Claims denial management is one of the most important areas of concern not only for patients but also for healthcare providers. After all, medical billing denial represents millions of dollars in lost revenue for hospitals each year. In fact, insurance denials cost each hospital an average of almost $5 million each year, which means there’s so much room for improvement. It should be taken into account that while the recommended rate of claims denials should just be under 4 percent, the actual rate for a majority of healthcare organizations is around 20 percent.

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Effectively managing claims denials can consequently increase an institution’s revenues and collections while protecting patients’ financial interests and affording them access to better healthcare services. In this article, we’ll talk about why medical claims denials happen in the first place and why health providers should use artificial intelligence software to prevent them from happening in the future.

Why Claims Denials Occur

There are many reasons why medical billing denials happen. Some of these can be subsequently corrected if the patient and health provider is able to provide the necessary additional or corrected documentation. However, other types of denials can be more difficult to reverse. Here are just some of the common reasons why they happen:

A claim is not actually covered by the payer Sometimes, claims are denied because the service rendered is not really covered by the insurer.

The claim is missing informationOne of the most common mistakes hospital staffers make is to leave a required field on the claim form. It is important to ensure that all missed fields are filled out before claims are transmitted.

The claim has incorrect patient information It should come as no surprise that if mistakes such as wrong or misspelled patient name, wrong date of birth, and wrong policy number are used on the bill, the claim can be denied as well.

The claim should be more specific – Each diagnosis must be coded as accurately as possible.  

The claim was not filed within the required time period – Claims that are not filed within the required timing window may result in denial as well.

There is duplicate billing on a service – Duplicate billing can happen when a service is performed more than once by the same provider on the same day, or when the staffer resubmits the unprocessed claim to avoid timely filing denial issues. Healthcare organizations must observe proper coding of claims to prevent them from being flagged as duplicates.

The claim is deliberately miscoded to maximize reimbursement If a health payer deliberately uses a higher-paying code on a claim or submits bills piece by piece to receive higher reimbursement, the claim is also likely to be denied. Take note that these can be considered fraudulent activities as well.

Additional documentation or authorization is required – Sometimes, health payers require further medical documentation or authorization in order to adjudicate a claim.

Artificial Intelligence: The Best Solution for Managing Claims Denial

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Naturally, many hospitals and other health organizations struggle to discover very complex denial patterns when they only have conventional tools at their disposal. Legacy revenue cycle management solutions are not able to stop medical billing denials before they happen because they can’t predict which claims will be denied or not.

The most effective way to ease the effect of claims denials on a health organization’s bottom line is to target the root cause of denied claims—something that is only possible with an artificial intelligence solution for denials management.

By quickly studying tens of thousands of individual claims attributes such as health provider location, service codes, patient identifier information, and physician information, a smart artificial intelligence software can rapidly reveal patterns and surface groups of similar claims that are typically denied. This sort of data analytics isn’t something one can do with Microsoft Excel or traditional revenue cycle management solutions.

The insights gained by studying the denied claims can then be employed by the medical staff to proactively improve upstream coding changes, which will make future claims more denial-proof. Thanks to AI’s capability to evaluate groups of denied claims, staffers will no longer have to evaluate individual denied claims. Furthermore, since industry changes—such as those that have to do with legislation and regulations as well as revisions in the billing codes—are likely to take place in the future, an AI solution with machine learning capabilities will be very helpful in this regard as well. This capacity for unsupervised learning is something that really sets an intelligent system apart from traditional tools.

Claims denials impact not just the revenues of hospitals and other healthcare organizations but also their patient’s access to affordable healthcare. By employing smart solutions like artificial intelligence in their claims denials management efforts, healthcare providers will be in a better position to address denied claims, minimizing their occurrence in the future.

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