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When it comes to DRG payment integrity, there’s a massive shift underway—towards smarter, faster, more cost-effective pre-pay solutions.
Because artificial superintelligence is still the stuff of science fiction, LLMs need proper guidance in order to be useful to medical professionals, researchers and others in the healthcare industry.
Inefficient claims review processes, audits that frustrate providers, and billions of dollars wasted on inaccurate payments have plagued the healthcare industry for years. Now, with AI, there's a smarter way forward.
Traditionally, payers have cast wide nets and asked providers to navigate hurdles only to claw back money after a payment has been made.
Most payers struggle to incorporate legislative, coding, and clinical changes into policy as well as to validate that policies are consistently applied to make timely, accurate payments of claims.
The last decade has seen AI’s escalating impact on a variety of industries take shape, and perhaps no vertical has been impacted as much as healthcare.
Translating clinical guidelines into AI-compatible formats requires understanding the guideline's intent and how AI applies rules at scale.
Artificial Intelligence (AI) is often hailed as the savior of healthcare, but can it really handle modern medicine? Here are three big reasons AI might stumble before it soars.
We're building a healthcare intelligence company that delivers unmatched value, transparency, and efficiency to payers across the country.
New executive orders prioritize rapid artificial intelligence (AI) innovation and reduced regulatory insight. How do these changes impact payers?
We need an operating system for AI: a framework that ensures transparency, control, and real impact. What would that look like? Let’s break it down, layer by layer.