By Lindy Chappell, Client Success Manager, Aspirion
As a hospital revenue cycle executive, you’re likely grappling with three major challenges: insurance denials, staffing shortages, and razor-thin margins. These issues are pushing many healthcare providers to explore artificial intelligence (AI) and machine learning (ML) solutions. But how can you implement these technologies without compromising the quality of your revenue cycle management (RCM)? Let’s dive in.
The Current RCM Landscape
The healthcare industry is facing unprecedented challenges:
- Insurance Denials: Nearly 15% of claims submitted to private payers are initially denied, including many that were pre-approved through prior authorization.
- Staffing Shortages: The World Economic Forum estimates a global health worker deficit of 10 million by 2030.
- Thin Margins: The Operating Margin Index stood at just 3.8% in May 2024, highlighting the financial pressures on healthcare organizations.
The AI & ML Revolution in Healthcare
Given these challenges, it’s no surprise that AI and ML are gaining traction in the healthcare sector:
- 79% of healthcare organizations are currently utilizing AI technology
- The worldwide AI in the healthcare industry is expected to grow at a compound annual growth rate of 37.5% from 2024 to 2030
AI and ML have the potential to accelerate workflows, increase efficiency, reduce errors, and improve appeal outcomes. But what about the quality of these outcomes?
Addressing the Risk of Decreased Appeal Overturn Rates
A common concern among RCM executives is whether implementing AI and ML could lead to decreased appeal overturn rates. The good news is that while payers continue to find new ways to deny claims, properly implemented AI and ML solutions do not cause any reduction in success rates. In fact, they can enhance the appeal process by identifying stronger supporting evidence and optimizing workflows.
Aspirion’s Approach: Implementing AI & ML Without Sacrificing Quality
At Aspirion, we’ve developed a unique approach to integrating AI and ML solutions while ensuring consistent or improved quality outcomes:
Expertise Acquisition: Our acquisition of Infinia ML in 2023 brought deep data-scientist expertise and a robust document-processing platform to our team
Continuous Improvement: We’re constantly developing and refining our AI and ML tools to stay ahead of evolving payer tactics and industry changes
Iterative Implementation: We use a phased approach to ensure outcomes remain consistent or improve standardized workflow and Human-in-the-Loop (HITL) automation
HITL is the collaboration between humans and artificial intelligence and yields significant benefits. Humans enhance AI accuracy by fine-tuning responses, especially in complex areas like medical records and contracts where context is crucial. They also improve data collection and validation. Human oversight helps detect and correct biases in AI systems, preventing the perpetuation of inequalities. Additionally, this partnership boosts efficiency by combining AI’s rapid data processing with human expertise for final analysis, creating a powerful, time-saving approach that leverages the strengths of both.
Human-on-the-Loop (HOTL) is an extension of HITL, and involves humans providing feedback to the AI system to improve its performance over time. HOTL is typically used when the AI system has reached a certain level of performance but still requires human feedback and intervention to continue improving. In HOTL, humans act as trainers or teachers for the AI, providing labeled data, correcting mistakes, and guiding the AI toward better outcomes.
Focus on Improved Outcomes: Our AI and ML solutions are designed to:
- Extract critical information from documents and identify optimal supporting evidence
- Auto-route workflow to appropriately skilled resources
- Predict the likelihood of payment/success based on past trends
- Identify underpayments more accurately
The Results: Enhanced RCM Quality
By implementing AI and ML solutions with this careful, quality-focused approach, we’ve seen significant improvements in RCM outcomes:
- More accurate and efficient document processing
- Optimized workflow routing
- Improved prediction of payment likelihood
- More precise underpayment identification
For RCM executives, the message is clear: AI and ML, when implemented thoughtfully, can significantly enhance your RCM processes without sacrificing quality. As you navigate the challenges of denials, staffing, and thin margins, consider how these technologies could transform your operations and improve your bottom line.
Are you ready to explore how our AI and ML RCM support can revolutionize your RCM processes while maintaining or improving quality? Let’s start a conversation about your specific RCM needs and challenges today.
