Claims Analytics in Healthcare: Benefits and Use Cases

Claims denials exact a severe toll on a practice's revenue stream, from the cost to rework a claim to the direct loss of income. Practices need to minimize these denials to maximize their bottom lines, and claims analytics solutions are the best tools for making that happen.

Read on to learn about the power of claims analytics and how it can improve your practice's short- and long-term profitability.

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What is claims analytics?

Claims analytics uses data to analyze healthcare claims and their results — rejection, denial, or acceptance. Today, most analytics procedures use technology in some way. Some are fully automated, while others use more basic software like databases and spreadsheets.

Claims analytics allows a practice or system to identify trends in claims processing. It returns critical information such as per-payer denial rates, reimbursement speed, and coding error frequency.

How is it different from claims analysis?

Although you might hear the terms "claims analytics" and "claims analysis" used interchangeably, they're different processes with different goals.

Healthcare claims analysis is the broader process of gathering information about accepted, denied, and rejected claims. It uses that information to create a plan to optimize revenue.

Analytics is the mathematical part of that process. Today, it's usually the technical component as well. Analytics collects and processes hard data like claims denial rates. It then organizes that data into patterns so analysis teams can apply it.

Tech-supported analytics makes the analysis process faster and more accurate. It still takes a human being to apply the insights gained through analysis, but analytics leads to better results.

Denied claims on the rise spur the need for claims analytics

Healthcare providers have seen their denied claims increase by 20% over the past five years. When Harmony Healthcare surveyed 131 providers in 2021, 33% reported average denial rates of 10% or more, bringing them close to a danger zone.

The Kaiser Family Foundation calculated an average in-network denial rate of 18% for the 2020 fiscal year. This number comes from an analysis of denials and appeals for non-group qualified health plans available through HealthCare.gov.

The data indicate significant variation contributing to that 18% average. Some providers saw denial rates as high as 80%. For hospitals, unresolved claim denials lead to an average annual loss of $5 million — as much as 5% of net revenue, according to the Journal of the American Health Information Management Association (AHIMA). Given these trends, AHIMA concluded that the best way to reduce the financial impact of a denial is to prevent them.

These statistics point to a critical need for healthcare claims analytics. By identifying trends in denials, analytics can help practices mitigate or eliminate those denials.

Healthcare claims analytics in practice: 4 use cases

Although there's a systemic increase in claims denial, each practice and health system can reduce those denials for themselves. A quality claims analytics solution approaches the problem on multiple levels, allowing you to find and address trends in denials.

Here's how a savvy practice can use such a system.

Use case 1: Issue identification

Without claims analytics, every denial exists in a vacuum. Say Patient X had coverage denied for their MRI. Without analytics, all you know is the reason behind the denial. You don't know how often that same issue happens. You can't see what coverage details those denied claims have in common.

Analytics turns denials into information you can use by showing you the patterns in your denials. Instead of understanding each denial as a stand-alone problem, you see how common that particular issue is.

This step makes it easier to decide what to do next. Patient X's denial may be unique to their situation and uncommon in your practice. It doesn't call for transformative action. But if it's an issue that has presented itself repeatedly in recent months, it's worth a closer look.

Claims analysis allows you to identify and prioritize those issues that lead to denial.

Use case 2: CPT code analysis

Current procedural terminology (CPT) codes should help you resolve claim denials, not cause them. Claims analytics connects your CPT code billing with payment information and denial rates so you can learn from the process.

You can learn how many times your practice billed a specific CPT code. From there, it's possible to uncover which CPT codes or modifiers have the most denials or underpayments associated. If specific codes have disproportionate denials, you can dive deeper and discover what else those claims have in common.

Use case 3: Payer analysis

By using claims analytics to organize denials by payer, you can identify whether any payers return a disproportionate number of denials. You may be able to trace the issue back to that payer's claim requirements.

A simple review of those requirements — and retraining billing professionals if necessary — could reduce your denial rate.

Payer analysis also gives you more leverage when negotiating payer contracts. According to ECG Management Consultants, a Siemens company, managed care can use denial data to amend contracts in a way that protects revenue.

The same information can help your organization negotiate new contracts more effectively. Preventing issues with future payers is easier if you understand why and how it happened with your problematic current payer.

Use case 4: Denials management

According to Kaiser data, a lack of referral or prior authorization drives 10% of claim denials in marketplace plans. Another 16% occur because the patient's plan excludes the claimed service. Just 1.7% of non-behavioral health claims get denied for medical necessity reasons.

These kinds of insights are possible because of claims analytics. They offer some direction by pointing you toward a common problem, but they don't consider the factors unique to your practice.

Ophthalmology practices are the perfect example. Due to the complex coding requirements in ophthalmology, coding and modifier errors account for a large percentage of denials in this specialty — more so than national data might indicate.

In-house analytics is even more precise. When you can run analyses independently, you collect data specific to your organization.

You can allocate resources to the issues that directly impact your bottom line. For example, if coding errors drive most of your denials, you'll likely spend more time working with your billing and coding team than your contract negotiators.

How providers can unlock claims data analytics to its full potential

Analytics drives change. The better your data analytics system, the more information you have, and the better your strategies will be.

Here's how to build that optimal system.

Adopt an automated claims analytics solution

Automated claims analytics solutions are the fastest and most cost-efficient way to perform claims analytics. They free you from the need to manually collect and organize data, a process that leaves too much room for human error. Automated solutions aggregate data as it comes in.

Automated solutions also point you toward problems you might not see on your own. They call your attention to patterns of payer underpayment or denial that would otherwise go unnoticed. Armed with this information, you can focus on resolution.

The right solution will even help you manage your denial investigation processes. They keep all stakeholders informed and make it easy to track the success of policy, procedural, or contract changes.

Have a department or individual responsible for claims analytics

It's possible to conduct claims analytics manually, especially if you have a smaller practice with simple billing systems and few variations. In these cases, an individual or small department can organize claims denials using spreadsheets and visually scan for patterns.

A manual system will be harder to sustain if your practice is larger or your billing system is more complex. Code organization becomes more challenging when your practice handles advanced procedures or multiple billing codes. You'd need a larger team with high-level expertise to get results comparable to what's possible with automation.

A smarter solution is to adopt an automated solution and designate a person or department to its management. Because software solutions are so much faster than manual analysis, individuals or teams can better use the data and have a more significant impact.

The benefits of healthcare claims analytics software

Software analytics is the first step to action. A good software solution allows you to do more with your data and do it faster, but its value lies in what you do next.

With healthcare claims analytics software, you can spend less time crunching numbers and more time applying solutions. Consider these four processes that develop directly from data insights.

Identify revenue opportunities from denials and payment variance

The Journal of AHIMA reports that healthcare practices can recover up to two-thirds of denied claims, yet organizations fail to resubmit 60% of denials. Claims analytics solutions enable your practice to rework and appeal more denials.

High-quality solutions automatically detect patterns of denials and underpayments from payers. Those patterns show you where to focus your attention and resources.

A software solution will show errors wherever they appear — on your side or the payers. If your payers make contractual errors, you can create a repeatable procedure for highlighting those errors and requesting a review.

If coding is the problem, your analytics solution will show you where it happens most. You can take this data to your billing and coding team and jump-start the troubleshooting process.

Improve clean claims rate

Successfully reworking denied claims will improve your bottom line, but it's also essential to increase your clean claims rate (CCR) — the proportion of your claims that don't need edits before submission to payers.

A claims analytics solution will highlight the administrative errors that need editing before submission. Training your billing and coding team to double-check these errors will strengthen your CCR and reduce your risk of denial.

Decrease days in accounts receivable (AR)

Accounts receivable delays correlate directly with practice profitability. According to the Radiology Business Managers Association (RBMA), the likelihood of collecting a delinquent balance drops to 50% from 73% between 90 and 180 days in AR.

The RBMA has established 60 days in AR as the acceptable maximum, but that varies by practice type. For example, the American Academy of Family Physicians recommends that claims stay in AR for no more than 30 to 40 days.

Claims analytics helps to reduce days in AR by minimizing the number of issues in submitted claims. When claims are cleaner, they go through processing faster, and the practice receives payment sooner.

Save costs spent on appealing claims

According to the Healthcare Financial Management Association, reworked claims are four times as costly to process as an initial claim. These extra costs can bring the overall cost of denial reworking to 20% of your revenue cycle expenses.

Beckers Hospital Review has estimated that reworks cost a practice or health system $118 per claim. If you fix just 10 problems before they happen, it adds up to $1,180 in savings. A hundred fixed issues save you $11,800.

Claims analytics lets you solve more of those problems faster. It highlights your most common errors so you can spend more time preventing them and less time correcting them later.

Improve productivity by reducing manual spreadsheet work

If you've been resisting claims analytics automation due to the up-front cost, consider how much you'd spend doing the same thing manually. Manual spreadsheet work requires hours of attention from team members who could spend that time on tasks that directly generate revenue.

Automation does the data collection, sorting, and processing for you. It presents valuable insights in visually digestible formats, allowing you to identify and act on issues quickly.

Get healthcare payer analytics with MD Clarity Software

Ready to experience the revenue benefits of fewer denials? Get the solution that combines claims analytics with overall revenue cycle management.

MD Clarity is here to help providers accelerate their revenue cycle on every level, from payer contract management to claims data analytics.

Request a demo today and see how MD Clarity can bring transparency to your billing and claims processing.

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