The healthcare billing processes are seriously affected by claims denials. The revenue of companies that work in healthcare decreases due to errors in coding, additional costs to rework a claim, etc. Claims analytics helps to minimize these refusals and increase business profitability.
What Is Claims Data Analytics?
Some websites recklessly equate the concepts of “claims analytics” and “claims analysis”. However, these terms and processes have significant differences.
Claims analysis is a more general concept compared to claims analytics. Due to claims analysis the info about all rejected (the claim contains incorrect data), denied (the claim was found unpayable), and accepted healthcare claims is collected. It becomes a base for creating a plan to boost the medical company’s revenue.
Claims analytics is the technical and even mathematical part of claims analysis. Analytics collects and processes the rates at which claims are denied. Analytics arranges this data into structural patterns and makes it easier for analysis teams to use. The more exactly the healthcare company organizes the claims analytics process, the more accurate the analysis will be.
What Is the Use of Claims Analytics?
A survey of 235 medical group leaders was conducted in early spring 2024. 60% of respondents noted that the number of denied claims at their clinics increased compared to the same period in 2023. Only 11% of respondents highlighted that claims denials in early 2024 decreased compared to early 2023. The reasons for the success of these 11% are the measures taken in the field of clinical documentation processing.
For healthcare business owners, unresolved claim denials are fraught with significant annual losses in net revenue. Additionally, errors in healthcare coding can also have financial consequences. A quality claims analytics solution allows a healthcare provider to create a complete picture of accepted and denied claims.
Check the CPT Code
Each invoice with a specific current procedural terminology (CPT) code is associated with payment information and denial details. Claims analytics identifies CPT codes with the risk of being denied or underpaid.
Identify Failure Patterns
Each rejected claim has its details. Without analytics, the provider knows only the reason for the refusal. But the frequency of this problem and its details remain unknown. If a refusal to a particular patient is atypical and unique to a healthcare organization, it will not affect its core revenue. And if there have been many similar failures recently, they must be analyzed.
Minimize Coding Errors
Physicians spend part of their working time on medical coding and billing. Of course, a physician can delegate these responsibilities to staff, but only this physician will be responsible for coding errors or malpractice. Process automation and accompanying claims analytics ensure efficient billing, accurate service coding, and timely reimbursement.
Improve the Contract Terms with the Payer
When a provider uses analytics to organize denials by insurance, they can find how many denials insurance companies return. The provider may be able to trace the problem back to that insurance’s claim requirements. Example: the provider receives another claim analytics report and thinks the number of refusals from the insurance company is off-scale. The provider looks at the details and the reasons. They understand that there are unnecessarily many denials, and review the contract with this insurance company. When the provider and insurer renegotiate the agreement, they should clarify two key points.
The first is language that provides an adequate period to resolve appeals of denials. The second is amendments parties can make to the contract only by mutual agreement. These changes will protect the company’s revenue from losses due to the high number of denials. If the insurance agency is unwilling or unable to resolve major denial issues quickly, the provider must negotiate higher rates. It will help them compensate for lost income and additional administrative costs.
What Benefits Do Claims Analytics Provide to Healthcare Businesses?
Check out three strong points on how analytics can help your healthcare business.
Increase Clean Claims Rate
Clean claims rate (CCR) is a key metric in healthcare revenue management. It shows the number of claims for which data was collected accurately and without errors the first time. A high number indicates that the healthcare provider codes and bills services efficiently and transparently.Â
Reduce the Period of Accounts Receivable
The profitability of a healthcare organization depends on the term of accounts receivable (AR). The higher the days’ charges in AR, the less likely the debt will be repaid. The American Journal of Roentgenology states that with a period of 90 days, the probability of collecting a delinquent account is 73%, and with a period of 180 days it is only 50%. The American Academy of Family Physicians notes that the desirable period is 30-40 days.
Reduce the Cost of Appealing Claims
Often, when insurance companies deny claims, the healthcare provider company will not resubmit most of these denials. The provider must investigate and correct each denial by the insurance company or file an appeal at an additional cost. It costs four times more to appeal a claim than to file it correctly the first time. A provider can save thousands of dollars with the predictive feature of claims analytics because the need to re-process and correct denied claims can be significantly reduced. Claims analytics solutions identify and analyze patterns of underpayments and refusals. It highlights frequent mistakes and the provider can prevent them rather than waste time fixing them later. It does not matter: it is an error on the part of the insurer or an error in coding the service on the part of the provider.
How to Make Claims Data Analytics as Effective as Possible?
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Automate Claims Analysis Processes
Automation is one of the criteria for efficiency and economic benefit. Automated solutions collect and process data as soon as the data enters the system. In addition, such solutions analyze data and immediately identify problems that a person can’t notice due to inattention or other behavioral factors. If the insurance company again refuses to pay, or if there is an underpayment, they will be instantly “caught” by the program. Additionally, claims data analytics makes the denial investigation process transparent and makes it easy to track the success of changes to procedures, policies, and contracts.
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Appoint a Person Responsible for Claims Analytics
If your healthcare business is still small and the billing system is quite simple, you can conduct claims analytics manually. One person or a small department is enough to find patterns in accepted and rejected claims. If the company is large, then the invoicing system is more complex. To keep everything under control, you will need a large team. It is much easier to implement an automated claims analytics solution and then assign a person to manage it.
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Implement and Leverage AI-based Forecasts
Today, claims analytics solutions are traditionally based on collected data and take a step toward new technologies. It’s about using AI to predict potential problems and billing patterns. AI-based forecasts will help suppliers identify issues before they will impact the company’s workflow and revenue.
In Conclusion
Claims analytics makes healthcare businesses more profitable through faster claims processing and billing transparency. Also, this solution increases patient’s loyalty because their AR is reduced.
