Measuring Health Outcomes: Capturing Complexity

1. Measuring Health Outcomes:Capturing Complexity NatStats Conference 2008 Dr Penny Allbon Director AIHW

2. The complexity is growing

3. How is the information environment responding • Indicators explosion • Data sets explosion

4. Linked data sets growing • Ehealth looming • Personal record control emerging

5. key drivers for measuring health outcomes into the future • Assess how healthy we are and how this is changing – monitoring and surveillance • the holy grail of attribution (health research) • the drive for effectiveness and efficiency of services • Improve the outcomes of care and treatment • the individual’s desire to better understand and control their own health

6. Australia compares well among OECD

7. Broad cause mortality trends, Australia

8. Leading burdens of disease

9. Burden of disease in 2003 by major disease group, subdivided into that portion mostly unavoidable, that portion due to 14 burden of disease risk factors, and other treatable or preventable burden Source: AIHW & University of Queensland

10. Projected health expenditure ($ billion 2003 prices)

11. What’s the impact of obesity? • Australia experienced significant increases in obesity in the 2 decades to 2003 when heart disease was declining • From the Harvard Nurses study we might conclude that increasing BMI means coronary heart disease is decreasing at a slower rate than it would otherwise.  

12. Cancer survival improving

13. Cancer Data Improvement • Cancer Data Clearing House (AIHW) Maintenance function - Improving consistency, keeping up-to-date, reporting • Safety &quality • volume/outcomes analysis? • Recording data on the staging of cancer • Outpatients – chemotherapy, radiotherapy. Slow, can this be given priority?

14. Potentially preventable hospitalisations (PPH) by remoteness of patient, 2005–06

15. Push the basics • Keep basic order and cooperation – standards, business rules, frameworks, metadata expansion • Common concepts and definitions

16. Facilitate re-use • Working together and coordinating effort – within a collaborative framework • Central portal of what’s available? Use of Clearing Houses?

17. Think about priorities • Do we need a process to prioritize the information requirements for the health outcomes agenda?

18. Balance privacy/access • Be pro-active in educating the public about the usefulness of the information

19. More data linkage • continue to build the capacity to link data – securely and without scaring the horses. From stovepipes to integrated systems

20. Exploit ehealth well • Get the statistical capacity of ehealth set up for maximum benefit Statistical purpose Information for analytical purposes including public health and policy planning, safety initiatives, disease detection, research and education Statistical benefits Better planning and demand management Better epidemiology and public health

21. Patient (IHI) interacts with clinician Clinician raises a natural language and/or form-based record Human coding assisted by guidelines Terminology Map Unit records with coded values Aggregate reports eg. NMDS, device registry, patient records possibly linked by IHI with consent Classifications Map Clinician or coder raises a terminology or rule-based record Machine coding according to maps Information for research and analysis: hybrid

22. Simple truths for a complex world • Profile • Institutional strength • Expertise