From simply inaccurate to complex and inaccurate: complexity in standards-based quality measures

Quality measurement has been slow to make a major impact in health care. Initial measures were too simple to affect outcomes of importance. Incentive programs such as Meaningful Use encourage better measures, but in process may become more complex. We evaluated the measures selected for Meaningful Use in two ways: we counted unique concept identifiers, taxonomies, and aggregated concepts as measures of complexity; and we surveyed informatics professionals to assess difficulty. There were 20,316 unique concept identifiers, 35 taxonomies, and 317 aggregated concepts across the 45 measures. Half the respondents reported measures at least moderately difficult. The number of identifiers was associated with fewer implementations (r=-.37); rating-of-difficulty was associated with more taxonomies (r=.24). The impact on accuracy may be substantial when moving to measures intended to be more relevant to clinical outcomes but requiring the use of more taxonomies, unused structured concept identifiers, or concepts only in free text fields.