On Improving Measures of Outputs and Outcomes in Health Care

1. On Improving Measures of Outputs and Outcomes in Health Care • what do we want to know? • outputs – why bother ? • outcomes – absolutely ! • context (“awkward facts” ?) • the SNA / productivity approach • alternative approaches – person-level health and health care trajectories Michael Wolfson, Statistics Canada CMA Ottawa October 2007

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3. What Do We Want to Know?(in the context of “outputs” and “outcomes”) • are our health care (or health more generally) dollars being spent efficiently and effectively • what changes in the way we allocate health dollars would improve the health status of the Canadian population • what kinds of institutional structures are most likely to lead to cost-effective use of scarce health dollars CMA Ottawa October 2007

4. (total health spending as pct GDP) “Health care costs 10% of GDP” CMA Ottawa October 2007

5. Health Spending, 2006(estimated, $ billions, Source: CIHI) CMA Ottawa October 2007

6. Example – Capital Health (Edmonton Alberta) Institutional Structure • 11 hospitals • 6 community health / primary care centres • 1 rehab centre • 1 specialized heart institute • 10 community mental health clinics • 36 continuing care facilities • 29 public health establishments (including specialized units for birth control, immunization, STDs, TB, and travellers) • 37 patient labs • 69 physiotherapy clinics • 17 x-ray clinics CMA Ottawa October 2007

7. Economics 101 output input CMA Ottawa October 2007

8. Economics 101 output input CMA Ottawa October 2007

9. Economics 101 output input CMA Ottawa October 2007

10. Economics 101 output inefficient input CMA Ottawa October 2007

11. Economics 101 output “flat of the curve” inefficient input CMA Ottawa October 2007

12. Economics 101 output “flat of the curve” input CMA Ottawa October 2007

13. (Tu et al on Coronary Surgery) n.b. virtually no differences in one year survival; but no data on differences in health-related QoL e.g. almost 17x, with no benefits? CMA Ottawa October 2007

14. Medicare Spending Varies Widely Across the U.S., both per capita, and using an “end of life” spending index Fisher et al., 2003 (fisher 1) CMA Ottawa October 2007

15. Q1 to Q5: quintiles (fifths) of “hospital referral regions” with increasing levels of an index of Medicare spending (based on “end of life” expenditures) Cohorts: subsets of the Medicare population with selected conditions (MCBS = Medicare Beneficiary Survey) Conclusion: if anything, more spending increases mortality Source: Fisher et al, 2003 (fisher 2) CMA Ottawa October 2007

16. Underlying Person-Oriented Information (POI) for Heart Attack / Revascularization Analysis one year observation window (excluded) one year follow-up window Heart Attack (AMI) Treatment (revascularization = bypass or angioplasty) Death CMA Ottawa October 2007

17. 1995/96 2003/04 Heart Attack Patients in Large Health Regions – Treatment and 30 Day Mortality Rates (%) – 1995/96 to 2003/04 CMA Ottawa October 2007

18. SNA Approach: Treat Public Sector Activities the Same as the Private Sector  Define (i.e. make up) “Outputs” “Profits” ??? Outputs (total $) Inputs (total $) Public Sector Commercial Sector Industries CMA Ottawa October 2007

19. Why the SNA Approach is Problematic • “outputs” do not exist naturally in publicly provided health care • we certainly can count “activities”, like numbers of vaccinations (probably all useful) and numbers of coronary procedures (recall earlier slide!) • but outcomes of interventions should clearly be the objective of systematic and routine measurement • productivity is obviously important • but high “productivity” in doing useless or iatrogenic activities is bad • remember the three “E’s”: efficacy, effectiveness, and efficiency; no point measuring efficiency unless we know efficacy and effectiveness CMA Ottawa October 2007

20. Simple Weather Forecast CMA Ottawa October 2007

21. Detailed Cloud Forecast CMA Ottawa October 2007

22. health status “before” health status “after” health intervention other factors Definition - Health Outcome health outcome  change in health status attributable to a health intervention (for an individual) CMA Ottawa October 2007

23. Stat Can / CIHI Outcomes Analysis Framework CMA Ottawa October 2007

24. E. A. Codman and W.E. Deming • Codman: early 1900s Boston surgeon • famous for “End Results Cards” – to keep track of surgical patients and follow them up one year later to • observe outcomes • systematically learn from experience • 100 years later: not yet implemented in health care • Deming: post WW II concern with product quality in manufacturing • father of the field of statistical process quality control • 50 years later: not yet implemented in health care CMA Ottawa October 2007

25. “Wall of Ignorance” CMA Ottawa October 2007

26. Platitudes? You can’t manage what you can’t measure You get what you measure “Don’t ask how many (health care) events per pound; ask how much health per pound.” D. Berwick, BMJ 2005 CMA Ottawa October 2007

27. Vision – Coherent, Integrated Statistical System Broad Summary Indicators Health Accounts / Simulation Models Regional Indicators / Planning Info Facility Management Information / Unit Costs Basic Encounter Data / Health Surveys CMA Ottawa October 2007

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29. Hospital 65+ Patient Co-morbidity based on 676,508 hospital inpatient discharges across 10 provinces in 2001/2 CMA Ottawa October 2007

30. The SNA Approach(es), or“Let us Assume…” Economics • “Measures of productivity growth constitute core indicators for the analysis of economic growth.” • “desirable characteristics of productivity measures (are defined) by reference to a coherent framework that links economic theory and index number theory … much of the underlying methodology relies on the theory of production and on the assumption that there are similar production activities across units of observation (firms or establishments).” from “Measuring Productivity, OECD Manual”, 2001 CMA Ottawa October 2007

31. Definition – Productivity(“standard” economics and SNA) • the economy has myriad productive agents (firms) • each of whom uses inputs = total capital services + total labour services (factors of production) • to produce outputs (goods and services) summing to GDP • everything is measured in $ -- with the total being (conceptually) the sum of unit prices x quantities • but over time, prices (p’s) change, and this is not “real” • and quantities (q’s) change e.g. in terms of “quality” • to measure productivity, time series of outputs and inputs are constructed • taking out “pure” price changes, and • adjusting for improvements in quality • so that  productivity =  output – sum { inputs } CMA Ottawa October 2007