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How to use Automation and AI to Decrease Claim Denials

by bellmedex
claim denials

Health systems of all sizes continue to face expensive claim denial issues, and the incidence of claim denials has been continuously rising since 2016. The pandemic only made the problem worse; by the third quarter of 2020, the initial claim denial rate had reached 11%. This indicates that more than one in ten claims are reject. Hospitals are losing out on significant money because the vast majority of these denials may be avoided or correct. Why does this issue keep getting worse? Claims administration is becoming more and more challenging due to the numerous complexities in the procedures and laws, and the majority of health systems mainly rely on human resources rather than technology.

But adding more workers won’t address the core issues because human workforces are already overburdened. The good news is that today’s solutions to lower claim denials and deliver a profit are power by automation and artificial intelligence. There are potential to use artificial intelligence at every stage of the hospital revenue cycle to prevent expensive denials. AI may reduce human mistake, lengthen staff claim processing time, and offer insightful denials management software advice, all of which reduce revenue loss and enhance the hospital’s cash flow and cash management. This is how:

Ensuring eligibility is correct

Errors and inefficiencies can arise even at the initial patient engagement, which can result in problems down the line. The entire revenue cycle benefits from ensuring precise eligibility by automating benefit verifications with AI, including fewer denials. In actuality, problems with eligibility and registration result in the denial of 23.9% of claims. Many of them are the result of erroneous benefit information being load into the EHR or changes in coverage occurring between the initial appointment schedule and the visit. By reviewing coverage more frequently and minimizing benefit pull errors. Intelligent automation can supplement your current EDI Real-Time Eligibility checks, lowering the number of denials brought on by incorrect or outdated benefit information.

Automatic prior approval

Prior permission and medical necessity problems account for a significant portion of rejections. In addition, if the number of prior authorizations rises, the quantity of denials is probably going to rise as well unless considerable process improvement measures done. The prior permission process is currently one of the hospital revenue cycle’s most labor- and high-touch steps. By automating various steps in the process, from determining whether an authorization is necessary to assisting in the submission of prior auth requests with data from the EHR to continuously checking prior authorization statuses, an end-to-end AI-powered prior authorization solution reduces denials. This lessens errors that result in subsequent denials.

Status checks for claims

Claims status checks are a required step in the management of healthcare claims, although they are not always beneficial. Frequently, after checking the status of a claim, no more action is required. However, personally monitoring a claim’s status takes an average of 14 minutes, and when you include in the volume of claims and the ideal frequency for evaluating them, it becomes clear that humans are unable to do this task. It is a great candidate for automation because it is a straightforward, repeated process. However, how can this lower denials? Most revenue cycle departments consistently fall behind on claims, which leaves them with no time to rework denials that could be reverse and no time to enhance other revenue cycle processes that would denials in the first place.

At the moment, 48% of claim denials and rejections go unrelieved or unworked. Automating claims status checks may not, in and of itself, minimize denials. But it can save a lot of staff time that may be applied to other revenue cycle phases, such as resolving denials.

Denials management with AI

Artificial intelligence can assist health systems in recovering more of these claims after they have been denied. Automated claims status checks, as previously mentioned, are actually the initial stage of a rejections management system powered by AI. Artificial intelligence can automatically correct any minor mistakes after the claim is marked as “denied” and resubmit it. AI can forward a denial to a human for more complicated issues. But it will provide thorough patient and denial information, considerably reducing the amount of time required for rework. A rejections management system powered by AI ensures that all possible reimbursements are collected and that denials are actually handle, boosting income and decreasing days in accounts receivable.

Insights from deep learning on rejections

Unlike straightforward RPA solutions, artificial intelligence has the capacity to examine the data behind claims procedures and produce useful insights. For instance, it was discover at one health system that a particular drug refusal brought on by the absence of prior authorizations and medical need. With this knowledge, the hospital was able to identify and address this recurrent problem. The data your firm uses on a daily basis has opportunities for process improvement. All you need is artificial intelligence (AI) to uncover them.

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