Payment Accuracy Strategy and the American Value-Based Healthcare System

As the U.S. shifts to value-based care models, getting reimbursement claims accurate from the get-go becomes ever more important. Driving Payment Accuracy is of critical importance to many health plans. It’s important to understand how the market is evolving, how to take advantage of best practices, and how to avoid common pitfalls associated with some approaches.

 

Payment Accuracy Strategy and the American Value-Based Healthcare System
Payment Accuracy Strategy and the American Value-Based Healthcare System

This article provides an in-depth review of the top techniques making a positive impact in the market, and explains why other methodologies are proving less effective.

You’ll get recommendations on how to move forward with a cohesive strategy that results in savings and improved provider relationships.

Content Summary

Introduction
How Payment Accuracy Reins in Healthcare Costs
Value-Based Care and New Technologies
Of Codes and Contracts
Administrative Costs Keep Growing
Standardized Healthcare IT Systems
Where Cloud-Based Systems Fit
Artificial Intelligence and Machine Learning
From Abrasive to Collaborative

Introduction

“Life, Liberty and the Pursuit of Happiness.”

This famous phrase is perhaps the most well-known excerpt from the U.S. Declaration of Independence. Indeed, promoting a healthy, long life has been at the forefront of family and political discourse in the United States since the founding of the republic.

There’s Only One Problem: The price of delivering healthcare to stay healthy has become increasingly burdensome.

The cost of healthcare in the U.S. economy in 2018 was estimated to be $3.65 trillion.

That’s why it doesn’t matter where you sit in today’s healthcare ecosystem: For consumers, for healthcare providers, and payers, it’s not only about improving health outcomes but also finding a way to rein in costs.

But keeping the growth of costs in check is a challenging proposition when the estimated average inflation rate for medical care in the United States between 2000 and 2019 has been 3.39%, some 60% higher than the 2.13% U.S. inflation rate over the same period in the overall economy.

How Payment Accuracy Reins in Healthcare Costs

And while the science and medicine of healthcare continue to advance, it’s less obvious how to make the same kind advances when it comes to healthcare costs. The flashpoint here is the process by which payers—insurance companies, government agencies, and the like— reimburse providers: hospitals, doctors, labs, or clinics.

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.

The U.S. healthcare reimbursement system has evolved to provide three general kinds of checks on claims reimbursement. These are buttressed by healthcare IT systems, as well as automated or physical audits. These checks on the accuracy of a claim can occur at any one of three points, or a combination:

  1. Pre-Submission is the newest kind of accuracy check at the point of submission—when the provider submits a claim for a service, medicine, or device.
  2. Pre-Payment checks occur before money is sent by the payer to the provider.
  3. Post-Payment Audit and Recovery checks and audits have long been the most common. They occur when a payer verifies a claim’s accuracy after reimbursement.

Driving accuracy earlier in the payment cycle lowers administrative costs–and reduces friction, or abrasion, between payers and providers.

When bills are checked and verified early in the payment cycle, there are fewer underpayments that irritate providers. Conversely, there are fewer overpayments which compel payers to ask for a portion of their money back, a process known as clawbacks and a noted source of provider/payer tension.

Value-Based Care and New Technologies

There’s another important factor to consider. The nature of the U.S. healthcare system is evolving from fee-for-service—also known as volume-based care—to value-based care (VBC), which includes accountable care, bundled payments, and pay for performance.

Payment accuracy, therefore, holds the key to reining in costs for two reasons: to keep administrative costs in check and to help the system convert to VBC models.

Fortunately, several technology factors also promote payment accuracy. These include:

  1. Cloud-Based Payment and Verification, which standardizes processes between payers and providers using third-party IT systems;
  2. Artificial Intelligence, which helps providers or payers analyze claims and find payment anomalies; and
  3. Machine Learning, a form of artificial intelligence, which continuously improves results by looking within massive quantities of data for patterns that then can be leveraged to reduce costs.

These three approaches are helpful not only because they help reduce the rate of growth in healthcare costs, but also because they help identify claims or groups of claims that may suffer from errors, fraud, or overall waste.

Of Codes and Contracts

Technology solutions rely upon the codes that track healthcare services and form the basis for claims providers’ files. Payment systems were originally built using codes that were not intended to support today’s complex healthcare system.

Codes were initially developed when virtually all healthcare in the U.S. was delivered on a fee-for-service basis, when value-based care was not yet prominent. Moreover, codes and coding rules haven’t kept pace with advancements in technology and medical procedures. Existing codes often cannot define newer procedures, products, or devices.

Payment system reimbursement also is based upon a set of contractual business rules and processes in which providers and payers (both private and public) negotiate with one another.

Contracts define the process for filing claims, making payments, and verifying (auditing) the accurate filing and payment of claims.

Payment rules are made more complex when multiple payers or types of payers are involved, such as when a consumer may have coverage from Medicare as well as a private supplemental policy.

To complicate matters further, contracts and the rules that define the provider-payer relationship are in a state of flux as providers transition to value-based models.

Administrative Costs Keep Growing

There are four additional trends or practices that have served to increase healthcare administrative costs and which payment accuracy can work to mitigate:

  1. Upcoding, the practice of consistently coding services at a too-high level;
  2. Technical Denials, wherein a payer, sometimes automatically, rejects a claim out of hand for an administrative reason;
  3. Overpayment Clawbacks, as an expected budget line item on the part of payers; and
  4. Stacking Post-Payment Audits, wherein multiple programs audit the same claim or set of claims, duplicating requests for the same backup material.

Now we will cover each in more detail.

Upcoding has long been a challenge, especially when it comes to high-volume, small-dollar overpayments. For example, a dermatologist may code nearly all of his or her patient office visits at Level 5, indicating extensive evaluation and management. The average dermatology practice, by contrast, may only code 30% of appointments at Level 5. The question for the payer becomes: how do you convince that particular provider to change his or her coding behavior? How can you distinguish legitimate Level 5 visits from up-coded ones?

Technical Denials, in which a payer denies an entire claim, are increasing in frequency. These denials cause a great deal of friction in the provider-payer relationship. Payers issue technical denials most often when a provider fails to meet administrative requirements, rather than for medical reasons.

The Reasons for Technical Denials

There are many reasons that a payer might send a provider a technical denial. Examples include, but are not limited to: filing a claim too late, filing for a noncovered service, filing an outpatient service claim that overlaps with an inpatient stay or filing a claim for an investigational drug that’s not covered.

The problem is that when a provider receives a technical denial it often comes with little explanation, compelling the provider to call the payer to discuss and resolve the issue. That has resulted in providers adding additional staff to deal with claims. And while providers correct many of the claims that are rejected as a result of a technical denial, the whole process incurs more administrative expense—and abrasion.

In this sort of combative environment, a few providers respond with intentional obstruction, adopting tactics that make medical record reviews and audits more difficult and expensive for payers. This includes the occasional counter-intuitive process of taking electronic records and printing out a claim on paper, even if the back-up material runs into hundreds of pages.

Expected Overpayment Clawbacks are a growing phenomenon. For payers, the recovery of overpayments through the audit process used to be considered ‘found money’ at the end of a fiscal reporting period. Today, payers expect refunds from a proportion of the monies sent to reimburse providers.

Indeed, payers may count on recovery as part of their revenue stream. They may embed overpayment projections into their budgets, which may make or break a payer’s fiscal quarter.

Stacking Post-Payment Audits is the fourth burden on administrative costs. Payers may “stack” their post-reimbursement audit software, using multiple vendors’ auditing products, scanning the same claims with different software. Payers using multiple solutions may end up asking providers for near-duplicate explanations that hone in on the same or a similar billing discrepancy.

Standardized Healthcare IT Systems

To reduce the rate of growth in healthcare administrative costs, payment accuracy requires standardized IT systems that are integrated from the start. Third-party IT solutions and services are a cost-effective way to achieve these goals.

The process is based on automation that heads off errors as early in the process as possible. The result for both payers and providers is that the number of claims that require audit is reduced.

The key is not to buy and operate an IT solution in-house. That requires providers and payers to invest in costly infrastructure. When systems are hosted in-house, costs may balloon from building and maintaining systems to procuring hard-to-find technical talent. Cloud-based systems are easily updated, and therefore much more cost-effective.

Where Cloud-Based Systems Fit

Cloud-based third-party IT systems help payers and providers by continuously updating information in two ways.

  1. New provider-payer contracts and processes are more easily revised, especially as value-based care grows as a proportion of healthcare delivery in the U.S.; and
  2. Continuous real-world learning helps mitigate the abrasion in the provider-payer relationship by reducing reliance on audits.

Cloud-based technologies make the automated identification of payment errors smarter and more precise. Systems combine financial data with clinical records, allowing payers and providers to see where a given claim sits within a group of similar claims.

That helps every player in the process assess the probability that a given claim is an outlier, whether the claim is in the pre-submission, pre-payment, or post-payment phase.

Artificial Intelligence and Machine Learning

This is where AI and machine learning come in. AI analyzes claims information—including the codes which underpin the claim—to find patterns that prevent, catch and correct payment errors at the earliest stage possible in the payment cycle.

In the pre-submission or pre-payment phase, machine learning programs make it possible to quickly understand whether or not a claim will be valid or eventually may need to be reviewed. The idea is to examine the attributes of incoming claims and score them for potential savings.

In the post-payment stage, machine learning analyzes data that may help change provider behavior through education. Payers can adopt outreach and education programs tailored for provider staff who code and submit claims. The goal, also, is to reduce the volume of inaccurate claims.

Taken together, these systems provide greater predictive accuracy because they “learn” both from the payer’s internal data, and massive volumes of aggregated historical claims.

From Abrasive to Collaborative

Payment accuracy technologies help curb administrative costs. They promote processes that maximize savings. They change the provider-payer relationship from abrasive to collaborative—from negative to positive. Finally, the delivery of an accurate bill— delivered via a collaborative process—is the foundation for the move to value-based care, proven to deliver positive outcomes at a lower cost.

Payment accuracy benefits the healthcare consumer because plan members become less frustrated when providers and payers do not agree on who owes what to whom. A positive dynamic between providers and payers leads to improved consumer satisfaction.

And isn’t consumer satisfaction a core component of improved healthcare delivery? If the industry’s emphasis—correctly—is on improving patient outcomes, why not make one of those outcomes a far smoother and accurate financial reimbursement process?

Source: Change Healthcare