Claims Denials management is critical for healthcare providers to optimize revenue and reduce financial leakage caused by claim denials. We combine the approach of using Claims Data to identify denials patterns by using AI and Machine Learning; our automations can invoke automations pre-submission and post denial. Our approach to solving the denials problems is:

Root Cause Analysis: Integrated AI helps identify the root causes of denials across the revenue cycle. 

Some items include:

1. Consecutive Account Logic – Causes denials and compliance concerns with government payers. Recommendations for remediation can be solved with reports, supporting data and automation to resolve these issues either before or after they occur.
2. Category II Codes Leaking Revenue – Category II codes are commonly used for reporting, but due to their $0 charge amounts, they typically aren’t reviewed thoroughly. For example, a Pregnancy Billing scenario where $0 Category II codes were not being rebilled as their representative charges when the patient’s pregnancy concluded at a different organization.
3. Provider Based Billing – AI identified problems with Provider Based Billing claims caused by service location PBB configuration issues. AI determined that a specific combination of billing provider and modifiers was causing the denials, allowing the organization to rectify their build, institute automation and prevent future denials.
4. Authorization Denials – Using AI to analyze successful and unsuccessful Authorizations historic trends and then using automation to request Prior Authorization correctly prevents the denial from occurring in the first place and leads to operational efficiencies.
5. Registration Denials – AI identifies front-end errors related to incorrect coverage identification and filing order. By analyzing coordination of benefits information against subsequent rebills of those services, AI found gaps in Medicare filing order logic. In cases where claims were initially sent to the incorrect payer and had to be rebilled to the appropriate payer using automation.
6. Attachment Denials – AI and Automation ensure that proper attachments are added to the claim before processing and correct those that have been denied.

Our solution is composed of several parts:

1. Claims Denials Intelligence Building. Using AI, we can take a historical view of the 835 files from your top payors to identify key high-level trends of denials by payor, CPT code, diagnosis, physician and patient demographics. In addition, we gather data from the payors regarding rules around claims submission and any data from the internal patient financial services to provide further insight.

2. Intelligence Consumption. This approach, in time, will provide incredible value in that it can react and adapt to Payor changes but also ensure that the claims submitted will be completed and have a higher chance of acceptance. The outcome will be capturing lost top line revenue and providing specific in-house intelligence for prior authorization using predictive, generative and analytic AI intelligence. This can be consumed in a platform independent way (agentic AI Bots, data analytics programs, ChatBots, Epic or other applications that have API integration).

This is a feed and use the “brain” approach all while giving an operations team the tools it needs to be efficient, save on costs and capture the revenue that would otherwise be written off. Payors are increasingly using AI, so this is a crucial strategy for Providers to have this on their roadmap.