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Feed-back on Belgium Experiments

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1 Feed-back on Belgium Experiments
Brant E. Fries, Ph.D. University of Michigan, USA Brussels, Belgium February 19, 2008

2 New Technology of Assessment
Link assessment directly with clinical care Turn data into information Multiple uses of data Careful design and scientific testing Cross-sector compatibility Cross nation comparisons

3 It’s not enough just to measure…..

4 Link assessment directly with clinical care
Primary goal: care of the individual Use Clinical Assessment Protocols (CAPs) triggers to target opportunities for better care content to design interventions New triggers better identify individuals who can benefit NOT automated care planning!

5 Turn Data into Information
Assessment data can be used for multiple purposes: Clinical care Case-mix measurement and payment Quality measurement and program evaluation Eligibility determination or program recommendation Program monitoring and policy research

6 Multiple Uses of Data Efficient, as only collected once
Focuses attention on proper assessment Offsetting incentives encourage accuracy

7 Careful Design and Scientific Testing
Design by clinical experts Cross-fertilization from other long-term care sectors Extensive testing: Reliability Validity International testing and experience Development of multiple applications Careful translation to other languages Cross-sector integration

8 Cross-Sector Compatibility
Goal of new interRAI “Suite” Assessment items consistent across instruments Common structure for all databases Allows comparisons between sectors Effective items used across sectors

9 Comparing Persons Served in Two Michigan Settings

10 Cross-National Comparisons
Practical experience from use in multiple nations Cross-national comparisons provide more accurate standards Only possible with standardized assessment

11 Lessons Learned Staff training Plan for staff turnover
Develop cadre of “trained trainers” Invest in computer-directed learning technology Establish web-based clinical “help desk” Create “Frequently Asked Questions” (FAQ) list; post and update frequently Set training requirements in regulation or contracts

12 Lessons Learned - 2 Develop “business intelligence” plan
Analyze desired uses of data How will stakeholders get timely information? Design reports for key audiences: Administrators Clinicians Policymakers Consumers? Fund adequate, multi-year budget Future data analysis: make or buy? All or above: at both government and provider level

13 Lessons Learned - 3 Insure timely data outputs
Include both clinicians and administrators in software design/ purchasing decisions Negotiate performance contracts with software vendors Set aside funds to create additional reports as need arises

14 Lessons Learned - 4 Don’t let the assessments become “paperwork”
Field needs strong, persistent message about importance of data quality from top leadership. Use the data in many ways: “Continuous Quality Improvement” (CQI) efforts Encourage users to identify effective “best practices” Invest in audit process to ensure assessment and data accuracy over time

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