1. HSAG Performance Improvement Projects Using Data to Develop Interventions and Statistical Testing to Evaluate Results Breakout Session #1 Florida EQR Quarterly Meeting June 18, 2008
Presented by:
Donald Grostic, MS
Associate Director, Research and Analysis Team
Yolanda Strozier, MBA
EQRO Project Manager

2. Intervention Cycle Framework
Data Collection (CMS Protocol Activity VI)
Data Mining and
Causal/Barrier Analysis
Statistical Testing and
Linking Intervention
to Outcomes
Evaluate
Identify
CMS
Activity
VII,VIII
CMS Activity
VII,VIII, IX, X
Implement
Plan
Three Tips
CMS
Activity
VII, VIII
CMS Activity
VIII
Steps for
Intervention

3. What does the intervention cycle have to do with CMS PIP Activities?
Identify
Plan
Implement
Evaluate
Activity 7
Assess the Improvement Strategy ☑
Activity 8
Review Data Analysis & Interpretation of Results
Activity 9
Reported Improvement is Real?
Activity 10
Sustained Improvement?

4. The ‘Identify’ Stage
Identify

5. Data Mining What is data mining? Answer:
Data mining is the process of sorting through large amounts of data and picking out relevant information.

6. Data Mining (continued)
What is data mining used for?
Answers:
Data mining is the statistical and logical analysis of large sets of data, looking for patterns of care, or service delivery that can aid decision making.
To identify and determine areas of non-compliance that will be analyzed during the causal/barrier analysis.

7. Data Mining vs. Data Analysis Plan
How does data mining differ from a data analysis plan?
Answer:
A data analysis plan includes calculating and comparing overall indicator rates between measurement periods using statistical testing.
Data mining will include analysis that goes beyond just calculating and comparing indicator rates between measurements.

8. Data Mining–Example PIP topic (clinical): Indicator:
Follow-up after acute care inpatient hospitalization.
Indicator:
The percentage of members with follow-up within 7 days after acute care discharge for a mental health diagnosis.

9. Data Mining Example Step One
Group the population or sample.
First, group members by county or ZIP code. For our example, the population breaks into three counties: County A, County B, and County C.

10. Data Mining Example Step Two
Calculate compliance and noncompliance for each county.
The percentage compliant and noncompliant by county are presented in the following table.
Question: Which county should you data mine further?
Percentage Compliant
Percentage Non-Compliant
County A
65%
35%
County B
County C
20%
80%

11. Data Mining Example Step Three
Identify groups where the majority of members are noncompliant.
Answer:
First we need to know how many members of the population are in each county.
Selecting County B will have the greatest effect on the compliance rate because it has the majority of the population and the second lowest compliance rate.
Percentage Compliant
Percentage Non-Compliant
Number of Members
County A
65%
35% 80
County B
220
County C
20%
80% 20

12. Data Mining Example Next Steps
Now that you have identified County B, what should you do next?
Answer:
Continue the process of grouping and selecting to find the group that will have the greatest effect on compliance.
For County B, you may consider grouping the data by PCP or facility next.

13. Data Mining Caution! Words of caution:
Grouping and selecting can be taken to a point where the groups selected may be too small to make an impact.
Always keep in mind the number of members affected in the selected group relative to the total population.
If there is difficulty identifying noncompliant groups or non-compliance is equally distributed among groups, you may be dealing with a systemwide issue.
Please keep in mind that data mining is a dynamic, iterative process that takes practice.
The more you data mine the better you will become at selecting groups that yield the best effect on rates.

14. Questions and Answers

15. What is a Causal/Barrier Analysis?
A causal/barrier analysis is:
A systematic process for identifying the problem.
A method for determining what causes the barriers.
A way to identify what improvement opportunities are available.
Causal/barrier analysis has also been called:
Root cause analysis

16. How do I perform a causal/barrier analysis?
Determine why an event or condition occurs.
What is the problem?
- Define the problem and explain why it’s a concern.
Determine the significance of the problem.
- Look at the data and see how the problem impacts your members and/or health plan.

17. How do I perform a causal/barrier analysis? (cont.)
3. Identify the causes/barriers.
- Conduct analysis of chart review data,
surveys, focus groups.
- Brainstorming at quality improvement
committee meetings.
- Literature review.
4. Develop/implement interventions based on identified barriers.

18. Causal/Barrier Methods and Tools
Quality improvement committees
Develop an internal task force
Focus groups
Consensus expert panels
Tools:
Fishbone
Control chart
Flow chart (process mapping)
Barrier/intervention table

19. Questions and Answers

20. The ‘Plan’ Stage
Identify
Plan

21. A Physical Health Example
What questions could be asked to drill down these causes?
What data are needed to identify the most crucial cause?

22. A Mental Health Example
Discharge planning
Client
Communication
Transportation
Community involvement
No follow-up appointment set at time of discharge
Time lag/claim data
Not client focused
Provider access
Culture change
Demographic information
What questions could be asked to drill down these causes?
What data are needed to identify the most crucial cause?

23. Interventions Checklist
Analyze barriers (root causes)
Choose and understand target audience
Select interventions based on cost/benefit
Implement interventions
Track intermediate results (optional)
Remeasure
Modify interventions as needed

24. Questions and Answers

25. The ‘Implement’ Stage
Identify
Implement
Plan

26. The ‘Implement’ Stage Three tips:
Observe and document whether the intervention is implemented as intended
Note any lesson(s) learned
Document any change(s) that may threaten the results between measurement periods
Methodology (e.g. definition of indicators, sampling)
Circumstances (e.g. merger, population, provider)

27. Questions and Answers

28. The ‘Evaluate’ Stage: Statistical Testing
Identify
Evaluate
Plan

29. Statistical Significance Testing
What is statistical testing and why do we use it?
Answers:
Statistical testing is calculating specific test statistics and associated p values to determine if an observed difference is a true difference and not due to chance alone.
The CMS Protocols require that statistical testing be used to prove that any improvement in rates is real.
Without statistical testing, a PIP would not meet the CMS Protocols.

30. Statistical Significance Testing
What type of statistical testing is appropriate for my PIP?
Answer:
Fisher’s Exact Test or Chi-square test for rates or proportions.
T test for means would be the appropriate statistical testing.

31. Statistical Significance Testing
What type of statistical testing is appropriate for this indicator?
Indicator A: The percentage of members with follow-up within 7 days after acute care discharge for a mental health diagnosis.
Answer:
Fisher’s Exact Test or Chi-square test for rates or proportions.

32. Statistical Significance Testing
What is the difference between Fisher’s Exact Test and a Chi-square test?
Answer:
Fisher’s Exact Test will provide the exact p value while the Chi-square test is an approximation of the p value.
As your numerators and denominators increase in size, the Chi-square test and Fisher’s Exact Test produce the same p value.
If in doubt about which test to use, use Fisher’s Exact Test.

33. Statistical Significance Testing
What type of statistical testing is appropriate for this indicator?
Indicator B: The average response from a member satisfaction survey where answers range from 1=satisfied to 5=dissatisfied.
Answer:
T test for means would be the appropriate statistical testing.

34. Statistical Significance Testing
How do I report statistical significance testing results?
Answer:
When using a Fisher’s Exact Test, Chi-square test or a t test, report the test used, its associated p value along with each indicator, and its numerator and denominator in tabular form.

35. Statistical Significance Testing
Indicator A: The percentage of members with follow-up within 7 days after acute care discharge for a mental health diagnosis.
Time Periods
Measurement Periods
Numerator
Denominator
Rate or Results
Industry Benchmark
Statistical Testing and Significance
CY 2003
Baseline 20 41
48.8%
60%
N/A
CY 2004
Remeasurement 1 27 51
52.9%
Fisher’s Exact Test
P value =
Chi-square test
P value =
NOT SIGNIFICANT AT THE 95% CONFIDENCE LEVEL

36. Statistical Significance Testing
Indicator B: The average response from a member satisfaction survey where answers range from 1=satisfied to 5=dissatisfied.
Time Periods
Measurement Periods
Numerator
Denominator
Rate or Results
Industry Benchmark
Statistical Testing and Significance
CY 2008
Baseline
253
100
2.53
N/A
STD DEV = 1.298
CY 2009
Remeasurement 1
371
113
3.28
STD DEV = 1.561
T-test
P value =
SIGNIFICANT AT THE 95% CONFIDENCE LEVEL

37. Statistical Significance Testing
If I use the entire population for my study, do I still have to do statistical significance testing?
Answer:
Yes. It is appropriate to do statistical
testing on the entire eligible population.

38. Reasons for Statistical Significance Testing on Entire Populations
CMS is interested in performance over time.
The population will continuously change over time.
The members who are studied in one year may or may not appear in the following years.
A population that is selected at one point in time is a sample from the true population that contains all members.
The entire eligible population for a measure in one year is a sample population drawn from a universe of “all years” or “all populations” that could be selected.
CMS has approved statistical testing on populations.

39. Questions and Answers

40. The ‘Evaluate’ Stage: Linking Intervention to OutcomesNo
Revise
Yes
Improved?
Identify
Evaluate
Standardize
Plan
Implement

41. The ‘Evaluate’ Stage: Linking Intervention to Outcomes
Threats to internal/external validity: Any environmental, organizational, methodological changes between measurement periods? No
Yes
Outcome: Improved
Intervention seems to be effective
Consider standardizing the intervention to subsequent measurement periods
Cannot ascertain if the improvement really is due to intervention
Investigate the relationship between the change circumstances and the outcomes
Outcome: No Change or Worsens
Intervention does not seem to be effective
Consider revising the intervention to subsequent measurement periods

42. The ‘Evaluate’ Stage: Linking Intervention to OutcomesNo
Revise
Evaluate
Improved?
Yes
Standardize
Identify QI
Implement
Plan

43. Questions and Answers