The impact of a measure, now called High Priority by the CMS consensus-based entity (CBE), is when the measure topic addresses a specific national health goal or priority; affects large numbers of patients; is a leading cause of morbidity/mortality; high resource use and severity of patient/societal consequences of poor quality. For patient-reported outcomes (PROs), there is evidence that the target population values the PRO and finds it meaningful.
Impact of a Measure (Importance Subcriterion)
The importance criterion is the extent to which the specific measure focus is important to making significant gains in health care quality (e.g., safety, timeliness, effectiveness, efficiency, equity, patient centeredness) and improving health outcomes for a specific high-impact aspect of health care where there is variation in or overall poor performance.
Inter-Rater (Inter-abstractor) Reliability Testing
Inter-rater reliability testing assesses the extent to which observations from two or more human observers are congruent with each other.
An intermediate outcome is a measure that assesses the change produced by a health care intervention that leads to a long-term outcome.
Internal Consistency Reliability Testing
Internal consistency reliability testing is testing a multiple item test or survey to assess the extent the items designed to measure a given construct are inter-correlated. Pertains to survey type measures and to the data elements used in measures constructed from patient assessment instruments.
Intra-class correlation refers to correlations within a class of data (for example correlations within repeated measurements of weight), rather than to correlations between two different classes of data (for example the correlation between weight and length). (Liljequist, Elfving, & Skavberg Roaldsen, 2019)
Inverse measures are measures where a lower performance rate is better. 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 (i.e., the numerator) with the predicted number based on the baseline U.S. experience (e.g., standard population), adjusting for several risk factors that have been 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 a SIR equal to or very close to zero.