The Measures Application Partnership
Recently, the National Quality Forum (NQF), the steward of the Measure Applications Partnership (MAP), released its final, pre-rulemaking report to the HHS on February 1st. Section 3014 of the ACA mandated the creation of the “pre-rulemaking process,” which includes the annual public release on December 1 of quality measures under consideration for HHS programs, as well as sufficient time for multi-stakeholder comment on the proposed measures. HHS contracted with the NQF to convene the multi-stakeholder group (named MAP by NQF) to provide analysis and strategic guidance on performance and public reporting measures, including on increasing alignment within HHS and between public and private payers. The pre-rulemaking report is one of several reports created by MAP to improve and streamline quality measurement and reporting. The pre-rulemaking report provides HHS with analysis and feedback for over 350 measures under consideration for almost 20 of its programs.
MAP’s work begins to lay the foundation for advancing quality measurement and reporting. As eQMs are developed and gradually replace more traditional measures, measure alignment will become paramount. To calculate and report an eQM requires the ability to capture structured data, extract those data elements from multiple sources within the EHR, and then run a measure logic engine to apply the rules of the measure. This is a complicated process fraught with many challenges and requiring appreciable investments by providers and EHR vendors. The life-cycle of these measures (development, validation, testing and programming of measure reporting tool) does not allow for rapid adaptation to provider workflow or the rapid incorporation of changing evidence.
Moreover, vocabulary standards for eQMs are currently being evaluated. The Office of the National Coordinator for Health IT (ONC) is working with NQF on developing the vocabulary standards for the electronic specifications for these eQMs. One of their goals is to create a standardized model (the Quality Data Model) to turn measure specifications into computable value sets, which then can be used for quality measurement.