In healthcare claims processing, every delay, manual touchpoint, or error has a cascading effect, including:
At the heart of many of these delays and errors are the unspoken or underappreciated perils of low auto adjudication rates.
Enter generative AI, which is revolutionizing our ability to go-to-market with quality claims applications that do more with less.
In this article, we discuss how to boost auto adjudication rates in healthcare claims applications with generative AI and why this matters more than ever before.
What is Auto Adjudication, and Why Does it Matter?
Auto adjudication in healthcare claims applications is the ability to automatically assess the validity of the contents and either approve or deny a claim without human intervention. The ideal system, at least in theory, would enable a “black box” solution that analyzes every claim and applies payer policies and contracts to reach a decision.
But, in practice, auto adjudication is far from perfect, and in many scenarios, operations teams still need to manually review, validate, and approve or deny claims.
This leads to multiple failures, including:
Manual processing is generally not a long-term option, especially when claims are increasing at such a blistering pace. (More on that below.)
Enter AI, which is becoming a game-changing technology in the modern healthcare claims application lifecycle. Generative AI, which is a type of AI trained to create and modify information in real-time across modalities like text and data, can be a major component in the process to remediate low auto adjudication rates.
The Problem: What Happens When Manual Review Occurs?
The reasons a claims system might fail to auto-adjudicate a claim include:
Manual review is the fallback when auto adjudication fails to evaluate a claim — or is only designed to perform partial processing. For example, in some scenarios, auto adjudication might only determine copayments, while the rest of the claims data is still subject to human eyes.
Manual processing results in:
Higher workload: Claims need to be interpreted by humans, which is time-consuming.
Additional operating costs: Manual processing implies additional operating costs per claim.
Manual errors: Humans are not immune to making mistakes.
Costly reprocessing: Claims may have to be reprocessed if adjudicated inaccurately.
Interest penalties: In some states, healthcare payers have an obligation to pay claims within a certain time frame. Interest payments or penalties may need to be paid out if manual delays or errors cause claims to take longer than expected.
Clearly, healthcare payers cannot rely on manual processes forever.
How Generative AI Can Boost Auto Adjudication Rates in Healthcare
Generative AI — and the combination of key breakthroughs like Large Language Models (LLMs), OCR, NLU, and others — has created new opportunities for better performance across the healthcare claims process. In particular, generative AI can:
AI can process and understand information captured in many different modalities or formats, including scanned PDFs, faxes, and electronic forms. Generative AI can help healthcare payers with:
Capturing the Edge: Generative AI + Auto Adjudication = Competitive Advantage
Generative AI can help power more intelligent automation, giving payers access to:
Increased auto adjudication rates: By putting more claims through automatic processing, generative AI can drive higher auto adjudication rates, resulting in faster and cheaper claims processing.
Reduced reliance on manual review: Automating more of the adjudication process will help operations teams avoid significant volumes of manual review, freeing up precious resources.
Lower operating costs: Automating more of the adjudication process will help operations teamsavoid significant volumes of manual review, freeing up precious resources.
Reduced errors and reprocessing: Intelligent automation means cleaner claims data and far fewer errors requiring reprocessing, which reduces operational costs and increases payer credibility with providers and members.
Fewer interest penalties: Faster claims processing will help payers avoid interest penalties, resulting in significant savings.
AI-driven insights: By more intelligently processing and adjudicating claims, generative AI can also enable new forms of self-service insight and analysis.
Contextual understanding: The ability to understand and process claims data across modalities or use cases is another major opportunity with generative AI in healthcare claims applications.
Competitive differentiation: Intelligent automation is one key way that the use of generative AI in healthcare claims will lead to meaningful competitive differentiation.
Final Thoughts
Auto adjudication has always been a goal for healthcare payers, but the older generation of technology solutions simply couldn’t keep pace with the complexity of the real world.
Generative AI is starting to change that — and the combination of automation and intelligence in healthcare claims applications will be transformative.
