Administrative Data and Health Policy:Examples and Lessons LearnedGraham Wood wardDirector of Planning, Reporting and EvaluationOntario Renal NetworkCanadian Research Data Centre NetworkOctober 24th, 2012

Policy Based Evidence Making

Graham Wood wardDirector of Planning, Reporting and EvaluationOntario Renal NetworkCanadian Research Data Centre NetworkOctober 24th, 2012

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

WHO AM I?

Who am I?

Who Am I?

BSc & MScZoology

study design and statistics

1996 - 2000Health data and analytics to support health policy

& planning

1991 - 1996Applied research for public

dental health programs

2000 – 2002, 2004-2006Health data and analytics to support health policy & planning (& politics)

2002 - 2004Research & health data and analytics to support health policy & planning

2006 - 2012Health system planning and policy

SOME EXAMPLES

Who “created” the data?

What’s going on in the background?

Lessons Learned

Good enough for the decision that needs to be made

politics

dataresearchfeasibility

Lessons Learned

Good enough for the decision that needs to be made

Talk to the front line!

Old Funding Method

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Based on activity level reporting of dialysis-related service volumes

Data SourcesData 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 ReportCKD 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

Number of Ambulatory Dialysis Treatments per Patient

20

Best Practice

LOW HANGING FRUIT

Dialysis Capacity Planning - Patient Travel

Source: ORRS April 30th, 2011 monthly patient census22

Capacity Planning

23

99%94%

86% 86%

115%

90% 90%86%

96%

71%

90%

108%

72%

83%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Occ

upan

cy R

ate

(%)

LHIN

Average Occupancy Rate by LHIN, 2009

General Lessons Learned

Working with Government

General Lessons Learned

A good data analyst is gold!

General Lessons Learned

Think people and systems, not data, records, statistics, or research

General Lessons Learned

Build and Value Strong Teams

“If everything in under control, you are going too slow!”

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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!

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

THANKSgraham.woodward@renalnetwork.on.ca