Advancing Towards Meeting Digital Measurement's True Potential

The Centers for Medicare & Medicaid Services’ (CMS) firm commitment to a digital quality future is not likely to change with the new Administration. CMS emphasized the importance of digital quality measurement (dQM) by setting a goal for all-digital quality reporting by 2030. More recently, CMS said that all but 3 of the 20 measures on its annual “Measures Under Consideration” list will be collected digitally. And the National Committee for Quality Assurance (NCQA) featured the importance of a fully digital quality future in our “Future of Healthcare Quality” recommendations to the Biden-Harris transition team.

dQMs have many benefits over traditional measures. They reduce reporting burden, improve accuracy, support a learning health system, and let us measure much more of what matters using the rich clinical data in electronic health records (EHRs), registries, health information exchanges and other electronic sources.

But not all digital measures meet dQM’s full potential to revolutionize quality by measuring more of what really matters. Many current dQMs are merely electronic replications of traditional measure specifications—with all their inherent limitations. Traditional claims-based measures provide rates of how often a process occurred in the past, but generally provide little to no information on outcomes. Traditional hybrid measures that use both claims and arduous manual chart review can assess intermediate outcomes but can only say whether the target outcome occurred at a point in time. Traditional measures cannot assess the individual risks and needs of a specific patient, and this lack of patient-centeredness limits their value. Their retrospective nature, where care is delivered one year, quality results reported the next and feedback shared after that, further constrains any prospective value of the knowledge they produce to inform care delivery.

There are good reasons (at least for now) to translate traditional measures into digital format. Most are straightforward to digitize. Translation into digital format reduces burden and improves accuracy and is a logical step toward a more meaningful digital quality future. We have to walk before we run towards the digital quality future NCQA and CMS envision.

As we start to run, however, we must build on the experience with traditional measures to create next generation of dQMs assessing not only whether a process occurred, but also whether the data generated by the process provided a clinical benefit appropriate to an individual person. They do this by using very specific information on each individual’s unique circumstances rather than to a generalized guideline published for an “average” patient. These new dQMs assess patient care prospectively to inform care delivery in near real-time and they reward meaningful progress based on benefit to individual patients, even if the patient does not fully achieve the targeted outcome.

Consider how we measure effective management of hypertension. Digitizing the traditional blood pressure control measure makes it more efficient to locate and abstract needed information from standardized electronic clinical data. However, the measure still follows the old dichotomous paradigm of whether a patient did or did not meet generic blood pressure targets at a single point in time. We obtain no knowledge about whether the patient managed their hypertension well over the entire measurement period.

Nor does the digitized traditional measure version reward progress toward individual care goals. The next generation of dQMs can track progress over time and recognize true clinical benefits, even if the patient does not meet the target. For example, lowering a dangerous blood pressure reading of 190/130 to 146/100 does not meet the traditional target (<140/90) and therefore gives the clinician no credit, even though it reflects much greater clinical benefit to the patient than lowering a 146/94 reading to the target, which the traditional measure does reward.

Perhaps the best examples of dQMs that meet many of the principles for a digital future are the HEDIS^®[1] depression measures in the electronic clinical data system (ECDS) reporting program. The measures start with screening each patient for depression using standardized, validated instruments and suggest actions based on a patient-reported response. Actions vary from follow-up after a positive screen to monitoring response to treatment based on the individual patient’s repeated interactions with standardized instruments. These measures help inform care planning because the measure calculation provides immediate information from patient data and suggests action, where appropriate. Because these measures are digital, they generate information in real time so clinicians can adjust treatment if subsequent testing indicates that the patient had an inadequate response to the initial approach.

Few dQMs today focus on outcomes or take advantage of the genuine opportunities that digital measure specifications can provide. Obviously, it takes time for stakeholders to fully appreciate and adapt to the new paradigm dQMs afford. But as we lengthen our stride and learn to run, it will be ever more important to develop new dQMs that meet the full dQM promise of measuring what matters for individuals—measuring progress rather than achieving simple, dichotomous targets—and informing care at the point of delivery.

^[1] HEDIS, the Healthcare Effectiveness and Data Information Set, is a registered trademark of NCQA.