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Blueprint Measure Lifecycle

Measure Testing

This section provides an overview of the types of testing needed to assess measure evaluation criteria and outlines the process for development, implementation, and reporting of test plans, results, and associated artifacts. The content in this section is not meant to be prescriptive or exhaustive. Measure developers can use testing approaches other than what this section describes. Measure developers should always select the best method(s) for the measure under development and provide empirical evidence for the importance, feasibility, scientific acceptability, and usability and use. Measure developers should ensure they report their findings and rationales in their documentation, such as the Measure Information and Justification Form and Instructions.

Measure testing is an iterative process conducted concurrently with measure specification. Iterative testing provides measure developers an opportunity to refine draft specifications before finalization; augment or reevaluate earlier judgments about the measure’s importance; and assess feasibility, usability, and scientific acceptability of the measure.

Measure testing enables a measure developer to assess suitability of the quality measure’s technical specifications and acquire empirical evidence to help assess strengths and weaknesses of a measure with respect to the CMS consensus-based entityPartnership for Quality Measurement Endorsement and Maintenance. To evaluate a measure, the measure developer should use information gathered through measure testing in conjunction with expert judgment. Measure testing refers to evaluating the draft specifications of quality measures, including components of the quality measures, such as the data elements, instruments, and performance score.

Testing and Evaluation for Special Measures

The moniker "special measures" indicates the measure developer may need to assess different measure aspects or consider additional factors. For example, while measure developers assess all measures for feasibility, data element feasibility is a major focus in testing and evaluating electronic clinical quality measures (eCQMs). To assist in assessing an eCQM’s feasibility, the measure developer needs to include the eCQM Feasibility Scorecard, not only as part of the testing plan, but also during feasibility testing as changes happen. For the measure testing summary related to an eCQM, documentation must show 

  • Evidence of testing with at least two different electronic health records
  • Quality Data Model data elements and the feasibility ratings of those elements

Another example of special measures is composite measures. Because composite measures include component measures, there are additional considerations when testing and evaluating composite measures. For more information on testing and evaluating special types of measures, refer to the supplemental materials describing special measures:

Measure Lifecycle

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  • Determine the measure ingredients and calculation formula
  • Collect input and testing findings on initial measure specifications to aid in refinement
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  • Includes annual update and comprehensive reevaluation every 3 years
  • Early maintenance review may be necessary
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  • Identify ideas or topics that are most important to interested parties and address a gap in measurement
  • Scan the environment
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  • Collect qualitative and quantitative data to establish whether the measure meets evaluation criteria
  • Iterate with Measure Specification
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  • Pre- and rulemaking processes
  • Collect user input on initial measure specifications to aid in refinement
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