The fewer healthcare claims that are rejected, the better. Is there a more comfortable feeling in healthcare? Almost 15% of all claims filed with a private payer are initially rejected. In 2023, qualified health plan insurance companies sold on Healthcare.gov rejected 19% of claims in the network.
What can you do to start moving that needle in the right direction? Honestly, we need better AI. Listen!
There is little more personal than your own health, so when computer algorithms run by artificial intelligence refuse to care, it is upsetting for both patients and healthcare providers. Humans want other people to make decisions about their claims, but that is rarely how those decisions are made anymore. In a perfect world, there is no denial, but it is necessary in the current US healthcare system. Insurance fraud and excessive claims will be ramped if the payer covers all claims. There must be a balance between what the payers cover and what they don't have. These increased rejection rates result in clear imbalances.
How can I tilt the scale to reduce the rejection rate? AI has been the first decision maker for years, but it needs to be better trained. And to do that, you need help.
Refusal AI:
Over the past few years, there have been large investments in automating the claims and review process in the health insurance industry. This technology has already led to improvements in patients and healthcare providers.
Less boring tasks: Adding AI to the claims process allows clinicians to spend more time caring for patients through peer-to-peer reviews, instead of reading all billing line-by-lines. Free time to see complex claims that may require that extra human touch. Faster billing: Instead of waiting days or weeks to learn billing status, AI can make almost instant decisions about billing. If a request is denied, the AI model can provide immediate feedback and allow the provider to resubmit it for approval. 15% of claims are initially denied, but 54% of claims that are denied are ultimately paid. If the denial is due to what is missing in the claim, the faster you know it, the more you can correct it and receive payment. Better revenue cycle management: Time is money. If the billing process is slow, the provider may not be paid on time. Speeding up the process with AI will give providers a better understanding of when they will be paid.
Improvements in AI for healthcare rejection
Of course, you can't throw AI into problems or expect it to be fixed immediately. It will take time. It takes work to polish your AI model to get as close as possible. Remember when the generated AI model first created the image. People looked barely human – their hands may have eight fingers and their torso may be as long as a person's feet. However, years of data and human adjustments have taught these models to create photographs and realistic images. Healthcare's claims AI is sometimes in the stage of development's goofy image. This requires more time and data. Fortunately, there is a lot of data in the US healthcare industry. What these models need is more human corrections, and no one is better suited to support AI models than clinicians who deal with them every day.
This improves when clinicians insert their own claim knowledge into these models. Information such as consideration of patient history and evolving clinical guidelines, as well as why they decided to care. The more the AI models understand the decision-making process, the better AI will be. This human tweak of these AI models adopts healthcare claims AI from 8 finger photos to photos you swear.
Bridging payer provider gaps
Payers are working to bring the healthcare industry finances to light. The provider is committed to treating patients. These benefits may not always match. Therefore, the reason for rejection. So, even if AI usage increases in claims, there is a way for providers to stay one step ahead of the process.
Predictive Analytics: Healthcare systems need to create or incorporate their own predictive AI models to better understand and prepare payer billing models. Think of this as ramming recipes for your precious family. This is an additional step that a provider can take before filing a claim to protect the provider from being denied. Education: Predictive analytics will help more providers better understand the playbook of payer billing models and what they need to do to ensure approval. If clinicians continue to reject the same claim, healthcare providers must find ways to reach and educate them on how to lower the rejection rate.
Artificial intelligence does not rule out rejection and should not. However, AI can reduce them and continue to speed up the approval process. As technology advances, becomes more common and more successful, trust continues. Clinicians can focus more on patients instead of searching per line to ensure they are approved. Patients will be too worried about denial and will put energy on healing. AI will stay here, and as long as the best-intentioned experts guide the right path, health care will improve and rejection rates will decrease.
Photo: Utah778, Getty Images
Christine Smith Stetler, RN, Solutions Engineering AVP at GeneanAlytics is passionate about bringing medical data and technology innovation directly to consumers and their caregivers, helping them live their best lives. For over 20 years in her favourite field, Christine has supported both patients and fellow clinicians directly, practically and technically.
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