February 22, 2016

Data is What You Make It

By


Joseph Nichols, MD

Old habits die hard – but hopefully, not too hard.

That’s one way to summarize a recently published white paper by Joseph C. Nichols, principal of Health Data Consulting. The paper, titled Data Value: Breaking Old Habits, shines a spotlight on the ways the healthcare industry historically has collected and used data, along with how things are changing under ICD-10 and value-based purchasing.

“Ironically, healthcare seems to be the one industry where data collected on … transactions is often considered an administrative burden. Unlike other industries, data quality is not considered a key focus,” Nichols wrote. “The potential for the use of transactional data to understand healthcare across all healthcare enterprises is immense, if we could just trust it. Unfortunately, we often use this data to make assumptions and decisions that simply can’t be supported based on a better understanding of the quality of the data we have.”

The reasons other industries place such a high premium on good data are numerous, Nichols notes – for decades, such sectors have viewed the quality, accuracy, and completeness of their data as being directly tied to their own survival. Such industries don’t even tolerate incomplete, inaccurate, or inconsistent data, he adds – it’s a foreign notion.

Then there’s healthcare.

With shrinking funds, increased oversight, and rapidly proliferating regulations to attend to, data can tend to take a back seat to other, more pressing concerns.

“For physicians, medical school taught us that understanding all of the data parameters about the patient’s health state is critical to making wise decisions to improve or maintain their health status,” Nichols wrote. “What we didn’t learn was the value of capturing those parameters in a complete, consistent, and standard way. Without a consistent commitment to capturing data that is complete, accurate, and standard, we cannot expect to get information about healthcare that can be used to understand patterns of illness and changes in those patterns; the risk and severity of disease in a population; the value of healthcare in terms of outcome and experience of care; causes of diseases and injuries that could be mitigated; (or) the effectiveness of policies to improve healthcare value.”

How to undo bad habits that contribute to the lack of data uniformity? They aren’t cure-alls, Nichols notes, but the industry’s gradual transition to value-based care and ICD-10 can help.

Still…

“There seems to be a belief that the move to ICD-10 will address many of the issues of data quality and specificity, but those who understand ICD-10 know that we can be just as vague in ICD-10 as we were in ICD-9,” Nichols wrote. “We have always had the opportunity to be more specific in ICD-9, but rarely have we taken that opportunity. While there is no doubt that an improvement in data specificity can have a profound impact on data value, until the majority of providers submitting these transactions buy into the value of specific definition of their patients’ health state, the promised advantages of the ICD-10 standard change will be lost.”

The white paper’s thesis hinges on a massive analysis of historical data – three years and nearly $10 billion in charges, to be exact. What that analysis uncovered was that for far too many conditions, vague or non-specific documentation yielded vague and non-specific data.

For breast cancer, for example, “malignant neoplasm of breast, unspecified” accounted for approximately two-thirds of all cases. Similarly, for cardiac rhythm disorders, about half of cases were classified as “cardiac dysrhythmia, unspecified,” with another nearly 40 percent “atrial fibrillation.”

So for the vast majority of coded documentation speaking to such conditions, factors such as laterality, acuity, and severity were nowhere to be found.

“It is generally accepted that atrial fibrillation is the most common type of cardiac rhythm disorder. It would be extremely valuable to have a better understanding of the costs, risks, demographics, outcomes, and other parameters of this disease across all healthcare enterprises and population based on large population data sets,” Nichols wrote. “Unfortunately, when we look at this data, the quality and specificity is such that we really don’t know what types of rhythm disorders are being treated with any level of reliability.”

“The only data we can rely on is at a level that loses a great deal of potential value,” he adds. “The specificity of the few is lost in the vagueness of the majority.”

Nichols concluded his paper with five strategies he feels can help stem the tide of such vagueness. Specifically, he said, there needs to be a focus on population health; bundled payment and episode-based care models; better definitions of risk, severity, and complexity of health status; disease surveillance; and payment that adjusts for risk severity, complexity, case mix, and other parameters.

And just maybe, as a sixth, a renewed commitment to break bad habits.

Disclaimer: Every reasonable effort was made to ensure the accuracy of this information at the time it was published. However, due to the nature of industry changes over time we cannot guarantee its validity after the year it was published.
Mark Spivey

Mark Spivey is a national correspondent for ICDmonitor.com who has been writing on numerous topics facing the nation’s healthcare system (and federal oversight of it) for five years. 

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