8/15/2019
David Foster, Sr. Director, Analytics and Insight
Last month, I was fortunate enough to attend the third annual NCQA HL7 Digital Quality Summit. For three days, some of the most brilliant minds in healthcare and tech gathered to discuss strategies to improve electronic quality measurement. Most attendees were from payer organizations, but data companies that help payers clean up and process the data, as well as auditors who check the data and measurements for accuracy, were there as well, along with a handful of provider organizations interested in improving their digital quality measurement to optimize payment in value-based models.
The summit provided a lot of great information about the future of automating interventions with patients and members who might not experience the best quality of care. My key takeaway from the summit: If the methods you use to gather data aren’t up to par, your digital quality measurement efforts might be as tedious and inefficient as the old days of manual paper chart reviews. Electronic medical records and data interchange standards like the C-CDA not only help coordinate care but also provide better data so we can measure care quality for NCQA HEDIS. When more organizations start using HL7 data interchange standards, we can do more to address data quality issues.
The workshops and presentations gave a good overview of HL7 standards and basic issues that firms typically run into when measuring quality with electronic data. It was encouraging to see so many payer folks digging into health IT standards, such as CDS hooks, that we’ve been talking about at HIMSS for years. Payers can’t just rely on X12 EDI anymore. For instance, claims data is not timely or detailed enough to calculate what you really need in order to measure quality for chronic disease management.
Another promising use of digital quality measurement is to identify care gaps. Eventually, we’ll be able to extend this and use automated intervention to help close those gaps. One of the challenges of this automation approach is that quality measurement and patient or member engagement are typically handled by different departments. There are promising exceptions to this general lack of connectedness, however. Occasionally, a quality expert in the data department will collaborate with the member engagement department. Together, they figure out how to integrate Healthwise with their improvement programs and send out automated interventions. A key advantage of this method is that it doesn’t put additional pressure back on the providers or care management groups that already have huge clinician caseloads. Providers already work to submit clinical data for care measurement—let’s help them be more effective by automatically closing gaps for them.
Patient engagement can also help us figure out who to include when calculating quality. Exclusion criteria ensure that organizations only count the appropriate individuals—the ones who are eligible to receive a given treatment or attain a given biometric goal. Peggy O’Kane, founder and president of NCQA, mentioned there are plenty of possibilities to keep quality measures meaningful as medicine personalizes. According to Peggy, we will likely see a period of dueling measurement standards: “one measure fits all” vs. “different measures for different cohorts.” Informing patients and collecting patient-generated health data about preferences are good ways to meet this exclusion criteria.
Let’s continue to create more examples of incorporating data quality measurement into interventions that enlist the patient. Lenel James, business lead for the Industry Standards and eHealth team of Blue Cross and Blue Shield Association, has put out the call for more of us to participate in the HL7 Connectathon coming up this September. And the NCQA is launching a new digital measurement community in 2020. As we continue to navigate interoperability issues, we’ll discover more ways to solve care quality problems with digital automation by enlisting the patients rather than putting more burden on clinicians.