February 27,

Measuring Efficiency... Many Choices 20 Environment, society Health system Inputs Health Health outcomes Outputs Project choices

High-Level Take-Away Learnings CIHI project findings: Significant factors as predictors of efficiency  Average income  Physical activity  Daily smoking  Multiple chronic conditions  Relationship to a GP  Hospital readmission rates  Length of stay in alternate level of care 21 Thoughts How can the health care system influence these factors? How does this fit with B.C.’s strategic directions?

Physical Activity We can promote... But some things are beyond our influence! 22 How can the health care system influence these factors?

Smoking Rates Government actions to reduce smoking Reduction of smoking rates has been part of our strategic plan since 2000 Increase price of tobacco products through higher taxes Restrict sales of tobacco products Implement smoking bans  2000: Vancouver was the first municipality in Canada to ban smoking in all public places  2002: B.C. banned smoking in public places, with allowance for smoking rooms  2008: B.C. banned smoking in public places, no exceptions Offer smoking cessation programs 23 How can the health care system influence these factors?

Chronic Conditions Identifying people with chronic conditions  Data sets may collect only single diagnosis per encounter  Chronic condition recorded when initially diagnosed  If stable, may not appear on later encounters  Analysis based on diagnoses recorded that visit or that year may miss chronic conditions B.C. has created virtual chronic condition registries  Specific algorithms identify earliest diagnosis of specific chronic conditions  Algorithm uses diagnoses from physician/hospital/prescription drugs databases  If criteria are met, add person to registry  Registries used to estimate prevalence rates, analyse services used by people with chronic conditions B.C.’s actions  Developed best practice care pathways, integrated care programs  Instituted physician incentive programs for best care, with measured compliance  Promoted attachment to physician: GP for Me program 24

Multiple Chronic Conditions 2.01 million British Columbians (44%) have 1 or more of these 18 chronic conditions (Chronic Disease Management Registries). Analysis of comorbidities is a challenge! To understand multiple comorbidities, we use a population segmentation approach: Divided people with these conditions into High, Medium and Low Complexity groups, based on complexity of individual conditions or selected comorbidities 25

Understanding the Health of the Population and Their Use of Services Evolution of Analysis Single chronic condition Multiple chronic conditions Overall health conditions Use of services across the health system How can we improve quality of care, access, efficiency, sustainability? Are these the right services in the right place at the right time? Result: Strategic planning and initiatives Prevention Early treatment Better coordination of care Integrated community care Alternatives to hospital care 26 Percentage of B.C. Population Percentage of Services Use of Selected Services, 2011/12 (Size of dot = per capita $)

About the Canadian Institute for Health Information (CIHI) CIHI established in 1994 as independent, not-for-profit corporation CIHI’s vision: Better data. Better decisions. Healthier Canadians. CIHI’s 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 CIHI’s data holdings: 27 databases of health information Range of stakeholders in health system and beyond –Government organizations (such as Health Canada and Statistics Canada), ministries of health, regional health authorities, non-government organizations, private-sector organizations, professional associations, health facilities 27

About the Canadian Population Health Initiative (CPHI) CPHI is a branch within CIHI CPHI’s mission: To support policy-makers and health system managers in Canada in their efforts to improve population health and reduce health inequalities through research and analysis, evidence synthesis and performance measurement 28

For More Information CIHI: CPHI: 29

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