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.
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.
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.
We first have to reflect on the year behind us. What happened in healthcare in 2024 and how can we take those learnings into 2025?
AI—as it stands today—excels only at certain tasks, like understanding large swaths of data and making judgements based on well-defined rules.
As we approach the end of 2024, we not only have to reflect on the year behind us, but look forward to the year ahead and what it could hold for the healthcare industry.
At this current peak of the AI hype cycle, it’s difficult to find a product that is not marketed as AI. Let’s get into the levels of AI.
What is AI best suited for? Hearing examples of when to never use AI in healthcare may not be exactly what you were expecting.
To ensure safe, responsible investments in AI, payers need to consider the applications of AI and how to use it safely.