As healthcare providers wade through hundreds of often burdensome reportable quality measures, a new National Quality Forum study offers guidance on reducing variation and repetition.
In 2013, NQF commissioned a report from Bailit and Associates that found 1,367 quality measures in use across 48 different state and regional programs. Of them, 509 were distinct, but more than 800 overlapped or had similar focus, with one or more variations in the specifications, the agency said.
"Quality measures are essential building blocks in large-scale public- and private-payer efforts to reform the nation's healthcare system," said Helen Darling, NQF's interim president and CEO, in a statement. "But slightly different versions of the same measure contribute to waste through reporting burden for providers and make performance comparisons more difficult."
NQF's Variation in Measure Specifications project, funded by the U.S. Department of Health and Human Services, convened a 16-member panel of different stakeholders – experts in measure development, health informaticists, provider groups, payers and others – in a bid to better understand how measures are put to work, seeking a common understanding of the key terms, concepts and measure components.
The project identified many reasons for measure variation, such as varying end-user preferences, changes to the evidence underlying a measure or implementation challenges such as access to needed data.
It also found that a lack of awareness of existing measures often leads to unintended variation. To help combat this, the new report calls for the development of a comprehensive database of measures that are under development and in use to improve awareness of measures and any variants among those developing or using measures.
"We have an urgent need to focus on the measures that really matter for quality improvement," said NQF's Chief Scientific Officer Helen Burstin, MD. "To make care better for patients, in addition to reducing variation in measures, it's also important that we eliminate measures that are duplicative, ineffective, or that have reached the limits of their usefulness."