In the complex and often frustrating world of healthcare billing, claim denials, underpayments, and repeated resubmissions are among the most persistent and costly challenges faced by providers. The process is time-consuming, error-prone, and resource-intensive which results in wasted time and detracts from patient care. Fortunately, advanced machine learning (ML) and artificial intelligence (AI) are transforming this process, by helping providers reduce the time, cost, and frustration associated with claims remediation.
Understanding the Burden of Claim Remediation
Claim denials can occur for a wide variety of reasons, most often from incorrect coding, missing documentation, authorization issues, eligibility issues, or simple human error. For example, according to a report from Premier, nearly 15% of claims submitted to private payers initially were denied, including ones pre-approved during the prior authorization process. 15.7% of Medicare Advantage and 13.9% of commercial claims were initially denied. This is generally due to the significant administrative effort required. While there may be debate around the numbers of claims never resubmitted, in the end more often than not, patients are billed for the remaining amounts.
Patient frustrations aside, this inefficiency results in lost revenue and increased operational costs. Manual resubmission processes involve digging through claim histories, correcting errors, gathering supporting documents, and navigating insurer requirements; all tasks that are not only tedious but also vulnerable to further mistakes. It’s a cycle that drains healthcare organizations and delays payments.
How AI-Powered Solutions Reduce Time and Cost
AI technologies, especially advanced ML and generative AI are being deployed to address these challenges. These tools can analyze thousands of claims in seconds, identifying common reasons for denials by payor and line of business such as Medicare, Medicaid, Commercial, etc. They also highlight discrepancies, and recommend corrective actions with high precision and in real time compared to human teams.
With AI models, these solutions can instantly identify errors and inconsistencies in denied or partially paid claims for missing fields, inaccurate coding, or mismatched patient information. By automating denial review and programmatic resubmission, they suggest the most likely successful corrections based on correct coding edits, payer and state specific rules and historical data. These corrected claims can then be programmatically resubmitted without human touch, minimizing opportunity for errors and improving turnaround time.
Generative AI can help prioritize appeals based on factors like claim value, probability of success, and payer response times. By organizing and auto-populating appeal letters, attaching the correct documentation, and even learning which arguments are most persuasive with specific payers, AI streamlines the entire appeals process using intelligent workflows.
AI also helps to cut down the workloads for administrative teams. Healthcare staff responsible for billing spend countless hours reviewing rejected claims, gathering documentation, and managing communication with insurance providers. This process can be burdensome on large hospitals or healthcare networks handling thousands of claims monthly. AI removes the manual efforts by automating routine tasks and reducing workloads in three unique ways:
- Automating Repetitive Tasks: Such as checking for eligibility, verifying benefits, or compiling documentation using robotic process automation (RPA) integrated with AI decision-making.
- Leveraging Natural Language Processing (NLP): AI can interpret and extract critical information from unstructured data sources such as clinical notes, EHRs, and payer correspondence enabling faster data retrieval and documentation matching for claim resubmission.
- Providing Smart Dashboards and Alerts: Advanced AI systems can flag issues in real time – even before a claim is even submitted – to prevent denials from occurring in the first place.
As a result, administrative teams are freed from the manual drudgery and can instead focus on higher-value activities, such as proactive denial management strategies and improving the patient billing and care experience.
Leveraging Big Data for Continuous Improvement
One of the most transformative aspects of AI in claim remediation is its ability to learn and improve over time. With access to large datasets of claims, payment/denial histories, payer guidelines, and outcomes, AI can identify trends that human analysts would likely miss. For instance, AI can pinpoint systemic issues in documentation, coding practices, or workflows that frequently lead to denials. By predicting these root causes proactively, healthcare providers can improve their first pass claim acceptance rates, minimizing the need for remediation in the first place.
AI analyzes patterns in how specific payers handle certain types of claims, appeals, or coding scenarios, helping providers customize submissions to align with payer preferences and increase the odds of success. By providing insights to payer behavior, they can address the varying requirements and tendencies from insurers. Machine learning models can also integrate real-time feedback from successful and failed claims to continuously refine their predictions and suggestions. As the system gets smarter over time, it offers more accurate guidance for future submissions.
More Time for Patient Care
The goal of claim remediation is not just to improve cash flow but to support the healthcare mission. When providers spend less time battling denials and more time on patients, everyone wins. Examples include:
- Faster Reimbursements: By reducing delays and increasing the efficiency of resubmissions, providers receive payments more quickly, helping stabilize operations.
- Improved Resource Allocation: With fewer staff hours spent on paperwork, more resources can be dedicated to clinical functions or patient-facing services.
- Better Patient Experiences: Streamlined billing processes reduce confusion and frustration for patients, improving satisfaction and trust.
In the long run, AI-enabled claim remediation isn’t just about fixing but about transforming processes to be more proactive, intelligent, and aligned with the values and goals of modern healthcare.
Looking Ahead
As AI continues to mature, its role in revenue cycle management will only deepen. From AI-powered bots that handle full claim lifecycles to predictive models that forecast revenue outcomes, we’re heading toward a future where claim denials become the exception, not the norm. By improving the accuracy of claim resubmissions, reducing administrative burdens, and streamlining appeals with payers, advanced ML and AI enables healthcare organizations to work smarter, not harder. The advanced analytic capabilities of these technologies include trend prediction and continuous learning which lead to fewer denials and faster reimbursements. As a result, providers can dedicate more energy towards patients.
The healthcare industry is often criticized for being slow to adopt new technologies, but that is changing rapidly in the area of claim remediation. AI offers a clear path forward—one that reduces costs, improves accuracy, and gives providers the bandwidth to do what they do best: care for people.
About Jai Pillai
Jai Pillai is COO of Red Sky Health, creators of a proprietary AI platform called Daniel that makes recommendations to reduce claims denials. Daniel identifies claims issues, provides guidance to fix them in real time, and programmatically resubmits the claim. Formed by healthcare and technology startup veterans, the Company’s mission is to ensure that healthcare providers are properly paid for their services by making sure that insurance claims denials are rapidly and comprehensively resubmitted and paid. To learn more, visit them at RedSkyHealth.com or follow them on LinkedIn.
