Payment Accuracy and the American Health Care System

“Life, Liberty and the pursuit of Happiness.” Promoting a healthy, long life has been an unalienable right in the United States since the founding of the republic.

That’s all well and good, but the price of delivering care to stay healthy has become burdensome. Health care expenses in the United States were estimated in 2018 to be $3.65 trillion. And the average inflation rate for health care in the United States between 2000 and 2019 is now 3.41 percent, or 62 percent higher than the 2.11 percent overall inflation rate.

While the science and medicine of health care continue to advance, it’s less obvious how to make the same progress when it comes to costs. That’s where payment accuracy comes in. When payers produce bills for reimbursement that are accurate, there’s less administrative cost for both payers and providers — and less abrasion between the two parties.

There are three kinds of payment accuracy checks on claims reimbursement. These may occur singly or together:

Pre-Submission: A newer check, before the provider submits a claim;

Pre-Payment: Before money is sent by the payer to the provider;

Post-Payment Audit and Recovery: The most common, when the payer verifies claim accuracy after reimbursement.

The sooner bills are checked and verified in the payment cycle, the lower the administrative cost. There are fewer underpayments that irritate providers and fewer overpayment clawbacks where payers ask for a portion of their money back.

Getting payment right is important, too, as the U.S. health care system evolves from fee-for-service (volume-based care) to value-based care, with accountable care, bundled payment, pay for performance and other alternative payment models. Payment accuracy helps the system convert to VBC models.

Simultaneously, three newer technology trends help reduce the health care cost growth by identifying claims or groups of claims affected by error, fraud or overall waste. These are:

Cloud-based claims payment-and-verification information technology systems, which are standardized;

Artificial intelligence, which helps providers or payers analyze claims and find anomalies;

Machine learning, a form of AI, which looks within massive quantities of data for patterns that then can be leveraged to reduce costs.

Technology solutions rely on the codes which track healthcare services. Many of these, however, haven’t kept up with VBC models, let alone newer medical procedures, products, or devices. Codes originally were developed when virtually all healthcare in the U.S. was delivered on a fee-for-service basis.

In addition, payment system reimbursement is based upon a set of business rules and processes that are set in contracts between providers and payers. This has become more complex if a consumer uses multiple payers, such as Medicare and a private supplemental policy, and when providers and payers rework their contracts to account for value-based models.

On the ground, four trends also have increased health care administrative costs — and can benefit from payment accuracy practices.

Upcoding, the practice of consistently coding services at a too-high level, especially when it comes to high-volume, small-dollar overpayments.

Technical denials, wherein a payer, sometimes automatically, rejects a claim if a provider fails to meet administrative requirements, such as filing a claim too late; filing for a non-covered service; filing an outpatient service claim that overlaps with an inpatient stay; or filing a claim for an investigational drug that’s not covered. As a result, providers often add more staff just to deal with claims. In this combative environment, some providers respond with intentional obstruction, adopting tactics that make medical record reviews and audits more difficult and expensive for payers.

Overpayment clawbacks, when monies obtained through the audit process become an anticipated payer revenue stream. Clawbacks used to be considered found money, rather than a line item that embeds overpayment projections into payer budgets.

Stacking post-payment audits, or using multiple vendors’ auditing products, can compel providers to produce near-duplicate explanations for the same or similar billing discrepancies.

Payment accuracy is the key to tackling both the movement to VBC and the drivers of administrative cost growth. That’s where standard payment-and-verification, cloud-based IT systems come in.

Third-party IT solutions automate processes and can head off errors earlier in the process. Fewer claims require an audit.

The key is to avoid buying and operating an in-house IT solution. These require providers and payers to invest in their own infrastructure and recruit hard-to-find technical talent.

Integrated, cloud-based third-party IT systems, on the other hand, provide two benefits: They continuously and easily update information about new provider-payer contracts and processes, especially as value-based care grows. And they allow for continuous real-world learning, automating the identification of payment, making the whole process smarter and more precise.

That helps everyone — whether pre-submission, pre-payment, or post-payment — assess the probability that a given claim is an outlier. If a questionable claim passes muster, a payer might not have to request medical records nor need to perform an audit.

Artificial intelligence and machine learning help further by analyzing claims information — including the codes that underpin a claim — to find patterns that prevent, catch and correct payment errors as early as possible in the payment cycle. This can be done at speeds and with precision unattainable through human means.

In the pre-submission or pre-payment phase, machine learning helps the parties quickly understand whether a claim is valid or needs to be reviewed. In the post-payment stage, machine learning gives payers information for possible outreach and education programs tailored for provider staff who code and submit claims.

Cloud-based IT systems with AI and machine learning provide greater predictive accuracy because they learn from both the payer’s internal data and from massive volumes of aggregated national historical claims data.

The result: reduced health care administrative costs; a less abrasive, improved provider-payer relationship; and an easier transition to value-based care. The provider-payer dynamic morphs from abrasive to collaborative — from negative to positive.

In the end, this more positive provider-payer dynamic improves consumer satisfaction.

We all want to improve health care delivery and patient outcomes. Let’s make one of those outcomes a far smoother and accurate financial reimbursement process.


Dave Cardelle is vice president of payment integrity solution management at Change Healthcare.

Amy Larsson is vice president of clinical claims management solutions at Change Healthcare.

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