1. Administrative Data and Health Policy: Examples and Lessons Learned Graham Wood ward Director of Planning, Reporting and Evaluation Ontario Renal Network Canadian Research Data Centre Network October 24 th, 2012

2. Policy Based Evidence Making Graham Wood ward Director of Planning, Reporting and Evaluation Ontario Renal Network Canadian Research Data Centre Network October 24 th, 2012

3. Outline Who Am I? Some examples of why I like coming to work One area of low hanging fruit (at least for Ontario) General Lessons Learned

4. WHO AM I?

5. Who am I?

6. Who Am I? BSc & MSc Zoology study design and statistics 1996 - 2000 Health data and analytics to support health policy & planning 1991 - 1996 Applied research for public dental health programs 2000 – 2002, 2004-2006 Health data and analytics to support health policy & planning (& politics) 2002 - 2004 Research & health data and analytics to support health policy & planning 2006 - 2012 Health system planning and policy

7. SOME EXAMPLES

8. Who “created” the data?

10. What’s going on in the background?

13. Lessons Learned Good enough for the decision that needs to be made politics data research feasibility

17. Lessons Learned Good enough for the decision that needs to be made Talk to the front line!

18. Old Funding Method 18 Based on activity level reporting of dialysis-related service volumes

19. Data Sources Data Source Description & Application to Proposed Funding Framework Development Ontario Renal Reporting System (ORRS) * Database of all incident & prevalent chronic dialysis patients in Ontario. A superset of CORR. Used to identify & count all chronic dialysis patients & to monitor their modality & location of care. 2008 JPPC Report CKD micro-costing report recommending changes to reimbursement & funding model. Used to assess possible changes to service definitions & reimbursement rates. National Ambulatory Care Reporting System (NACRS) Database of ambulatory (emergency, day surgery, outpatient) care in all Ontario hospitals. Used to count ambulatory care received by chronic dialysis patients (primarily dialysis visits at this time). Discharge Abstract Database (DAD) Database of inpatient (acute, ALC) care in all Ontario hospitals. Used to count acute events involving chronic dialysis patients. Management Information System (MIS) Database of aggregate service & funding associated with mandated hospital functional centres. Used to track hospital dialysis unit expenditures. Web-Enabled Reporting System (WERS) Database of funded CKD services provided & reported by Ontario CKD Programs. Used to count & fund aggregate volumes of service. *Note: Prior to 2009/2010, data were reported for only half of Ontario’s prevalent patients within The Renal Disease Registry (TRDR). 19 RECORD LINKAGE

20. Number of Ambulatory Dialysis Treatments per Patient 20 Best Practice

21. LOW HANGING FRUIT

22. Dialysis Capacity Planning - Patient Travel Source: ORRS April 30 th, 2011 monthly patient census 22

23. Capacity Planning 23

24. General Lessons Learned Working with Government

25. General Lessons Learned A good data analyst is gold!

26. General Lessons Learned Think people and systems, not data, records, statistics, or research

27. General Lessons Learned Build and Value Strong Teams

28. “If everything in under control, you are going too slow!” 28 Lessons Learned “…never look down at the whirlpools below, focus on a fixed point at eye level and keep moving.” Catherine Gildiner – too close to the falls Health policy is not linear. It is an opportunistic process. Be ready to act fast!

29. There must be time to learn through play! BUT RESPECT PRIVACY!

30. THANKS graham.woodward@renalnetwork.on.ca