Willow co-founder Ian Ye uses AI to automate revenue cycle management for clinics.
Imagine you run a high-end restaurant and 20% of your customers leave without paying, and the rest take up to a year to pay. Sounds silly, right? But this is the everyday reality for healthcare providers. Long waiting times for payments, endless paperwork, and the constant risk of not getting paid at all.
When I began my internship at a pediatric cardiology clinic, I discovered that the insurance claim I filed almost a year ago had not yet been reimbursed. As it turns out, this claim was initially denied due to lack of a prior authorization number, but since the office outsources the claims, until the claims team finally figured out the required information. It took many months of email exchange. It was in the EMR the whole time. Almost a year later, the clinic is finally receiving the money it is rightfully entitled to.
Revenue cycle management in healthcare
Such is the nature of revenue cycle management in healthcare, especially for smaller providers who can't afford to keep billing in-house. A process where providers wait for insurance reimbursement, overcome denials, and resubmit claims due to miscommunication, simple oversights in insurance coverage research, or even fatal typos in patient dates of birth. may begin, and the period may extend well beyond a year before payment. Even if there is one, it will be received.
Additionally, insurance companies often try to pay out as little as possible. Health insurance and health care providers are meant to coexist harmoniously to provide health care, but small practices are often forced into an unfair battle with these insurance companies. That could mean denying claims for trivial reasons, arbitrarily underpaying for services, or creating unfair and opaque contracts for small clinics. there is.
Application of artificial intelligence
So what are the specific ways that AI can facilitate this process? That's the problem that my company and others are trying to solve.
The most obvious improvement is in the claims scrubbing process. Traditionally managed by clearinghouses, this is a tool that allows providers to easily send batches of electronic claims to various insurance payers at once. Payment institutions reject fraudulent claims before they are sent to payers based on certain universal rules, such as formatting issues, incorrect use/combination of codes, and conflicting insurance information.
But AI can learn. By using payer policies as ground truth and learning from all denials, AI tools can create a comprehensive rules engine for each individual payer, ensuring that claims are denied for the same reasons as any other claim. This reduces the burden on claimants to read and memorize the policy. They are often unnecessarily complex and constantly changing. Then, when you access your EMR, AI can automatically resubmit corrected claims or flag issues if information is missing. Eventually, you won't need a billing team. A rules engine allows AI to correctly create claims on its own.
Knowing what to do after receiving a payer response is a process that requires understanding your insurance contract, rate card, and payer policy. AI tools with access to that information can interpret the response along with the medical record and generate payer-specific action items. This can be done by writing a dispute letter for services that are paid for at a lower rate than usual, by using the contract or payer's policy as support, or by attaching diagnostic codes to better justify the services provided. Similar to automatically resubmitting claims that are denied due to medical necessity. The AI uses the payer's policies to follow the steps required for bill adjustments specific to each payer.
AI also has the potential to improve efficiency in front-office operations, especially regarding pre-approvals. Lack of prior authorization is a common reason for refusal and usually cannot be appealed. The process of getting pre-authorized for services requires spending hours on the phone with insurance and completing documentation of medical necessity.
AI can streamline this by 1) identifying the need for pre-authorization for a service based on policy and 2) having an agent follow the payer’s steps to obtain authorization, as pre-authorization varies by payer and service. Masu.
Addressing AI challenges
While we know that AI can theoretically be a tremendous resource for clinics, there is still much work to be done before clinics can fully adopt AI.
Perhaps the most important issue is reliability. Clinics trust their billing teams. No matter how bad your performance is, claims teams have been filing claims for years. Regardless of transparency, AI cannot be held accountable or supervised in the same way as humans. Clinics could use AI to increase revenue and reduce AR days, but the risks of switching to fully automated solutions where decisions are made without human oversight are too great.
Taking a two-pronged approach with both a claims team and advanced claims management software is too expensive and burdensome. It could take a very long time for clinics to start trusting AI that much. Therefore, AI tools should aim to augment existing claims teams by providing tools to increase efficiency.
Another major consideration when relying on AI is the protection of patient health information (PHI). The more layers of software that process this information, the greater the potential for a data breach. The threat of cyber-attacks on PHI is real, with catastrophic data breaches occurring at information exchanges in the past few years.
AI implementation must be meticulous and comprehensive to comply with HIPAA laws and protect against cyber-attacks.
When deployed carefully, AI can help improve revenue predictability and reduce labor costs. As insurance companies increasingly leverage AI to streamline and even limit coverage decisions, clinics could be left behind if they don't deploy similar technology to balance the playing field. There is. When deployed carefully and strategically, AI can help clinics gain predictability of revenue and reduce costs, allowing them to focus on what really matters: delivering care.
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