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Importance to measure and report criterion

The Importance criterion is the extent to which the specific measure focus is evidence-based and important to making significant gains in healthcare quality where there is variation in or overall, less-than-optimal performance. National Quality Forum. (2021). Measure evaluation criteria and guidance for evaluating measures for endorsement.

Industry standard modeling technique

Industry standard modeling techniques use a standard visual approach to facilitate communication related to business models and software design. For example, Unified Modeling Language (UML) uses integrated diagrams to specify and document the structure and dynamic behavior of information system and software artifacts.

Informatics Activities

Informatics activities include tasks related to knowledge engineering and technical development of clinical practice guidelines artifacts (L2 to L4). These may include, but not limited to developing use case diagrams, flow diagrams, and decision trees; identifying relevant data elements, FHIR profiles, terminologies, value sets, libraries, rules, and logic representations; writing software code (e.g., for clinical decision support); and developing comprehensive test cases and testing resources.

Integrated process

With an integrated process, steps related to guideline development, informatics activities, implementation, evaluation, and communications are conducted in parallel, insofar as possible, rather than in sequence.

Intermediate outcome measure

An intermediate outcome measure is a measure that assesses the change produced by a healthcare intervention that leads to a long-term outcome.

Inverse measure

An inverse measure is a measure where a lower performance rate is better. Therefore, a zero performance rate for these measures is a good score. For example, the National Healthcare Safety Network calculates most healthcare-associated infections (HAIs) as a standardized infection ratio (SIR). The SIR compares the actual number of HAIs (the numerator) with the predicted number based on the baseline U.S. experience (e.g., standard population), adjusting for several risk factors found to be most associated with differences in infection rates. The goal is to have the numerator equal to or very close to zero thereby having an SIR equal to or very close to zero.