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

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

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?

The ‘Identify’ Stage Identify

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

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.

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.

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.

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.

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%

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

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.

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.

Questions and Answers

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

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.

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.

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

Questions and Answers

The ‘Plan’ Stage Identify Plan

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

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?

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

Questions and Answers

The ‘Implement’ Stage Identify Implement Plan

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)

Questions and Answers

The ‘Evaluate’ Stage: Statistical Testing Identify Evaluate Plan

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.

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.

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.

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.

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.

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.

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 = 0.8340 Chi-square test P value = 0.8517 NOT SIGNIFICANT AT THE 95% CONFIDENCE LEVEL

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 = 0.0002 SIGNIFICANT AT THE 95% CONFIDENCE LEVEL

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.

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.

Questions and Answers

The ‘Evaluate’ Stage: Linking Intervention to Outcomes No Revise Yes Improved? Identify Evaluate Standardize Plan Implement

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

The ‘Evaluate’ Stage: Linking Intervention to Outcomes No Revise Evaluate Improved? Yes Standardize Identify QI Implement Plan

Questions and Answers