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Data for Decision-Making Processes: Linking Data to
Quality Improvement Initiatives
Bisma Sayed, M.S.W.University of MiamiDepartment of Sociology
John Dow, M.S.W.South Florida Behavioral Health Network
Understand the value of utilizing data for decision-making
Determine what should be measured and what data elements should be used
Data analysis and interpretation
Recognize limitations
Validate findings using other data sources
Objectives
The recent recession coupled with health care reform has had cascading consequences on behavioral health care service delivery in Florida.
Current funding limitations and budget cuts have increased the urgency for cost-effective and efficient delivery of behavioral health care services.
How can behavioral health care organizations lower cost, raise quality, and still offer accessible services to increasing numbers of consumers?◦ Meet standards◦ Coordinate◦ Demonstrate outcomes◦ Eliminate duplication◦ Produce reportable, effective, and sustainable
results
Behavioral Health Care Service Delivery
Quality Improvement vs. Quality Assessment
Quality Improvement
Quality Improvement Processes allow organizations to analyze current practices, identify strengths and weaknesses, set goals, and monitor progress
Quality in the behavioral health care setting may be defined as the ‘extent to which a health care service or product produces a desired outcome’
Behavioral Health Care: Quality Improvement Initiatives
Quality of care measures◦ Effective◦ Appropriate◦ Safe◦ Efficient◦ Responsive◦ Accessible◦ Continuous◦ Capable◦ Sustainable
Quality Improvement Initiatives
Examine current organizational functioning
Identify target problems
Identify quality of care measures
Identify goals (short term or long term)
Measure baseline performance on quality measures
Quality Improvement Processes
Develop and conduct interventions designed to affect the targeted measures
Repeat measurement of performance based on quality indicator
Document and disseminate results.
Quality Improvement Processes
“If you do not measure it (or cannot measure it), it didn’t happen.”
How can we measure it?
Quality Assessment
Data provides the foundation for quality improvement initiatives◦ Timely◦ Transparent◦ Presented with humility◦ Based on past lessons learned◦ Accountability◦ Presented with compassion and understanding
DATA
The shift to evidence based care coupled with increased technological and statistical advances have resulted in an explosion of data. . .
. . . However, this knowledge remains to be harnessed
The influx of data has led organizations to report data, rather than analyze data.
Data Reporting Data Analysis
Behavioral Health Care: Data and Quality Improvement Initiatives
Data
Information
Knowledge
Decision
Action
Quality Improvement and Data: A continuous relationship
Quality Improvement◦ What is happening?◦ What factors affect delivery◦ How can we influence them
Reactive and Proactive
We need data to guide this.
“Data helps to push improvement (by identifying problems) and pull improvement (by identifying opportunities)”
The link between QI and Data
Facts and statistics collected together for reference or analysis
What is data?
•Surveys• Literature
Reviews•Key
informants•Surveillance
data• Focus Groups
Develop overall goal for improvement
Identify objectives using quality of care measures
Identify target populations
Identify data to be collected
Why use Data for Decision Making?
Determine data sources and/or collection method.
Determine data storage, management, and analysis techniques.
Analyze and Interpret Data
Utilize data for decision-making
How to use Data for Decision Making?
Data Collection and Management
Plan◦ Consider scope and purpose◦ Target Audience
Learn ( Do not reinvent the wheel)◦ Literature Reviews◦ Other sources of data
Test◦ Pilot-test on a smaller scale to identify challenges
Team work◦ Involve and Integrate
Data Collection and Management
Internal Data External Data Administrative or Clinical
Regardless of source of data or type of data, it must be reliable and valid◦ What is reliability and validity?
Types of Data
Process mapping: (Who? How long? Steps? Costs?)
Brainstorm
Quantitative or Qualitative◦ Nominal◦ Ordinal◦ Interval◦ Ratio
Data collection techniques and tools
Surveys and questionnaires
◦ Ethical Standards
◦ Confidentiality and Anonymity
◦ Response Rates
◦ Existing Surveys
◦ Guidance
◦ Pilot test
Data Collection
What is your target population? ◦ Consumers? Their families? Providers?
Community?
Data Collection
Clear and Understandable◦ Specific◦ Not loaded or leading◦ No double barreled question◦ No jargon or acronyms
Allow choice of only one option
Provide reasonable ranges of variation in the response options
Data Collection: Survey Questions
Social Desirability Bias
Target towards population◦ Appropriate for age, culture and literacy
Include adequate demographic information
Data Collection: Survey Questions
Why do we sample?
Sampling must be representative of your population
Selection bias
Sampling
Important step that can cause significant error if not done properly
Identify inconsistencies◦ For example, the mean age of adolescents
sampled across the nation is 23.5. The range is 13-56.
◦ Why do we have a 56 year old adolescent?
Data entry, checking, and cleaning
Spreadsheet programs◦ Reporting, not analysis
Database programs◦ Database changes – Store data with reports◦ Reporting, not analysis
Statistical Programs◦ Analysis
Storing and Managing Data
Data Analysis
Understand the variables◦ Categorical and numerical variables
Frequency Distribution Median and Percentile Counts and Sums Measures of central tendency Measures of variability
Analyzing Quantitative Data
Measures of Central Tendency◦ Mean◦ Median◦ Mode
Data Analysis for Numerical Variables
Range
Standard Deviation
What does this tell you about your population?
Measures of Variability
What is the goal of data analysis in QI?
Descriptive Analyses and Measures of Variation are useful, but. . .
Inferential statistics can add to the power of your conclusions.◦ Examine Relationship/Estimate size of difference◦ Confidence Intervals◦ Tests of statistical significance
Statistical Analyses
Correlation Analysis◦ Correlation Coefficient: Pearson Product Moment
Correlation Coefficient (r)
Scatter plots◦ Linear Relationships◦ Non-Linear Relationships
Correlation does not equal causation
Statistical Analyses
Nominal Level Data: Non-Parametric Tests◦ Chi Square◦ Cramer’s V/ Contingency Coefficient/Others
Numerical Data: Parametric Tests◦ T-tests (independent or dependent)◦ ANOVA◦ Regressions
Confidence Intervals◦ What are they? ◦ How can they be used?◦ Sample size matters
Statistical Analyses
When you combine your sample value with the margin of error, you obtain a confidence interval.
The confidence interval is the level of confidence that the sample value represents the true value as seen in the overall population.
Statistical Analyses
For example, the waiting time for appointments for clients referred to your clinic might be expressed as a mean of 13.5 weeks with a 95% confidence interval of 11.6 to 15.3 weeks (95% CI 11.6-15.3).
This means that you expect your population on average would wait between 11.6 and 15.3 weeks for an appointment.
The p value is the probability that the difference you have observed in your study samples could be due to chance.
Smaller p value = lowered probability that results are due to chance
Statistical Significance
Data Analysis: Statistical Significance
The size of the p value depends on the size of the sample, so be aware of possible mistakes that can occur in interpreting these values.
Statistical significance does not mean clinical significance.
Data Analysis: Statistical Significance
Keep it simple Consistent units Decimal Points Include raw numbers and percentages Always include n Identify missing data Group data appropriately
Presenting Data: Tabulating Data
Keep it simple Avoid complexity Clear headings Scale Carefully Raw numbers and percentages Always include n Group data appropriately
Presenting Data: Graphs
Basic population characteristics: Pie chart; bar graph
Measures of magnitude including comparisons: Bar chart or box plot
Presenting Data: Graphs
Frequency: Pie chart; bar chart
Trends over time: Line graph
Distribution of Data: Histogram; Scatter plot
Relationship between two things: scatter diagram
Presenting Data: Graphs
Data Analysis and Interpretation
Whether you are collecting your own data or relying on external sources, there is a difference between compiling/reporting data and analyzing data◦ Data : petabytes◦ Reports : terabytes◦ Excel : gigabytes◦ PowerPoint : megabytes◦ Insights : bytes◦ One business decision based on actual data:
Priceless1
Data Analysis and Interpretation
What is the problem?
What can you improve?
How can you improve?
Have you achieved improvement?
Have we sustained improvement?
Data Analysis and Interpretation
State and national datasets provide important information about key health indicators and can serve as basis for comparison.
However, we must be careful in interpreting and analyzing this data.◦ Understand limitations
Understand how data is presented◦ Mean, Median, Mode◦ Raw sums or percentages
Data Analysis and Interpretation
Level of variables◦ Individual◦ Community◦ State
State level data can help guide decisions, but you must examine individual data in your community to determine if the problem exists at a local level.
Data Analysis and Interpretation
Data Analysis and Interpretation: Ecological Fallacy
What does data drive?◦ Assessment◦ Priority setting◦ Allocation of resources◦ Directives to staff and community◦ Evaluation of clinical outcomes ◦ Basis of QI for providers◦ Feedback◦ Sets the groundwork for comprehensive planning
Using Data for Decision-Making
Assess performance and identify gaps
Understand the needs and opinion of stakeholders
Prioritize problems and improvement projects
Establish overall aims and targets for improvement
Using Data for Decision-Making
Establish a clear case for the need for improvement.
Data assists in sustained improvement: feedback to reinforce change and demonstrate benefits.
Using Data for Decision-Making
Thank you