ACOsAPP ReportingData Best PracticesValue-Based Health Care

Don’t Fall for Magical Thinking in APP Reporting

By June 16, 2023 One Comment

APP Reporting eCQMs Interoperability CQMs Roji Health Intelligence data aggregation

Since the adoption of the 2023 Final Rule requiring ACOs to adopt Alternate Payment Model Performance Pathway (APP) quality reporting by performance year 2025, many ACOs have been scrambling to understand how to make the leap. There’s a huge difference between the old method of quality reporting using the CMS Interface to report on a 248-patient sample and the new requirement for APP reporting on all practice patients, regardless of payer type.

The sheer size of reporting volume and mechanics means that your ACO will need to aggregate practice EHR data for the first time, even with fewer measures under the APP. If yours is a multi-practice ACO with no prior investment in data infrastructure—the norm, except for population health— this is a major undertaking. To meet the deadline, your ACO may be one of many feeling pressured to look for the “easy, fast” path to APP reporting. But that’s just magical thinking. Here’s the reality:

The Four Hurdles of APP Implementation

APP Reporting requires a solution that overcomes four major obstacles to achieve accurate reporting:

  1. APP reporting requires the measure to be patient-centric. If your patients are seeing participating primaries and specialists in your ACO, that requires matching patients across separate data feeds to report the patient only once for the measure. Patient matching is messy and ACOs rarely have the internal expertise to accomplish this.
  2. APP measures must include the entire provider’s population in the measure denominator. You must collect data on all patients and determine who is eligible for the measure.
  3. There are no shortcuts for matching patients, either through CMS claims identifiers (Medicare only) or patient ID numbers (different numbers for different systems). You must use the patient matching programming to do it.
  4. APP measures require the latest value for the measure numerator collected in the performance year. After your patients are aggregated and matched, you must align the values for the measure and report the most recent one for both HgbA1C and blood pressure. This requires a measures engine or algorithm for accurate reporting.

The bottom line: APP reporting will require an experienced entity, either a qualified registry or certified EHR (for single-TIN ACOs only), to provide the data crunching and reporting under the APP.

Are eCQMs Really the Easy-Peasy Answer?

It’s no surprise that some ACOs are panicking that the path to APP reporting is requiring resources and work. Naivete about data has led to some truly remarkable, mistaken conclusions:

1. Belief that eCQMs, or electronic measures, are automatically designed to be the fastest and easiest way to report measures, because they are “automatic.”

ACOs often think CQMs, which are the MIPS version of the same measure and can be constructed from a variety of non-automatic aggregated data systems, must be harder because they are not “automatic.” This conclusion is based on an underlying assumption that the digital future should be built on eCQMs because system-to-system transmission is the modern goal.

But the idea that any measure system is—or ever will be—“automatic” does not account for the fallible humans who create data or systems. Data does not always automatically “flow” into measures easily, for two reasons. First, organizations develop customized templates to accommodate specialty workflow in the EHR, causing the data to flow into a separate data field— and not to the pre-defined EHR reporting field that captures measure results. Second, the physician may record a measure value in progress notes. Likewise, an attached PDF from a remote lab or other provider may be in the patient’s record. Either way, the data is not where it must be to populate the measure. While these two issues may get resolved as technology progresses, there will still be failures.

Here’s the kicker: For eCQMs, any missing data causes the measure for that patient to FAIL. Missing data is always a surprise to providers, who can see the data in the record but don’t understand why it isn’t populating the numerator of a measure.

2. Expectation that eCQMs will produce higher measure results than CQMs.

In addition to the missing eCQM data, your ACO needs to understand the differences between eCQM and CQM measure calculations. Most importantly, CQM measure performance is based on the percentage of measures that are “completed,” which must be at least 70 percent. eCQM measures are calculated based on all measures. You won’t know, going into APP reporting, which will produce a better result against the benchmark unless you test both measure types. But this will be cost-prohibitive because the two methods require different data sources for calculation, and the measures are benchmarked differently.

In addition, because of slight variations in the measure specifics, there will be different measure results. For example, CQMs apply to a patient with a diagnosis of diabetes prior to the Performance Year (PY), but eCQMs apply to patients with a diagnosis during the PY. The exclusion of patients with good control—and who may have less frequent visits that trigger the measure—can result in a lower result for the eCQM versus the CQM.

3. Perspective that eCQMs are less expensive.

You may think of eCQMs as a one-step process, with data flow from the EHR to CMS. If you are a new ACO previously reporting eCQMs for the MIPS program, your perspective is based on the fallacy that one-EHR reporting is the same as multiple-EHR reporting. Consider the requirement of patient-centricity and matching measures discussed above.

More importantly, understand that aggregation for CQMs and eCQMs requires different data sources. For reporting eCQMs across multiple practices, you will be required to collect and process QRDA 1s for every practice, and then work out a patient matching mechanism on top of the QRDA file structure. Multi-practice QRDA 1s will require you to transform these file formats. Note that QRDA 1 processing is expensive, three to five times more expensive than flat files. Your total aggregation cost will be much higher than the norm for a reporting method that may produce lower results.

Finally, you may find that many practices on cloud-based or smaller EHRs cannot produce QRDA 1s. This is not unusual, even if the system is certified—it is certified to turn QRDA 1s into the reporting-ready QRDA 3, not to export QRDA 1s. Here’s the risk: if only one practice cannot provide QRDA 1s, your method of reporting eCQM fails. Should that happen, you will need to restart your initiative.

Be sure that your staff or contractor is extremely experienced with multiple practice data aggregation before you commit to reporting eCQMs, or you will hit delays and rack up extra expenses in the process.

4. Belief that CQMs are too much work, or missing advantages because they are built on different data sources.

Here’s what we have learned at Roji, a qualified registry that reports both eCQMs and CQMs across many clients: CQMs are often more advantageous to clients, because

  • The calculation of measures often favors CQMs;
  • The flexibility of additional data sources for CQMs, including QRDA 1s, allows us to improve measure results;
  • Processing data from flat files is cheaper and easier for clients than QRDA 1s;
  • QRDA 1s have a huge disadvantage in that the value of the data is so limited for the cost of aggregation. Flat files can provide a much richer data base to be used for analytics, population health, and other initiatives.

As both EHRs and digital quality measures evolve, we will see improvements in efficiency as well as data. But a decision to aggregate data solely for the purpose of APP reporting will not serve ACOs to deliver better quality care at lower cost; it will only satisfy one requirement.

Interoperability and APP Reporting

The National Association of ACOs (NAACOS) has promoted the use of eCQMs in reporting but recommended against APP Reporting without the existence of standardized interoperability features of EHRs. The report correctly corroborates some of the deficiencies in eCQMs noted above, such as the lack of QRDA 1 production in some EHRs, but there is still an underlying commitment to eCQMs as the easier, magical solution to quality reporting. The NAACOs report emphasizes that the obstacles to eCQM reporting should be cleared before CMS requires APP reporting.

There are several good suggestions in the task force report, such as moving toward better certification requirements for EHRs to produce data formats needed for quality reporting. But the underlying belief that system-generated reporting can ever be automatic, flawless, and cheaper is simply not realistic, for reasons stated above.

Further, progress in the development of digital quality measures, standardization of patient data formats, and interoperable means of transporting data from one system to another have been twenty years in development and will take another ten to twenty years to mature. Most large health systems do not have the imperative or even the staffing to invest in developing the simplest FHIR capabilities right now. Even large systems have often not invested in Value-Based Care technology and initiatives to produce higher volume savings on the scale needed to curtail costs and advance outcomes.

Interoperability between systems is intended for transmitting patient clinical information at points of care, for both the safety and coordination of patient care. Designing interoperability to promote flawless quality reporting is not its best use, and compounds the costs of moving forward with clinically-focused initiatives.

Value of Data Aggregation Goes Beyond APP Reporting

Magical thinking has a cost. In particular, it focuses on a method of reporting and not the value of data for ACOs—data that would enable ACOs to push beyond current limits of savings and to vastly improve outcomes for patients, identify patients receiving inequitable health care, and prioritize patients for interventions.

A NAACOS blog recently projected true data aggregation costs based on an alleged proposal of $4 million to $37 million to a large ACO. That this would represent a typical charge from vendors is fantastical, not even within the ballpark of reality. If the named ACO actually received a quote of $4 million to $37 million for data aggregation, either the ACO-stipulated requirements were unrealistic and massive, or the vendor was highly opportunistic. But presenting such information as an accurate depiction of data aggregation charges is misleading.

As your ACO moves both to create your Value-Based Care infrastructure and to implement APP reporting, your ticket for lower cost and better results is this: realism in the assessment of data aggregation, reporting mechanisms, and future opportunities. Call us today to help you prepare.

Founded in 2002, Roji Health Intelligence guides health care systems, providers and patients on the path to better health through Solutions that help providers improve their value and succeed in Risk.

Image: Johannes Plenio