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September 1, 2021

Changing Healthcare Data Quality Measure Strategies to Improve Care

With his experience from the military, technology strategy, and data expertise, Dale Sanders, Chief Strategy Officer at IMO, discussed the country’s healthcare data quality measurement strategy, eliminating low-value care, and easing the clinical data entry burden.

National Quality Measure Strategy Is Not Increasing the Quality of Care

Dale Sanders This Week in Health IT

Dale Sanders, Chief Strategy Officer at IMO

According to Sanders, the quality measures strategy should shift from a process-oriented focus to eliminating low-value care.

The current process-oriented focus forces clinicians to enter healthcare data into EHRs, proving they follow the clinical process. If clinicians were no longer required to meet this requirement, it could save the $300 billion a year that is spent on this low-value care. Additionally, it would protect physicians from burnout as it eliminates clinical data entry burdens.

“If we stopped doing just a handful of unnecessary things in healthcare meds, orders, [and] labs, we save all sorts of money. We reduce patient harm and inconvenience. And reduce the clinician data entry burden. And this is something that people don’t quite grasp. It’s the absence of data,” he said.

CMS Needs to Lead the Way

According to Sanders, this adjustment is within the purview of CMS, ONC, and private payers.

“But CMS is the largest purchaser of government services in the world, of healthcare services in the world. CMS needs to lead this, and they need to be proactive and aggressive about it,” he explained.

During the Trump Administration, CMS was gaining momentum on significant measures for this. However, reducing burdens of EHR entry for clinicians and rethinking quality measures have since slowed in the Biden Administration, he explained.

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Clinicians Support Changes to Healthcare Data Quality

I would argue that we should shift to a quality measure strategy that is focused on the elimination of low-value care. Measuring what clinicians didn’t do, which is the absence of data, and does not require data entry,” Sanders explained.

While most clinicians support it, there are some hesitancies. According to Sanders, opponents believe high-value care will be difficult to follow and involve more reauthorization. They believe this routine is too intangible to track easily. However, publications show how ceasing these handfuls of activities results in saving money, preventing patient harm, and giving convenience.

The proof is in the absence of data. Whereas our current quality measures is all about documenting, documenting, documenting. Proving in some way, justifying in some way that what you’re doing is practicing according to the quality measure strategy we have in the country now,” he explained. “Low value care is the opposite of that. It’s the absence of data. It’s the absence of data entry.”

Two aspects can block the elimination of low-value care. According to Sanders, the practices of low-value care (excessive testing and prescriptions) generate top-line revenue. Additionally, clinician’s must use their discernment and people skills to learn how to say “no” to a patient’s request for medication. The latter of the two, however, can potentially affect customer satisfaction scores.

“I think if you engage with most patients in a rational, empathetic way to explain to them why I think most patients will trust their physicians. I think most patients have an inherent trust in their physician,” he countered.

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IMO Moving Upstream for Future of Data Quality

Serving as the “analytics guy” in healthcare since 1997, Sanders struggled with data quality. According to Sanders, healthcare data quality problems begin at the data stream.

“The truth is it’s a dog chasing its tail because you never really get ahead of the data quality problems,” he said.

He has hope to continue leveraging IMO’s clinician-friendly experience around the EHR. He will be advocating for new things for the EHR experience and the IMO brand.

There are plans to build out tools to accelerate the accuracy of normalizing disparate, DHR, and clinical and claims data. According to Sanders, there is always a need for data curation and normalization.

Furthermore, the future strategy also consists of making value sets more meaningful and useful.

“It’s basically addressing these data quality problems that are driving me crazy as an analytics guy. I want to get upstream of it to the clinician experience, to the data quality curation pipeline,” he said.

Study Finds Inconsistent Patient Data is Used for Initiatives

A recent study aimed to understand how patient data is used in decision-making analytics and how healthcare data quality affects achieving enterprise goals. The IMO and HIMSS found 57% of data is inconsistent. This variance is due to subjective provider documentation. According to the study, patient data is used for revenue cycle management, clinical decision support, and quality measurement.

According to Sanders, the country needs to mandate orders and results that must harmonize to national standards. When data is matching these levels at minimal amounts, it presents challenges.

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“Imagine if you went to the grocery store or Target or Walmart, and one out of ten, maybe one out of 50 products, had a UPC code on it. What would be the implications to the consumer experience? What would be the implications to the supply chain? To the manufacturer?” he asked.

Harmonizing EHR Orderable Catalogs to a Universal Standard

Healthcare must embrace these standards fully, Sanders explained. It should be a mandated law for every lab order, result, medication order, or prescription should associate itself with national and international standards.

“The only reason that we’re harmonizing anything right now is if it’s associated with reimbursement, that’s why the numbers are so low,” he said.

As a CIO, Sanders recommends as organizations deploy EHRs, orderable catalogs should be built-in. These catalogs should be in harmony with national standards. Since many have not done this, there is a need to go back and modify this.

“Even if it doesn’t have anything to do with reimbursement, everything we order is going to have a UPC code associated with it,” he said.

Patient IDs Are A Cultural Imperative

According to Sanders, it is essential to have a national patient ID.

“[There] is a very vocal minority of people that oppose a national patient identifier. Most patients, especially if they knew the benefit it would have to them, would say, absolutely, sign me up. Let me have a national patient identifier,” he said.

The main benefit of a national patient ID is integrating records consistently in public and population health, in Sander’s opinion.

While there is no financial incentive for a public and private data system to come together, there is a cultural one. According to Sanders, there is a cultural imperative within healthcare to share names.

The Pandemic Will Have a Lasting Silver Lining

With an estimated 70% of the country’s population vaccinated for COVID-19, Sanders believes it speaks to the country’s power.

“I think we all should be kind of humbled at the scientists and the engineers and the logistics that went into the vaccination,” he said.

According to Sanders, COVID has been a tragedy; however, it will positively impact society in the long run.

What to Do in the Case of A Future Pandemic

If a future pandemic were to come, several things should change beforehand.

According to Sanders, the distinction between public and population health and infectious and chronic diseases is necessary to determine.

The separation between public and population health in the United States is not something that other countries have.

“Culturally, those two worlds have to work better together than they do right now,” he explained.

A data strategy must tie the two populations together to do this. During the pandemic, the inability to determine it to be a population health issue overwhelmed clinicians. This is why the distinction between the two must stop.

According to Sanders, the data sharing must come back to a standard sort of Classic Decision Theory. This is situational, where a hypothesis generation comes prior to intervention or assessment.

“Taking a data strategist approach that brings those two worlds together. And a decision theory approach to the data that we need to support those three components of decision making is, I think, fundamentally important to the future,” he said.

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