Using Data To Guide Continuous Improvement: Data Analysis

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Using Data To Guide Continuous Improvement Data
Analysis

• OSEP National Early Childhood Conference
• February 7, 2005
• Accountability and System Improvement
• Work Group

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Purpose of the Workshop

• To provide states and lead agencies with an
  experience in systematically analyzing data from
  multiple sources, in order to make informed
  decisions in identifying strategies for improving
  outcomes for infants, toddlers, preschoolers and
  their families and compliance.

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Outcomes for Participants

• Gain a deeper understanding of the role of data
  analysis in continuous improvement planning
• Learn how to turn data into meaningful
  information that can be used to improve services
  and results for infants, toddlers, and
  preschoolers with disabilities and their families

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OSEPs Accountability Strategy
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OSEPs Accountability Strategy
Focused Monitoring
High Risk
Continuous Improvement
System Verification
Inquiry Level I Information Review Level
II SEA/LA State Visit Level III SEA/LA and
Local Level State Visit
Intervention Technical Assistance Revision to
Annual Performance Report Required Targeted
Corrective Action Plan Sanctions
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Merging Reporting Requirements

• Monitoring Priorities and Timelines (Clusters,
  Probes)
• Targets
• Improvement Activities
• Timelines, Resources

State Performance Plan
APR

• Performance on Targets
• Explanation of Progress and Slippage
• Revisions to Activities, Timelines, Resources and
  Targets

Annual Performance Report
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Elements of a Continuous Improvement Process

• State Performance Planning
• Self-Assessing
• Improvement Planning
• Data Analysis
• Target Setting
• Future Activities/Strategies
• Improvement Plan Implementation
• Reporting-Annual Performance Report

Keep needs revisions?
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Data Analysis
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Data Analysis

• Compare present levels of system performance to
  baseline and targets
• to formulate educated guesses (hypotheses),
• to identify strengths and weaknesses, and
• to determine areas for improvement through a
  systematic examination of performance data.

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Why is systematic data analysis important?

• Check Assumptions
• Target Limited Resources
• Set High Expectations
• Heighten Accountability
• Focus on Continuous Improvement
• Examine Results

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Data analysis use - considerations

• Get ready what do you know already?
• Identify areas for comparison.
• Examine trends and relationships.
• Identify and define (possible) problem areas.
• Review and evaluate data to determine hypotheses.
• Develop potential improvement strategies.
• Evaluate how did the interventions work?
• What are alternate hypotheses?

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EXERCISE

• Part 1 Indicators Measures
• Part 2 Baseline/Trend Data
• Part 3 Analysis
• Part 4 Future Targets

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Wrap-up
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Why is this important?

• Focus improvement efforts
• Target scarce resources
• Improve results for children with disabilities

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Thanks to the Accountability and System
Improvement Work Group

• EspeciallyMarsha Brauen, Lynne Kahn, Jane Nell
  Luster, Kristen Reedy, Jim Henson, and Dick
  Zeller
• and
• OSEP StaffLarry Ringer, Rex Shipp,
• Rhonda Spence, and Maral Taylor