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Rhode Island Quality Institute Using Data for Continuous Improvement Using Data for Continuous Improvement Elaine Fontaine, Director, Data Quality and Analytics
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The Foundations of Improvement Science Plan – What Are We Trying to Accomplish? How Will We Know If A Change Is An Improvement? What Changes Can We Make That Will Result In Improvement? … Do – Study – Act
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What Are We Trying to Accomplish? Set SMART goals: S - specific, significant, stretching M - measurable, meaningful, motivational A - agreed upon, achievable, acceptable, action-oriented R - realistic, relevant, reasonable, rewarding, results-oriented T - time-based, time-bound, timely, tangible, trackable
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How Will We Know If a Change Is an Improvement? Outcome measures What is the intermediate or final desired result? Process measures What are the steps in the processes that support the system performing as planned? Balancing measures Are changes designed to improve one part of the system causing new problems in other parts of the system?
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Measure Considerations Units of Analysis Patient Provider Site Practice Academic Department ACO Standard vs. Homegrown Measures Availability of Benchmarks Type of Measure Counts Rates Aggregated Scores
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Understand Data Quality Requirements Intervention Feedback Accountability
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Understand Your Underlying Data Availability / Feasibility of Collection Data Quality Complete Valid Consistent Accurate Timely
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Profile to Know the Limitations of your Data For Every Data Element Data Type Quantitative Qualitative Categorical Free Text Null Values % Complete % In Range (Min, Max) Mean, Mode, Standard Deviation, Skewness, Sum % Discrete Text Patterns, Misspelling, Varying Value Duplicates
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Profile to Know the Limitations of your Data Between Data Elements and Tables Relational Integrity % Unique Identifier Match Collecting Data Over Time Reasonableness Trending Unexpected Variation Did Something Break in the Data Process? Basic Statistics Paired t-test for Change Over Time
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Use the Right Chart for the Right Purpose Run Charts and Statistical Process Control Often best for QI Projects Bar and Column Charts Categorical Data During A Single Period. Comparison Of The Actual Versus The Reference Amount Shows The Number Of Observations Of A Particular Variable For Given Interval (Histogram) Bars For Long Labels Charts For Quantitative Data By Category Stacked Bar/Chart Pie Charts Scatter Plots Radar Chart/Spider Graph
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Avoid These Common Visualization Mistakes Wrong Visualization for the Data LABELS! Missing Unclear Misleading Numbers Don’t Add Up Axis Crop/Scale Reverse Unintuitive Ordering Too much data Correlation and Causation Impossible Comparisons Missing Annotations No headline
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Examples 12 MeasureQ1Q2Q3Q4Q1Q2 A1c Control757677909193 LDL Control708090808583 BP Control788185879296
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Examples 13 MeasureQ4 2015 National 90th% A1c Control7578 LDL Control9089 BP Control9585 Eye Exam3565 Nephropathy6878
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Pulling It All Together: Interpret and Communicate Your Results Know Your Audience Be Transparent and Acknowledge Limitations Keep It Simple, but Be Prepared to Provide Details Provide Answers to These Questions - Did the Change Result in Improvement? Did Something Else Break in the Process?
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Important Take Aways Do Begin with the End in Mind Understand and Acknowledge Data Limitations Keep Your Analysis as Simple as Possible, but Not Simpler Provide Clear, Understandable Visualizations Don’t Let Perfection Be the Enemy of the Good Confuse Data for Quality Improvement with Data for Incentive Payments
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Questions?
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