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Federal Register / Vol. 86, No. 164 / Friday, August 27, 2021 / Notices
48199

In late 2020, NQF solicited nominations to convene a TEP. This panel of experts will be finalized In early 2021 and will be charged with developing an environmental scan report that wlll review literature related to high quality PROMs and how they can affect the development of PRO-PMs, speclflcally electronic or digital PRO-PMs. Because of the novel nature of this Initiative, NQF staff have also been exploring other resources, such as PRO-PMs that have undergone the NQF endorsement process either successfully or unsuccessfully, bodies that review and recommend PROMs, and any PROMs and/or PRO-PMs that are used by CMS VBP Programs or APMs. NQF alms to present Its lnltlal environ mental scan findings at the first TEP meeting In January 2021.

Ele.ctronic Health Record Data Quality one of the promises of EHRs Is that they enable automated cllnlcal quality measure reporting. EHR
systems are prlmarily designed to support patient care and billing, not necessarily capture additional data to support quality measurement Centers for Medicare & Medicaid Services, 2019b. However, since EHR data are routinely collected for patient care that can be used for cllnlcal quality measures, they can be reused to reduce provider burden associated with public reporting and VBP programs Eisenberg et al., 2013. Despite high adoption rates In multiple care settings, the promises of EH Rs have not yet been fully reallzed because of conslderable variation In data quality.
NQF defines electronic clinical quallty measures eCQMs as measures that are specified using the Industry accepted eCQM technical specifications, which include, but are not limited to, health quality measure format HQMF., the Quality Data Model QOM, Clinical Quality Language CQL, and value sets vetted through the National Library of Medicines Value Set Authority Center VSAC National Quality Forum, 2012a. Using EHRs as a source of data, eCQMs were designed to enable automated reporting of measures using structured data. With the use of structured data, eCQMs have the potential to provide timely and accurate information pertinent to clinical decision support and. facilitate timely and regular monitoring of service utlll:tatlon and health outcomes Balley et al., 2014. Currently, NQF has endorsed nearly 540 healthcare performance measures with only 34 of these being eCQMs. Although the number of endorsed eCQMs is low, several measures in NQFs portfolio are quality measures that rely on data that come from an EHR, which NQF refers to as EHR-sourced measures. NQF has Identified the ablllty of EHR systems to connect and exchange data as an Important aspect of quallty healthcare. However, eCQMs.and EHR data are not enough to enable automated quality measurement. To better understand the potential of improving quality measurement with the use of EHR data for clinical quality measures, it Is Important to examine the current state of EHR data quality.
In 2020, NQF continued the implementation of an 18-month project that was initiated in 2019 to Identify the causes, nature, and extent of EHR data quallty Issues, particularly as they relate to measure development, endorsement,.and Implementation. This multlstep effort was aimed at Identifying a set of strategies for addressing issues hindering EHR data quality and focused on how well EHR data can be used to support automated clinical quallty measurement. To achieve this, NQF convened a 21-member multlstakeholder TEP over a series of web meetings to guide and provide Input on the work.

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Addltlonally, NQF completed an envlronm.ental scan that was delivered to CMS In May .2020 and Identified currently avallable Information on EHR data quallty Issues, current efforts to address these Issues, and key stakeholders perspectives and Input based on their experiences. The current state assessment from the environmental scan set the foundation for the development of a final report that will be delivered to CMS In December 2020, which offers recommendations on how to advance EHR
data In ways that better support the development, endorsement, and lmplementatlon of eCQMs. An