In this episode, Dr. Dan Mingle provides a primer on ACO participation, explains how you can optimize quality scores when using MIPS CQMs, and shares some examples to help explain data completeness rules.

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Question One: Primer on ACO Participation

Karen asks: “Can you give me a primer on ACOs? As our practice joins an ACO next year, what issues do you think we should consider?”

• There are a few questions to ask yourself when participating in an ACO. First, what is the credibility of generating shared savings in your organization? Is there data showing your problem areas? Is there data showing where you’re most likely to generate savings?
• Next, if the ACO is going to generate savings, will your practice have an achievable role in creating those savings?
• After the ACO generates savings, do you understand how those savings are distributed to participants?
  • Will you, as a practice, be able to benefit in proportion to your contribution to the shared savings?
• Finally, what support will the ACO offer to help you make changes in your practice that will contribute to savings?
• When trying to understand the credibility of generating savings, here are some things to consider:
  • Is there easy access to Primary Care services in your area?
  • Is it credible that you can encourage your patients to begin their healthcare episodes in Primary Care?
    • There’s good evidence that episodes of care that begin in Primary Care cost about half of those episodes that start in the Emergency Room or are self-referred to a specialty provider.
  • Is there excessive access to expensive options?
    • An example of this could be open appointment slots for MRI scans. If it’s readily available, it’ll likely get used more, even though less expensive imaging options may work just as well.
  • In general, planned care is better than a chaotic, unplanned approach.
    • It’s helpful if you can categorize the common episodes of care as they present in your region and work to understand how these categories move through the local health system.
    • Then, you can look for opportunities to make them more efficient: speed recovery, eliminate duplication of efforts, decrease errors, and understand complications.
    • This process is only sometimes a matter of looking for the highest expenses. Instead, it’s more about looking for expensive care with a preferable option that’s more cost-effective.
• And a final note on ACO participation: unfortunately, you’ll always have a moving target for high performance due to CMS rule changes. Many organizations find that as they generate savings, the goals ratchet down, and it’s harder to create savings in future years. This dynamic is important to recognize, and you should understand that CMS is open to your feedback and experiences for future rule changes.

Question Two: Optimizing Quality Scores

Jason asks: “How do you recommend optimizing quality scores for ACOs using MIPS CQMs?”

• Remember that we have the power to change what we measure. So, it’s vital to start measuring and tracking your progress toward your goals.
• Decide on an interval in which you’ll track your progress: too infrequently, and you’ll lose out on opportunities to improve. However, measuring too frequently could mean you’ll have excessive opportunities to oversteer your efforts.
  • In Dr. Mingle’s experience, most quality improvement programs benefit from monthly or quarterly performance check-ins.
  • But daily or weekly check-ins can help turn your quality feedback into actionable insight to drive gaps in care analysis.
• Essentially, pick an interval that works for your organization and use your data to recognize crises when they occur and opportunities to increase performance over time.
• When starting a new measurement system, it’s reasonable to expect some of your measurements to be way off. Not all of the problems uncovered by your measurement will reflect defects in the quality of care. Some questions to drive improvement in your data quality are:
  • Where is the best data coming from?
  • Who collects the data, and where is it documented?
  • Can you get the data out of your system with simple queries?
• Watch and learn as you begin to improve. Measure, and then audit your measurements. You can start small by auditing ten patients to compare their quality scores. Then, extend your auditing to larger segments and continue to audit periodically to adjust and improve workflows.
• You’ll find opportunities to adjust training, templates, and workflows as you improve. This will be a continuous process to improve your system.
  • Remember that systems consist of people, processes, and technology. Look for ways to put the right people in the right jobs, provide the required training, adjust processes, and configure your technology to help you do a better job.
• At the end of his answer, Dr. Mingle highlights three general rules he follows for continuous system improvement:
  • Standardize: try to get everyone in your system to do things the same way.
  • Simplify: reduce steps and remove complexity at every opportunity.
  • Integrate: make choices that bring your care into closer collaboration and coordination across your team and organization.

Question Three: Data Completeness Examples

Amanda asks: “Can you provide a deeper explanation of data completeness? It’s still a little confusing.”

• Data completeness is represented in quality reports as the Reporting Rate. Generally, when you’re reporting quality to Medicare, you’re reporting four things:
  • Performance Met Rate
  • Performance Not Met Rate
  • Exclusion Rate
  • Reporting Rate
• Deficits in your Reporting Rate can harm your performance under the data completeness rules. CMS expects that you can’t always have access to all of your data, so they provide some wiggle room in the Reporting Rate – you only have to be above 70%.
  • The most common reason for the lack of access to data is that not everyone in your organization has moved from a paper chart to an EHR.
  • CMS also recognizes that the best data does not always come from the legal medical record. Sometimes it makes more sense to get data from a derived or parallel process like a tumor registry, surgical log, or vaccination log. Medicare assumes that for data outside the legal medical record, there may be holes in the data.
• CMS has said that if your data comes from the legal medical record, they assume that missing data indicates Performance Not Met.
  • There’s some variation in this rule with inverse measures, but we’ll put that aside for this explanation.
• Flu shots provide an excellent illustration for Reporting Rate:
  • To understand your Reporting Rate, you must know your denominator. In your billing data, you can understand which of your patients are eligible for a flu shot, and those eligible patients make up your denominator. Then, when you look in your clinical data for flu shot records, you’ll find those patients who have had flu shots. If you’re looking in the legal medical record for this data, the patients with flu shots are Performance Met for this measure. The patients who didn’t receive flu shots are Performance Not Met.
    • In this illustration, if you’re looking at a derived data set, CMS says you can’t count on the entry being there, so a lack of data means a lack of reporting. Instead of a performance deficit, you’ll have a reporting deficit. And, according to the data completeness rules, you don’t have a valid report if your Reporting Rate is below 70%.
• Dr. Mingle provides three more examples to illustrate the concept:
  • Let’s say you check your billing data and find that one hundred of the patients you saw last year were eligible for a flu shot. After searching the legal medical records, you find fifty patients with a recorded flu shot – those are Performance Met. Fifty patients have no flu shot recorded, so to the best of your knowledge, they didn’t have a flu shot. Those would be Performance Not Met.
    • Both data sets came from the legal medical record, and CMS assumes that if you search the legal medical record, your Reporting Rate is 100%. In this case, the missing data represents Performance Not Met, so you’d have a 50% Performance Rate.
  • Imagine a different practice with one hundred patients eligible for flu shots. In this practice, you’re keeping an immunization log that you judge as the best source for this data. Let’s say that you and your staff record flu shots in this log for 50% of the eligible patients. The remaining fifty patients have flu shots recorded in their medical records.
    • In this case, the missing data is considered a lack of reporting but not necessarily Performance Not Met. Your Performance Rate for the shots recorded in the legal medical record is 100%, but your Reporting Rate is only 50%. This fails the 70% data completeness requirements.
  • Consider an ACO or an evolving community health system for the final example. Ten practices are banded together in this organization, each with a different EHR. Data is accessible for six practices but inaccessible for four. Let’s imagine that each of our ten practices has ten patients eligible for a flu shot. In the six practices with accessible data, you find five flu shots each, giving us a 50% Performance Rate and a 100% Reporting Rate. But four practices can’t supply data, and we don’t know the flu shot status of forty eligible patients.
    • In this case, this organization would fail the data completeness requirements because they’re only reporting on 60% of their patients.
• A final note on data completeness: you can spend your 30% data completeness allowance on anything – data could be in a paper chart and inaccessible, or in a different EHR, etc. CMS allows for these situations, but they do not allow cherry-picking. You are not allowed to drop 30% of your patients with Performance Not Met status so you can elevate your performance.

Send us your value-based care questions!

If you’d like to ask a question about the APP transition, MIPS, Primary Care First, ACO quality reporting, or any other Alternative Payment Model, you can reach out to us in three ways:

• You can leave your questions in a YouTube comment under any episode of Ask Dr. Mingle.
• On LinkedIn, leave your questions in a comment on any of our posts.
• And you can reach out directly by sending an email to hello@minglehealth.com.

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