Healthcare organizations are increasingly recognizing the importance of claims data analysis in optimizing revenue cycle management (RCM). As reimbursement models evolve and payer contracts become more complex, claims analytics gives healthcare organizations an opportunity to identify revenue leakage and reduce claim denials.
Denials are of particular concern due to the significant variations between state and insurer. According to KFF, qualified health plans (QHPs) purchased via HealthCare.gov denied 37% of out-of-network claims and 19% of in-network claims in 2023. Denial rates on in-network claims among all insurers fluctuate from 1% to 54%.
Claims data analysis in healthcare gives your organization an opportunity to determine the root cause of denials and address payer underperformance head-on. Effective claims analysis can reduce claim denials and payment delays while promoting better cash flow for your organization. Here’s everything you need to know.
Claims data analysis in healthcare refers to the systematic evaluation of healthcare claims to identify trends and optimize reimbursement processes. To achieve this, you’ll need to:
Once you’ve aggregated this data, you can use it to evaluate payer performance and identify patterns of underpayments and denials. Even delayed payments can have a severe impact on your revenue cycle and threaten the financial health of your organization.
With the right payer contract management software, your organization can efficiently navigate the key facets of claims data analysis, which include the following:
Step one involves collecting relevant claims data, which you can source from:
It’s vital that you can gather this information in real-time, which means integrating analytics tools with your practice management and EHR solutions. This helps track claim statuses and identify potential issues sooner.
Claims analytics solutions allow you to track recurring claim denials and payment delays. For instance, if one payer denies an unusually high percentage of a specific code or treatment, it could indicate a procedural issue on your end or payer underperformance.
Some common billing errors you may uncover during your claims analysis include the following:
Once you’ve identified patterns and trends via claims data analysis, you’ll need to compare your findings to current insurance payer contracts. If insurance companies are underperforming or denying claims in a manner inconsistent with your contract terms, you need to act swiftly to remedy the issue.
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Claims analysis allows you to pinpoint the leading causes of denials, such as:
After you’ve identified the most prevalent causes of healthcare underpayments and claims denials, it’s time to recapture lost revenue. Adopt a data-driven claims management strategy that will facilitate efficient appeal processing.
That’s where a platform like Rivet Health can help. Our dynamic solution includes the tools you need to find and fix RCM shortcomings. You can explore historical claim data, receive automated alerts to track appeal deadlines, and promote the financial health of your organization.
Claims data analysis gives you actionable insights into payer performance. During contract negotiation, you can use this data to obtain more favorable reimbursement terms.
Claims analytics also reveals reimbursement discrepancies by comparing contracted rates with actual payments received. This allows providers to recover lost revenue and promote stable cash flow.
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Investing in claims data analysis technology will enable your organization to:
Additionally, claims analytics solutions like Rivet Health enable your team to prioritize denials with the highest likelihood of second-submission approval. You can automate many aspects of the resubmission process, thereby recapturing lost revenue and putting a stop to costly leakage.
Effective claims data analysis in healthcare involves overcoming several hurdles, such as the following:
Working with multiple payers can make gathering and analyzing claims data more complex due to the varying forms and protocols involved. Therefore, you must focus on commonalities across payers.
If you identify denial or underpayment trends specific to a single insurer, then you can examine their unique coding standards and submission formats in greater detail to determine the root cause of the problem.
The larger your dataset, the more relevant the insights will be. Unfortunately, it can be challenging to gather historical claims data, which may limit the relevance of your insights. Ensure that you are using integrated analytics tools that promote interoperability with your EHR and billing solutions.
The reimbursement process is complicated. Each payer has varying reimbursement structures based on your contract terms and their internal policies. These variations can make analyzing and identifying trends like underpayment more difficult.
A low reimbursement for a procedure may represent the norm for one payer while being considered an underpayment for a different insurer.
A lack of integration creates data silos and hinders your ability to perform claims analysis. Fortunately, Rivet Health Claims Resolution integrates with all of the top EHR and practice management solutions. Unlock the full potential of your claims data with Rivet Health.
Along with Claim Resolution, Rivet also offers many other features, such as the following:
Schedule a demo with Rivet Health and leverage our cutting-edge tools to streamline claims data analysis.
Have more questions about revenue cycle optimization and how Rivet Health can help your organization capture more revenue? Check out our extensive library of free educational resources, including guides like What is a Fee Schedule.
