It’s no secret that insurance denials are one of the most significant challenges facing the American healthcare system today. They represent a major financial burden for hospitals and healthcare systems, costing a staggering $19.7 billion each year.1 So, what’s driving these denials? The primary culprit: missing or inaccurate claims.2 Even a minor error—whether in patient data, coding, or documentation—can result in a denial, with most clinical claim rejections tracing back to incomplete or incorrect patient data.
Clean claims start with getting the data right the first time. So, what’s standing in the way? A significant portion of clinical claim errors arise from incomplete patient data. Missing information, such as the patient’s full name or the date of emergency care, is a common mistake that leads to claim rejections.3 Inaccurate data or insufficient documentation from the encounter is also a frequent contributor to denials.3 When these seemingly minor errors go unnoticed and are not properly addressed before filing a claim, healthcare systems often pay the price, facing higher denial rates and significant financial losses.
Many organizations still rely on manual processes and traditional technologies to properly code and validate medical claims—such as Computer-Assisted Coding (CAC) and other rules-based systems. However, these technologies fall short of modern healthcare demands, as medical coding is complex and requires flexibility—something these rigid systems struggle to provide. Rules-based systems are prone to errors when cases deviate from typical standards, require frequent updates, and often fail to capture the broader clinical context, leading to inaccurate coding.
There are two main costs associated with denials: the loss in revenue from a denied claim and the cost of reworking the claim. The loss in revenue is the direct cost associated with a denial and is the difference between what was billed and what was paid. In some cases, the denial results in partial reimbursement, but it can also result in no reimbursement at all.
The cost of reworking a claim is the indirect cost associated with a denial and is the cost of time and resources spent to resubmit a claim. Healthcare organizations spend approximately $118 per claim to be reworked, which totals $8.6 billion in administrative costs nationwide.4 With staffing shortages and claim backlogs, up to 65% of denied claims are never resubmitted—an alarming trend that is straining healthcare systems even further.5
With denial rates nearing 15% nationwide, healthcare providers are under increasing pressure to adopt solutions that will lead to more efficient and accurate revenue cycle processes.6 Enter AI. Autonomous solutions streamline claims processing, reduce human error, and automate routine tasks, freeing up valuable coder time to focus on more complex cases. By embracing a more innovative approach, healthcare organizations can reduce administrative costs and improve the accuracy and efficiency of their revenue cycle processes.
Furthermore, with properly integrated AI solutions, healthcare systems can access the full contextual data that supports the patient’s visit. Why is this important? A detailed longitudinal record of the patient’s medical history reveals trends, treatments, chronic illness, and outcomes, ensuring that the full patient story is captured when coding the medical claim. This leads to more accurate claim submissions and reduces the risk of incomplete or inaccurate information.
Achieving accurate claims data requires ongoing due diligence. One effective way to reduce the risk of inaccuracies or incomplete patient information in claim submissions is by embedding quality checkpoints throughout the automation process. While AI-driven automation streamlines claims management, checkpoints ensure accurate claims data, compliance with payer rules, and adherence to specific quality standards at every stage. This combination minimizes errors, accelerates processing, and helps prevent costly denials.
Another key strategy for ensuring accurate claims is to leverage a Coding Quality Assessment (CQA) team. CQA experts conduct regular audits of CPT/HCPCS codes, modifiers, and ICD-10-CM code sets to verify the accuracy of each code, reducing the risk of inaccurate or incomplete claims data and improving overall claims performance. By incorporating quality checkpoints and establishing a CQA team, healthcare systems can proactively address clinical claims errors before submission, minimizing their overall risk of denials.
Curious how AI-powered solutions can help you tackle denials and improve revenue cycle management? Read our case study with MGB to see how we reduced coding-related denials by 58.7%, saving over $750,000.
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