Accuracy of Electronically Reported “Meaningful Use” Clinical Quality Measures

Let’s face it. Although we can challenge Kern andcolleagues’ (1) methods (for example, by claiming that electronichealth records [EHRs] are much different today than they were in2008), I don’t think that we can challenge their findings. The Achil-les’ heel of end-to-end quality reporting from EHRs lies with datacapture. The challenges to data capture are many—divergent datarequirements (across quality measures, decision support rules, clinicalpractice guidelines, and others), time pressures, and skepticism (forexample, are all these data elements really necessary?)—which canleave providers overwhelmed and resistant.Quality reporting is required under the federal meaningful useregulations mandated by the 2009 Health Information Technologyfor Economic and Clinical Health (HITECH) Act. A meaningfuluse–certified EHR must be able to export standardized quality re-ports, which can then be fed into a calculation engine to computevarious aggregate scores (such as the number of patients meeting thenumerator and denominator criteria). Interoperability standards forquality reports have been carefully crafted within the Health Level 7standards organization and widely vetted, but we have to acknowl-edge that garbage in equals garbage out and that interoperabilitystandards that support quality reporting cannot compensate for in-consistent or missing data at the source.So, how is the standards community addressing the challenge ofdata capture? We are addressing it head-on by providing the defini-tive source of truth and direction needed by software vendors andclinicians. Through standardization comes a convergence on key dataelements needed for transitions in care, quality reporting, and deci-sion support. Rather than having numerous use cases (for example,transitions of care, clinical decision support, and quality measure-ment), each with its own data requirements converging on the point-of-care provider, standardization leads to a convergence of key dataelements. This convergence sets a clearer path for vendors and userinterface designers, lessens the burden of data capture on clinicians,and focuses data capture on data elements known to be valuable forvarious purposes.In other words, interoperability standards are relevant both onthe afferent and efferent limbs of the EHR. Focusing on standards isa tractable approach for addressing challenges to data capture andtherefore provides a strategy for addressing the very real challengeswith data quality identified by Kern and associates.