Pareto Intelligence, an analytics solution provider for healthcare organizations based in Chicago, Illinois, has uncovered a multitude of data mistakes in government health care programs which, say the company, had a total financial impact of over $150 million in 2017.
Data Mining Uncovers $150 Million in ACA, Medicaid and MA Mistakes
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#affordablecareactSecondary#medicaid#medicare#compliance#paretointelligence
The company, which is a data mining and analytics service, used its proprietary algorithms to analyze data completeness and accuracy for more than four million individuals who are in government health care records. Specifically, Pareto looked at Medicare Advantage, Medicaid, and Affordable Care Act (ACA) programs.
According to the company, the analysis revealed a significant number of data quality issues which impacted the government and therefore taxpayers in the form of potential overpayments and lost health plan revenue totaling more than $150 million.
Pareto’s software, brand named ‘Data Integrity solution’, pulls data from every step of data capture process—from the original encounter with a person or company all the way through the regulatory submission. Of course, this is only possible because Pareto employs advanced analytics to pinpoint specific areas of degradation. Pareto also uses clustering algorithms to determine the root cause of the data quality issues.
Pareto’s objective is to reveal the mistakes and then offer services to correct those mistakes and improve processes. The company claims that it is able to proactively identifies instances of incorrectly captured and submitted encounter information.
How significant is the problem of data quality? The company told OTW that even a 1-3% data quality issue can translate into a $5-15 million revenue shortfall for a 50,000-member health plan.
“Our experience has shown that most data integrity evaluations tend to focus on whether the information submitted to the government was accepted or not. However, this approach ignores the multitude of data quality issues that can occur pre-submission in the data process, causing encounters to not be submitted at all.” Pareto Intelligence Vice President of Client Advisory and Business Development Brandon Solomon told OTW. “That’s why Pareto has designed a holistic solution that evaluates end-to-end data integrity to identify and resolve issues posing financial and compliance risk for health plans at the source. The results have been significant for our clients, and we expect even better outcomes this year.”
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This is a fascinating development. In my practice we've seen similar outcomes with the revised protocol. The key differentiator seems to be patient selection criteria. Has anyone else noticed the correlation with BMI thresholds?
Great point. I'd push back slightly on the conclusion, the sample size in the cited study is too small to draw population-level inferences. That said, the directional signal is compelling and worth a larger RCT.
We implemented a similar approach last year. Early results are promising but we're still gathering 12-month follow-up data. Happy to share our protocol if anyone is interested.
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