Behind every real healthcare AI success story are six essential elements—skip one, and you’re not building a solution, you’re staging a performance.
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.
Machinify was able to to reveal maximum cost savings while reducing payment timelines with the fastest speed to pay on prepayment reviews.
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.
In this podcast, Tim explores how deeply embedded incentives, inefficiencies, and subjectivity in the current claims adjudication process create opportunities for manipulation and inefficiency.
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.
Strategies for Successful Adoption of Pre-Payment Solutions
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.