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Claim denials consume up valuable time, cost you money, and negatively affect patient satisfaction. You may get beyond this challenging obstacle by employing the proper denial management solutions.
In addition to the final result of diminished profitability and revenue loss, denied claims in healthcare frequently result in increased complexity, extended payment timeframes, and additional work for personnel. For many hospitals and health systems, high claim denial rates are a present and important problem.
According to a 2021 poll by Harmony Healthcare, 33% of hospital administrators said their organization’s average claims denial rate was more than 10%. 18% more respondents mentioned an average denial rate of 8% to 10%.

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Healthcare providers employ top medical billing companies with the ultimate goal of reducing rates and improving productivity. This objective is directly supported by choosing the best denial management system, however, there are numerous choices on the market. Traditional methods and procedures to manage denied claims frequently fall short since there are so many potential reasons for one.
Automation powered by AI that is tailored to the needs of healthcare providers with regard to claims offers a comprehensive strategy for handling denials.
Throughout the claims management process, denials may occur. Let’s go over some of the major areas where denials are frequent and how providers can best respond to them.

Claim Denials due to Eligibility

On the surface, eligibility might seem like a straightforward idea: Either a patient is qualified for treatment or they aren’t.
A medical billing refusal, however, can result from a number of circumstances, including modifications to a patient’s insurance status, revisions by payers regarding the services, drugs, and equipment that certain plans cover, as well as other factors.
According to research by 2020 Change Healthcare, “Registration/Eligibility” was the primary reason for denials, accounting for 26.6% of claims that were denied. Since 2016, the category has surpassed all others in terms of the overall number of claims denied.

Due to this, eligibility is a major concern for providers and must be meet in order to optimize claims processing.
It is insufficient to rely on personnel or an external supplier that uses human procedures to gather and enter the crucial data that establishes eligibility. The process takes a long time and is full of potential for human mistakes.

A specifically designed machine learning (ML) solution for revenue cycle management. Including rejections, need to be able to gather pertinent data and ascertain eligibility by scanning an insurance card. This method of establishing eligibility avoids early mistakes and deals with a significant issue in the main reason for refused claims.


Pre-authorization guidelines, procedures, and standards are constantly evolving.
Updates made by the payer on which treatments, medications, devices, or other services call for pre-authorization might swiftly result in an increase in denials without an equivalent change made by the provider. The addition of new codes, forms, or claim filing procedures can also do this.
Systems that are unable to change to meet new demands encourage providers to employ laborious methods for handling denials, increasing their level of uncertainty.

According to the kind of health plan involved, there is a considerable variance in prior authorization denial rates, according to research by the American Hospital Association. Medicare Advantage (12.4%) and Medicaid Managed Care (14.7%) topped the list, followed by commercial PPOs (11.3%) and HMOs (9.6%). The complexity of managing pre-authorization is only increase by the variance in the risk of a refusal dependent on the kind of health plan a patient uses.
In an effort to meet important demands connected to prior permission. Providers may use consultants, more staff, robotic process automation (RPA), and similar add-on items. As part of the pre-authorization procedure, these standards call for updating patient information, confirming health insurance, and appealing any associate refused claims.

These methods frequently need ongoing funding, upkeep, and supervision. Brittle processes that can’t adjust to frequent changes in pre-authorization workflows can significantly reduce efficacy and raise the number of medical claim denials in the case of RPA and similar tools.
The pre-authorization environment itself is just as dynamic as effective automation. In order to function consistently and competently. The ideal AI and ML combo adapt to new processes and circumstances as they occur.

Claim Denials Due to Incomplete or Wrong Information

For efficient claims processing and to prevent denials, precise and comprehensive information gathering is essential. Before a provider may fairly anticipate receiving payment from a payer, all information must be correct.
According to the same 2020 Change Healthcare study, rejections were caused by missing or erroneous information in 17.2% of cases. In other words, almost two out of every ten refused claims are the result of incomplete or incorrect information provided to an insurance payer.

According to the MGMA Health Insurer report card, inaccurate coding, whether it’s utilizing a mismatched ICD. Or obsolete CPT code or inputting erroneous patient information is one of the most prevalent causes of rejected claims. These mistakes may be corrected and prevented by having a specialized staff of denial management professionals properly screen claims. Saving you the aggravation and expensive expenses of dealing with an initial denial or failed appeal.

Changing a rejected claim after the fact helps reduce denials, but this tactic puts more work on the staff. A consultant, service provider, or add-on digital tool may charge a higher fee for other popular denial management services. A system that can guarantee that all required data is obtained and shared when a claim is initially submitted results in a more effective procedure. Providers are better able to prevent rejections in the first place and handle them when they do happen.

This demand may be more than adequately met by a strong automation platform that combines AI and ML. Ensuring that everything from pertinent codes to patient and provider details is include. Additionally, with the assistance of revenue cycle specialists, the system may gradually learn how to handle uncommon and exceptional circumstances.

Claim Modifications, Bundled Services, and Other Denials

Claim denials can be caused by a service that has previously been combined with another billable service. Wrongly applied modifiers, omitting necessary supporting paperwork when applying a modifier, and other mistakes.

Careful denial management is the key to maintaining a sustainable cash flow. You may do this by hiring a staff to specialize in collecting and processing claims as well as identifying frequent reasons of denied claims in your company. You will be able to minimize revenue loss. Make informed business decisions that will prevent future denials, and decrease your denial rate as a result of this.

Even seasoned professionals may find it difficult to keep track of every need, and exception. And detail has given the sheer number of potential problems. That might result from a certain mix of services, modifiers, and other factors.
Strong automation is a potent substitute for human-led workflows. And less capable technology that can’t evolve to meet changing regulations, forms, and procedures. While the solution itself handles the majority of the work, the human is in the loop. The revenue cycle professionals who support the system’s continuing development, provide targeted help as needed.

How to Manage Denials Effectively

BMB provides hospitals and healthcare systems with Unified Automation, an AI and ML-powered solution that observes, learns, and executes. In order to address the surge in rejection rates. Brittle and inflexible solutions like RPA or growing human resources through new hires and consultants are insufficient due to the numerous dynamics. Frequently changing components and overall complexity involved in the claims denial process.
Any supplier may simplify denial management thanks to the dependability and adaptability of AI and ML. As well as the targeted assistance offered by revenue cycle specialists.

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