Claims Analytics in Healthcare: Benefits and Use Cases

As if payer denials weren’t high enough already, a September 2024 brief from the American Hospital Association reviewed in Becker's Hospital CFO Report reveals that commercial claims denial numbers rose 20% from 2022 to 2023. 

Consulting firm Premier, Inc. echoes this rise. When it recently surveyed 516 hospitals across 36 states accounting for 52,123 acute care beds, it found that the denial rate averaged nearly 14%, a 17% rise from 2022’s level of 12%. 

Despite physician outcries and the AMA’s strenuous efforts to lower denial rates stemming from prior authorization issues and more, denial rates just keep rising. 

With the AMA’s efforts insufficient, it’s up to healthcare organizations to control payer denials. While part of the problem lies with payers, provider errors and omissions also trigger denials.  

When denials can deplete five percent of net patient revenue, of course they stay at the top of revenue cycle leaders’ radar.  Recouping parts of that five percent loss can make a difference in the typically razor-thin margins at healthcare organizations (2.3 percent at hospitals in 2024). A little extra cash buys an onsite lab, a new physician, or the framework of a much-needed community service, all moves that establish new revenue streams. 

Adding fuel to the frustration, RCM executives often hear that 85 percent of denials are avoidable. Coding errors, missed deadlines, and insufficient documentation are all claim submission aspects under the provider’s control, but the healthcare staffing shortage makes executing them tricky.

One powerful way to avoid these denial triggers is to use claims analytics. Review all claims analytics use cases and their benefits here to determine if it’s time for your organization to invest in or expand this error-catching technology. 

What is claims analytics?

Claims analytics is the process of using data analysis to examine healthcare claims data. Claims analytics collects, processes, and analyzes large volumes of claims information to identify patterns, trends, and anomalies related to denials, reimbursements, and overall financial performance. 

Claims analytics help healthcare organizations reduce denial rates by providing data-driven insights into the root causes of denials. By analyzing large volumes of claims data, analytics reveal patterns and trends in denials, pinpoint specific issues like coding errors or missing documentation, and proactively address these problems before claims are submitted. 

Additionally, analytics enable benchmarking against industry standards, highlighting areas for improvement in the revenue cycle process. With this actionable intelligence, healthcare organizations can implement targeted interventions, streamline workflows, and optimize their claims submission processes, ultimately leading to fewer denials and improved financial performance.

The difference between claims analytics and claims analysis

Claims analytics and claims analysis are related but distinct processes in healthcare revenue cycle management. Claims analysis is a broader process that involves RCM staff gathering and examining qualitative information about accepted and denied claims. It focuses on understanding overall patterns and trends in claims data, aiming to create plans and strategies to optimize revenue based on findings. This process can be done manually or with basic tools like spreadsheets. 

On the other hand, claims analytics is a data-driven approach that uses advanced technology and statistical methods. Automated claims analytics relies on AI in revenue cycle management and machine learning to provide quantitative metrics and predictive modeling. It delivers actionable insights derived from claims data.

Overall, analytics can process much larger datasets much faster than traditional, manual analysis. By providing granular insights in seconds, it helps organizations get to better revenue faster. 

In essence, claims analytics is a more advanced, technology-driven subset of the broader claims analysis process. 

Manual vs. automated claims analytics

While both manual and automated approaches aim to improve claims processing and reduce denials, they differ in their execution and effectiveness. Manual claims analytics involves staff members reviewing and analyzing claims data, often using spreadsheets or basic database tools.

 Experts claim that the manual approach is limited because it’s: 

• time-consuming and labor-intensive, requiring significant staff hours.
• prone to human error.
• limited in scope due to the volume of data that can be realistically processed.
• difficult to derive complex patterns or trends across large datasets. 
• likely to deliver delayed insights, as analysis often occurs after claims are processed.
• challenging to maintain consistent analysis methods when different staff members do separate analyses.

Automated claims analytics, on the other hand, leverages advanced software and technologies to process and analyze claims data. The explosion of patient data now makes it nearly impossible for a human to review and analyze its full extent, even for one patient. Automation claims analytics is being praised for its:  

•  rapid processing of large volumes of data in real-time.
•  increased accuracy and consistency in data analysis.
• ability to identify complex patterns and trends that may be missed by human analysts.
• predictive capabilities to forecast potential issues before they occur.
• continuous monitoring and analysis, providing up-to-date insights.
• scalability to handle growing data volumes without additional staff.
• integration with other systems for a more comprehensive view of the entire revenue cycle.

Automated solutions can quickly flag potential issues, such as recurring denial patterns or underpayments, allowing staff to focus on addressing these problems rather than spending time on data collection and basic analysis. Forward-thinking healthcare organizations that shift from manual to automated analytics are more proactive in their claims management, potentially reducing denial rates and improving overall financial performance.

While manual analytics may suffice for very small practices with simple billing structures, most healthcare organizations will benefit significantly from adopting automated claims analytics solutions. These tools not only save time and resources but also provide deeper, more actionable insights to optimize the claims process and maximize reimbursements.

Healthcare claims analytics in practice: 4 use cases

A quality claims analytics software solution approaches the claims denial problem on multiple levels.

These four use cases demonstrate how savvy practices use it.

Use case 1: issue identification

Without claims analytics, RCM staff is stuck with reviewing one denial at a time. If Patient X has coverage denied for their MRI, without analytics, all you know is the payer’s reason behind that single denial. You can't see how many other, similar MRIs are getting denied and why. 

Analytics detects the patterns in your denials. Instead of understanding each denial as a stand-alone problem, you get an idea of the larger trends draining more of your revenue. 

When claims analysis reveals the root causes of your most prevalent denials, you can prioritize the right remediations and stop the biggest revenue leakage spots first. 

Use case 2: CPT code analysis

Claims analytics helps identify problematic CPT codes by analyzing large volumes of claims data to detect patterns and anomalies in code usage and reimbursement. 

By examining factors such as payment variances and frequency of use across different providers or specialties, analytics can pinpoint CPT codes with issues. 

For example, it can reveal codes that are:

• frequently denied by payers.
• often used incorrectly or inappropriately bundled.
• show unusual patterns of utilization compared to peers or benchmarks.

This analysis can highlight potential areas of upcoding, undercoding, or misuse of modifiers. By identifying these problematic codes, healthcare organizations can focus their auditing and education efforts, improve coding accuracy, reduce denials, and optimize reimbursement. Additionally, claims analytics can track changes in code usage over time, helping organizations stay ahead of evolving coding guidelines and payer policies.

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. Those that issue abundant document requests or stick with minor details may have a high “hassle factor.” Worse, their contracts most likely do not perform to the level of the other payers in your mix. You may be able to trace the issue back to that payer's claim requirements. Claims analytics can feed into your payer performance monitoring, which delivers the data to back up your contract demands. 

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.

With in-house claims analytics you can determine just what’s triggering your payers’ denials. 

For instance, the complex coding requirements in ophthalmology mean that coding and modifier errors account for a large percentage of their denials. If the issue is severe enough, it could be worthwhile for a physician group or large practice to use an outside ophthalmology coding specialist. Claims analytics support this decision, potentially winning buy-in for the investment from peers and managers. 

How claims analytics and payer analytics interact

Claims analytics and payer analytics are related but have key differences. Claims analytics analyzes healthcare claims data, examining patterns in claims submissions, denials, and reimbursements. .

Payer analytics, on the other hand, takes a broader view of data from health insurance payers or companies. While it includes claims data, it also encompasses other payer data such as member enrollment, premiums, and plan designs. Payer analytics aims to understand overall payer behavior and trends so that healthcare organizations can evaluate managed care contract performance or the value each payer contract brings.  

 Take a quick, self-guided tour through a powerful contract performance optimization and underpayments recovery tool: 

How providers can unleash claims data analytics full potential

Analytics fuel change. The better your data analytics system, the more information you have, and the better your decisions and 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 resolving root causes.

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, however, 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, your organization can be more nimble and proactive. 

The benefits of healthcare claims analytics software

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 reviews in every case until the payer makes a permanent change.

If coding is the problem, your analytics solution will show you where CPT codes are misapplied. 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 are approved on the first submission. A higher clean claims rate impresses buyers and investors. HFMA recommends that organizations strive for a clean claims rate of 98 percent. 

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.

Save costs spent on appealing claims

The cost of denial rework eats into organization profits. The Premier survey mentioned above found that it costs nearly $47 to rework every claim. A Change Healthcare survey put that figure as high as $118. Of course, these costs range by specialty and location. 

If you fix just 5 problems before they happen per week, you stand to save $940 (20 x $47) to $2,360 (20 x $118) per month or $11,280 to $28,320 per year. Imagine the revenue saved if your volume was such that you caught 10 times those errors per week.  

Claims analytics highlights your most common errors so you can invest in preventing them once and less time correcting multiple instances later.

Decrease days in accounts receivable (AR)

Accounts receivable delays correlate directly with practice profitability, according to HFMA’s Best Practices for Resolution of Medical Accounts report. 

Acceptable days in A/R vary by specialty. The RBMA has established 60 days in A/R 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 A/R by minimizing the number of issues in submitted claims. Patients and payers are more willing to pay the initial bill. 

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 spend analyzing claims manually. Manual spreadsheet work requires hours of attention from team members. Because of its labor-intensive nature, this task tends to get overlooked. That means more denials and more revenue lost. 

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 claims analytics + payer analytics to optimize your revenue

In an active merger and acquisitions environment, healthcare finances get scrutinized more closely than ever. Organizations must demonstrate what they’re doing to maximize net revenue. Having claims errors and omissions that deplete reimbursements looks negligent. 

Potential buyers and investors will also be looking at how healthcare organizations manage their payers. Do you have an idea of your best and worst payers? Do you know how long each payer takes to reimburse? Have you created a data-fueled hassle-factor grade for each payer. 

 MD Clarity’s RevFind supports revenue cycle staff in executing all of these tasks. Once it ingests, digitizes, and analyzes all contracts, payment trends become clear. It also makes terms and fees easily searchable, highlights the best and worst payers, and sends alerts for contract renewal deadlines. All of these achievements minimize the labor required to manually determine contract performance. It also compares each actual payment against contracted rates, promptly alerting staff to any over- or underpayments. With RevFind’s help, some of our clients recoup millions in underpayments. 

Additionally, when revenue cycle leaders feed proposed payer changes into RevFind's contract modeling feature, they can get a true picture of how any change will impact their revenue. Users can input their own rates and terms to tweak numbers, creating unlimited scenarios to determine the best balance of rates.  MD Clarity helps providers accelerate their revenue cycle on every level, from payer contract management to claims data analytics.

Schedule a demo today to see how RevFind improves your net revenue and delivers the data you need to negotiate contracts aggressively.    

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