Protocol 5 Validation of Encounter Data Activity 3

Protocol 5:Validation of Encounter Data

Activity 3

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Protocol 5: Validation of Encounter Data

Protocol 5: Validation of Encounter Data

Protocol 5: Validation of Encounter Data

• In the first lesson, we discussed the tasks associated with Activities 1 and 2.
• If you recall, for Activity 1, the EQRO reviews state requirements for encounter data.
• In Activity 2, the EQRO reviews the MCP's ability to collect encounter data by reviewing the MCP's most recent ISCA.

Protocol 5: Validation of Encounter Data

Protocol 5: Validation of Encounter Data

Protocol 5: Validation of Encounter Data

To complete Protocol 5, the EQRO must perform five activities for each MCP:

Activity 2: Review the MCP's capability

Activity 1: Review state requirements

Activity 3: Analyze electronic encounter data

Activity 4: Review medical records

Activity 5: Submit findings

Protocol 5: Validation of Encounter Data

Activity 3: Analyze Electronic Encounter Data

Activity 3: Analyze Electronic Encounter Data

• Activity 3 is the core function used to determine the validity of the encounter data.
• When the EQRO has completed the steps within this activity, it should know whether the data are complete, of high quality, and can be used for analysis of quality, access, program integrity monitoring, among other critical state activities.

Protocol 5: Validation of Encounter Data

Activity 3: Analyze Electronic Encounter Data

Activity 3: Analyze Electronic Encounter Data

• If the EQRO cannot confirm the quality of the data after completing this activity, it should not proceed to Activity 4, the Medical Record Review.
• Instead, the EQRO should work closely with the state or plans to determine underlying problems or acquire additional information to determine the quality and usefulness of the data submitted.
• Difficulties completing this analysis may need to be summarized for the state to document serious data quality issues.

Protocol 5: Validation of Encounter Data

Activity 3: Analyze Electronic Encounter Data

Activity 3: Analyze Electronic Encounter Data

• The EQRO uses the information obtained from these analyses, the ISCA tool, follow-up interviews, and state edit results to assess the completeness and accuracy of the MCP’s encounter data.
• The results of this activity will help develop a long-term strategy for assessing the quality of the encounter data.
• The EQRO will be able to design targeted validation strategies to identify problem areas requiring resource intensive medical record review.

Protocol 5: Validation of Encounter Data

Activity 3: Analyze Electronic Encounter Data

Activity 3: Analyze Electronic Encounter Data

For this activity, the EQRO should perform the following steps:

Step1

develop a data quality test

Macro-analysis of data integrity

Step 2

Step4

Step3

Micro-analysis of analytic reports

compare findings to benchmarks

Protocol 5: Validation of Encounter Data

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

The EQRO uses the information collected from Activities 1 and 2 to develop a data quality test plan. The plan should:

Account for front-end edits built into the MCP’s data system so that it focuses on issues that the MCP may have missed or allowed for other reasons

Specify the areas to be tested and the expected results

Protocol 5: Validation of Encounter Data

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

What is a "Front-End Edit?"

• Encounter data are only useful if they are complete and accurate. To ensure quality, encounter data needs to be analyzed for quality.
• Typically, this involves the use of "front-end edits" to identify errors. A front-end edit flags low quality data.
• Edits that set thresholds too high or low may create high rejection rates and confusion about why the system is rejecting records.

Protocol 5: Validation of Encounter Data

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

What is a "Front-End Edit?"

These are common errors that may be detected in a front-end edit, and result in rejected encounter claims:

Duplicate claims: State systems usually have a front-end edit to flag encounters that appear to be duplicates

Incorrect data: Beneficiary information does not match, or is missing in state's enrollment files

Missing NPIs: The provider's National Provider Identifier (NPI) is not found in the state's "roster" list

Other coverage: The beneficiary has third-party insurance that must be billed first

Protocol 5: Validation of Encounter Data

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

Developing a Data Plan: Missing Encounter Data

The EQRO should develop a data plan that addresses the extent of missing encounter data.

Overall data quality issues

Problem: The EQRO needs to determine the type and amount of encounter data missing

Comparison: The EQRO should compare front-end edits with typical data on encounters for similar populations

Investigation: The EQRO should investigate front-end edits and determine how much data is missing

Protocol 5: Validation of Encounter Data

Step 1: Develop a Data Quality Test Plan Based on Data Element Validity Requirements

Developing a Data Plan: Missing Encounter Data

The EQRO should address the following questions when developing a data plan:

Question: What types of encounters may be missing?

Question: Were there any issues during data submission?

Question: What are the data quality issues?

Overall data quality issues

Protocol 5: Validation of Encounter Data

Step 2: Encounter Data Macro-Analysis

Step 2: Encounter Data: Macro-Analysis

• Steps 2 and 3 of Activity 3 are closely related.
• When the EQRO reviews the data for accuracy and completeness, it conducts both macro- and micro-analyses.
• Step 2 describes the macro-analysis, while Step 3 describes the micro-analysis.

Protocol 5: Validation of Encounter Data

Step 2: Encounter Data: Macro-Analysis

Step 2: Encounter Data: Macro-Analysis

For the macro-analysis, the EQRO should:

Analyze and interpret data in specific fields

Check the data for volume and consistency

Protocol 5: Validation of Encounter Data

Step 2: Encounter Data: Macro-Analysis

Step 2: Encounter Data: Macro-Analysis

The EQRO should analyze data in submitted fields without duplicating the state's edit checks. When interpreting data, an EQRO should consider these questions.

Question #1

Question #4

Question #2

Question #5

Question #3

Question #6

08

04

Protocol 5: Validation of Encounter Data

Step 3: Encounter Data: Micro-Analysis

Step 3: Encounter Data: Micro-Analysis

Examples of analytic reports that can detect broader data quality issues are:

Checks by provider types

Reasonability tests

Analyses by DOS vs. adjudication dates

Protocol 5: Validation of Encounter Data

Step 3: Encounter Data: Micro-Analysis

Step 3: Encounter Data: Micro-Analysis

Examples of analytic reports that can detect broader data quality issues are:

Analyses by demographic group or subpopulation

Relational analyses by service type or episodes of care

Analytic questions

Protocol 5: Validation of Encounter Data

Step 4: Compare Findings to State-Identified Benchmarks

Step 4: Compare Findings to State-Identified Benchmarks

• Benchmarks set standards for expected utilization volume, and may vary depending on the populations enrolled in MCPs.
• In constructing reports and benchmarks, it is important to ask the users of the data what they need to know, with what degree of confidence, and whether it varies by plan, beneficiary group, or type of service.

Protocol 5: Validation of Encounter Data

Step 4: Compare Findings to State-Identified Benchmarks

Step 4: Compare Findings to State-Identified Benchmarks

Benchmarks may be obtained from these sources:

State encounter data

Historical FFS data

Plans' financial reports

National data sources

HEDIS benchmarks

Protocol 5: Validation of Encounter Data

Step 4: Compare Findings to State-Identified Benchmarks

Step 4: Compare Findings to State-Identified Benchmarks

• For example, this data table displays the average number of days from the last service date to the payment date.
• As you can see here, the average days from billing date to paid date for professional encounters for this MCP were 80.3 days. The EQRO compares the average to the baseline, which is 106.5 days.
• The EQRO has provided a reference to demonstrate that the MCP is performing above average compared to the benchmark.

Protocol 5: Validation of Encounter Data

Step 4: Compare Findings to State-Identified Benchmarks

Step 4: Compare Findings to State-Identified Benchmarks

• Comparison data may require investigation. The EQRO may need to understand why there is a significant difference in the data.
• For example, emergency department utilization may be lower in managed care than in FFS plans. If there is a large swing in utilization. or differences from the benchmark may indicate incomplete or erroneous data.
• The differences may be attributed to an emergency, such as a natural disaster, that may account for the unusual changes in utilization.
• The EQRO should determine if further analysis is needed, and discuss findings with the state to assess any underlying factors.

Protocol 5: Validation of Encounter Data

Knowledge Check

Protocol 5: Validation of Encounter Data

Knowledge Check

Protocol 5: Validation of Encounter Data

Knowledge Check

Protocol 5: Validation of Encounter Data

Knowledge Check

Protocol 5: Validation of Encounter Data

Congratulations!

Congratulations!

You have completed Protocol 5: Activity 3

You are now ready to move onto the next lesson, Protocol 5, Activities 4 and 5. If you have any comments or questions about this lesson, please leave us a message in the chat boards!

EQRO Division Trainer: Michelle Hoover

mhoover@qsource.org