AI Denials Management Market Map: Recovering lost revenue with robots?
Updated 4/8/2025
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Despite confirming eligibility, checking benefits and receiving prior auth (if required), 15% of claims to private payers are still denied. With the cost to rework or appeal a denied claim $25 for practices and $181 for hospitals, 65% of denied claims are never re-submitted. As a result, 35% of hospitals report at least $50M in annual lost revenue from denied claims (not including lost revenue from underpayment). In short, there’s a huge opportunity for vendors who can solve this pain point.
Understanding Denials Management
Unsuccessful claims typically fall into two buckets:
Rejected claims, which never entered into adjudication because they failed initial validation checks for reasons like incorrect patient or provider information, formatting errors, or invalid coding.
Denied Claims, which have been adjudicated by the payer and result in non-payment or underpayment for reasons like missing or incorrect information, coverage or eligibility issues, authorization requirements, and coding errors.
While rejected claims typically require straightforward corrections and resubmission, denied claims demand a deeper analysis. AI denials management solutions help identify, analyze, and action on these denied claims to ensure that providers receive reimbursement for services rendered. Products in this category perform functions including:
Reviewing denial notifications and interpreting payer-provided reasons.
Conducting root-cause analyses to pinpoint denial issues.
Gathering necessary clinical documentation or additional information to address denials.
Preparing and submitting comprehensive appeals within mandated timelines.
Continuously monitoring and tracking appeal progress until a resolution is achieved.
Technical Foundations of AI Denials Management
At the core of AI-powered denials management lies a sophisticated data integration challenge. Successfully resolving denied claims relies on integrating three critical data sources:
Claims Data : Billing details such as CPT, HCPCS, ICD-10 codes, and modifiers, patient demographics, insurance coverage details, service dates, and complete claim history.
Patient Clinical Data (from the EHR): Detailed clinical documentation supporting medical necessity, physician notes, diagnostic information, treatment plans, medication histories, lab results, imaging studies, referrals, prior interventions, and pre-authorization documentation.
Payer and Medical Guidelines: Incorporates payer-specific policies related to coverage, coding, medical necessity, and submission guidelines, as well as authoritative clinical standards and best practices.
Once the claim data and supporting documentation are integrated, advanced software tools categorize each denied claim using a combination of machine learning classifiers, traditional NLP methods, and large language models (LLMs). The process looks something like:
Extracting and interpreting claim admittance reason codes (CARC) and remittance advice remark codes (RARC) from electronic remittance advice (EDI 835), classifying the denial reason into categories such as authorization issues, lack of medical necessity, coding errors, or other administrative errors.
Deeper analysis to precisely understand the root cause of the denial. This is achieved by correlating CARC/RARC combinations with detailed claim information, clinical documentation from the EHR, and relevant payer and contractual guidelines. Complex reasoning at this stage can leverage LLMs for interpretative insights or employ graph databases combined with NLP techniques to model the chain of events leading to the denial.
Determining the optimal action—whether to appeal, correct and resubmit, or accept the denial. While corrections typically involve straightforward administrative adjustments (such as correcting inaccurate data fields), appeals demand rigorous documentation, precise clinical justifications, and clear references to payer policies or contractual obligations. This scenario is where large language models particularly excel, synthesizing evidence and automatically generating structured, persuasive appeal letters.
An automation layer, often powered by robotic process automation (RPA), typically surrounds this workflow. This automation manages claims and appeal statuses, automatically resolves simple administrative issues via payer portals and clearinghouses, escalates more complex claims to human reviewers, and provides continuous tracking until claims are resolved.
Classification of AI Denials Management Solutions
Given the complex process of understanding and responding to a denied claim, many vendors in this space focus on a specific piece of the puzzle. For example:
Automated administrative workflow solutions streamline administrative tasks such as status checks, EHR synchronization, and document submissions. Examples: Rivet Claims Resolution and Crosby Health. Similarly, Protego Health leverages browser-based extensions for real-time interactions with payer portals, directly embedding claim correction functionalities into existing workflows.
Prioritization-based solutions utilize algorithms to rank denials based on potential overturn rates, financial impact, and required human intervention. Examples: Cofactor AI and Sift Denials.
Generative AI-driven correction and appeal generation tools correct coding errors, supplement documentation, and manage appeals. Examples: Cofactor AI, Crosby Health, Protego Health generate customized appeals by automating clinical and coding documentation corrections.
Underpayment detection solutions identify underpayments in both denied and seemingly paid claims. Examples: Adonis Intelligence, MDClarity Revfind, and Rivet Payer Performance
Real-time denials prevention solutions use real-time monitoring and browser-based integrations to detect issues before claims submission. Example: Adonis Intelligence monitors claim statuses and payer requirements continuously, flagging claims that are likely to be denied before submission.
Beyond these more specialized tools, incumbent end-to-end platforms like Change Healthcare Denial Management, Experian Health, MedMetrix, Waystar are also incorporating AI denial management modules alongside broader RCM processes.
The Future: Predictive Denials Management
While using AI to automate and augment denials management is only now becoming possible with the advent of truly useful LLMs, we also foresee a future where denials go down substantially as this line of reasoning is embedded into every aspect of the workflow from patient intake through treatment, where providers can ensure they are taking actions that are medically necessary and likely to be reimbursed. Some portion of that will be driven by classical “predictive modeling”, but it’s also likely that LLM-based reasoning that can interpret the latest guidelines, payer rules, and contracts, will also play a role here as well.
In such a world—and alongside advancements in interoperability that put comprehensive, longitudinal patient data into the hands of both health systems and health plan administrators—one can imagine a world where the idea of “denials” ceases to exist.