Overcoming Claim Denials with Intelligent Automation RPA & AI
The healthcare industry is facing a significant challenge when it comes to managing denials. The 2022 Revenue Cycle Denials Index, published by Change Healthcare, highlights the current state of denial management in the industry. According to the report, denial rates have increased from 6.91 percent in 2020 to 7.93 percent in 2021. The report also highlights that claim denials cost the average hospital approximately $5 million annually.
One of the major contributors to the increasing denials is legacy technology. The healthcare industry is facing challenges due to ongoing regulatory changes, the constant need for updating revenue cycle management/hospital information systems, workflows that are not automated for clinical attachments or to manage highest-priority denials, and the limited investment in modern analytics/artificial intelligence (AI) that can flag denials of pre-submission relative to payers.
Healthcare providers can benefit from streamlining denial management through intelligent automation. There are various use cases of robotic process automation (RPA) and AI in denial management that can significantly reduce the costs associated with denial management, improve efficiency, enhance accuracy, increase revenue, and improve the patient experience.
Ten Use Cases of RPA and AI in Denial Management
The first use case is automated claims processing. With RPA, bots can be trained to handle mundane and repetitive tasks, such as the submission of insurance claims, verifying eligibility, and processing payments. This frees up human staff to concentrate on more complex tasks that require judgment and redecision-making. By automating the claims process, organizations can reduce the processing time and minimize the chances of claim denials, resulting in increased customer satisfaction.
The second use case is real-time eligibility verification. By harnessing the power of AI, healthcare providers can verify patient eligibility in real time, identifying potential issues before claims are submitted. This can help prevent claim denials due to inaccurate or incomplete patient information, which can save time and improve cash flow for healthcare providers.
The third use case is predictive analytics. By analyzing historical claims data using AI-powered algorithms, healthcare organizations can identify patterns that may predict claim denials. This information can be used to develop proactive strategies to prevent denials before they occur, such as improving documentation practices or adjusting billing procedures. By leveraging predictive analytics, healthcare organizations can optimize their revenue cycle management and reduce the financial impact of denied claims.
The fourth use case is automated appeals processing. With RPA, bots can be programmed to handle the repetitive tasks involved in the appeals process, such as collecting and organizing documentation and communicating with insurance providers. This helps ensure that appeals are processed quickly and accurately, reducing the burden on staff and improving the likelihood of successful appeals. In addition, automated appeals processing can lead to faster resolution of claims disputes, which can improve patient satisfaction and trust in the healthcare system.
Identifying Fraud and Abuse
The fifth use case is fraud detection. By analyzing claims data using AI-powered algorithms, healthcare organizations can identify patterns of fraud and abuse, which can help prevent denials caused by fraudulent claims. AI can also help identify anomalies that may indicate fraudulent behavior, such as billing for services not rendered or submitting claims for patients who do not exist. By using AI to detect fraud, healthcare organizations can improve their revenue cycle management and reduce the financial impact of fraudulent claims. Additionally, fraud detection can help improve patient safety by ensuring that only valid claims are paid, reducing the risk of unnecessary or harmful procedures.
The sixth use case is automated claim status checking. RPA bots can be programmed to check the status of claims and identify any issues that may result in a denial or rejection. This can include checking for missing documentation or identifying errors in the claim submission process. By automating claim status checking, healthcare organizations can reduce the burden on staff and improve the accuracy and efficiency of the claims process. This, in turn, can lead to faster resolution of claims and improved cash flow for healthcare providers.
The seventh use case is automated claim corrections. RPA bots can be programmed to automatically correct claims that are denied or rejected, reducing the need for manual intervention and speeding up the claims process. This can include correcting errors in the claim submission process or resubmitting claims that were denied due to missing documentation. By automating claim corrections, healthcare organizations can improve the efficiency and accuracy of the claims process, leading to faster resolution of claims and improved cash flow. Additionally, automated claim corrections can help reduce administrative costs and free up staff to focus on more complex tasks.
The eighth use case is identifying patterns and trends. AI is a powerful tool that can be used by healthcare providers to identify patterns and trends in claims denials. By analyzing large volumes of data, AI can quickly and accurately pinpoint areas where issues commonly arise, such as coding errors or missing documentation. With this information, providers can take proactive steps to address these issues before they result in denials.
The ninth use case is predicting denials. In addition to identifying patterns and trends, AI can also be used to predict which claims are likely to be denied. By analyzing historical data and using machine learning algorithms, AI can identify factors that contribute to denials and provide providers with real-time alerts when similar patterns are detected. This allows providers to take pre-emptive action to reduce denials, such as submitting additional documentation or correcting errors before the claim is processed.
The tenth use case is identifying root causes. Rather than simply addressing the symptoms of denials, such as correcting coding errors, AI can delve deeper into the underlying causes of denials. This may include issues such as outdated policies or procedures, lack of training or education, or system-wide inefficiencies. By addressing these root causes, providers can significantly reduce the likelihood of future denials and improve overall claims management efficiency.
Denial Management Software Solutions
There are several software solutions available for denial management. These solutions are designed to help healthcare providers automate and streamline the denial management process. They can help automate the process of analyzing, correcting, and resubmitting denied claims. They can also help track the status of pending claims and help ensure they are resolved quickly.
One example of a software solution for denial management is Change Healthcare's denial analytics. This solution uses AI and machine learning algorithms to analyze large volumes of claims data to identify patterns and trends that can help to predict and prevent future denials.
When a claim is denied, the software automatically retrieves and analyzes the claim data, identifies the cause of the denial, and generates an appeal letter to the payer. The appeal letter includes relevant information needed to support the claim and increase the chances of a successful appeal.
The software also provides real-time tracking and reporting on the status of denied claims and appeals, allowing healthcare providers to quickly identify and resolve issues. Additionally, the solution offers a customizable workflow that allows users to create and manage their own rules for claim routing and escalation, further streamlining the denial management process.
Denial management solutions can also be used to automate the process of following up with payers on any outstanding claims. These solutions can provide several benefits for healthcare providers, including improved accuracy, reduced risk of errors, and improved cash flow.
The ability to fight denials requires the right technological resources. For example, claim editor or “claim scrubber” software processes professional and institutional claims from the payer’s perspective. This includes the medical necessity database to identify the complete set of codes and capture important complications that are frequently missed in a large, complex record. These solutions perform various types of edits, including diagnosis code, medical necessity, procedure code, claim-level technical, outpatient prospective payment system (OPPS), and file format edits.
Another key tool is the medical claim scrubber solution, which automates the matching of ICD-10-CM diagnosis codes with the appropriate Current Procedural Terminology (CPT®)/Healthcare Common Procedure Coding System (HCPCS) codes, ensuring that the claim complies with nationally accepted coding guidelines and standards. Code check software and encoders improve the accuracy and efficiency of codes, saving time and money.
When selecting a denial management solution, providers should consider the features offered and the cost of the solution. While some solutions may be more expensive than others, they may offer additional features and benefits that make them worthwhile. Providers should evaluate the features and benefits of each solution to select the one that best fits their need and budget.
In summary, RPA and AI can help healthcare providers and insurers overcome claim denials by streamlining processes, improving accuracy, and identifying potential issues before they occur. By leveraging these technologies, organizations can reduce administrative burdens, improve revenue, and provide better patient care.
Srivalli Harihara, CCS, CPC, PGP-AIML, is the senior manager of operations excellence at Coronis Health, a global healthcare revenue cycle management company. (Editor’s note: The author has no affiliation nor receives remuneration from Change Healthcare.)