Business Insight Intelligence for Radius Care with AWS QuickSight

The Situation

Claim payment denials are a significant and chronic issue for healthcare providers, impacting revenue and operational efficiency. The frequency and severity of denied claims vary by practice, specialty, and payer, but they remain a persistent challenge across the healthcare industry.  

Industry estimates suggest that 15% to 20% of healthcare claims are initially denied. However, in some cases, especially for smaller practices or certain specialties, the denial rate can exceed 25%. This variability and complexity can lead to significant revenue loss, as around 30% of all denied claims are never resubmitted, representing a massive loss of income for providers. 

Resubmitting denied claims can cost up to $25 to $118 per claim, depending on the complexity and effort required. This rework, which adds a significant financial and administrative burden on healthcare practices, is often lost or outsourced at reduced profitability. Efficient denial management is crucial to alleviate this burden.  

The Challenge

The administrative cost of managing denials is significant. For healthcare providers, constantly reworking claims can overwhelm billing departments, leading to inefficiencies and higher overhead costs. 

Handling claim denials is time-consuming. Healthcare staff must identify the cause of the denial, gather additional documentation, appeal or correct the claim, and resubmit it. This takes time from other essential functions, such as patient care. 

Consistent revenue loss and payment delays can lead to financial instability for practices with high denial rates, particularly for smaller practices that lack the resources to manage high volumes of denied claims. 

While most initial denials can be corrected, providers often leave denials unresolved due to the complexity and cost of the appeal process. However, successfully appealed claims can significantly improve a provider's revenue recovery rate. 

Radius Care faced significant challenges in managing and analyzing vast amounts of claims data effectively. The agency needed to process large datasets to track and evaluate the performance of claims including turnaround times, denial rates, and the reasons for denials. Their existing data management system was inefficient, lacked scalability, and did not support real-time data analysis, which hindered their ability to make timely decisions and optimize their collection strategies. Moreover, the absence of robust data security measures and the complexity of managing access controls posed additional risks in handling sensitive information.

They required a scalable, secure, and cost-effective solution that could integrate seamlessly with their operations, improve data visibility, enable real-time analytics, and enhance decision-making capabilities. The solution needed to handle large datasets efficiently, provide comprehensive data analysis tools, and ensure strict compliance with industry data security standards.

The Solution

To address these challenges, a custom analytics application was developed using AWS services. This solution leverages the power of cloud computing to enhance data processing and visualization capabilities.

Commissioned for a proof of concept AI solution, Envative quickly applied models to identify the root of the denials and predict when they may occur. Through analyzing historical claim data, we identified patterns and trends that lead to claim denials based on specific criteria, such as coding errors, missing documentation, or payer rules. Manipulating similar models over historical data allowed us to apply a “risk” rating to a claim in preparation for denial and identify possible actions for resolution.  

Further, we analyzed payer return files and identified commonly denied codes based on procedure, payer rules, and claim amount.  

With the newly discovered information, we could automatically scrub and review claims for common errors, such as incorrect codes, missing patient information, or incompatible procedures, before they are submitted to insurers. This would ensure that claims are accurate and complete and reduce the likelihood of denials. 

For example, if a claim were denied due to an incorrect diagnosis code, the software would suggest the appropriate code for resubmission. 

AWS Services:

• Amazon S3: Used as the primary data storage solution, where all claim-related data files are securely stored. S3 provides durable infrastructure to store important data and is designed for high availability and reliability.
• AWS GlueCrawler: Automatically scans data in Amazon S3, identifies the format, and infers schemas. It populates the AWS GlueCatalog with metadata tables, which are used for querying and analyzing data.
• AWS GlueCatalog: Acts as a central metadata repository, which stores information about different data sources accessible within AWS services like Amazon Athena.
• Amazon Athena: Used for querying data stored in Amazon S3 using standard SQL. Athena allows the agency to run ad-hoc queries directly against data in S3 without the need to set up complex ETL jobs.
• AWS QuickSight: Utilized for business intelligence and visualization. It connects to Athena for direct SQL querying capabilities and provides interactive dashboards to visualize patterns and insights related to claims processing, denial rates, and more.
• AWS IAM: Manages access to AWS services and resources securely. Using IAM, the application ensures that only authorized personnel can access sensitive data and perform operations according to their roles within the organization.