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AI That Pays: Lessons from Revenue Cycle — Nathan Wan, Ensemble Health

Jul 24, 2025 · 18:19

Nathan Wan, head of AI at Ensemble Health Partners, argues that Revenue Cycle Management (RCM) is a critical yet overlooked domain for AI disruption in healthcare, where 40% of hospitals operate at negative margins due to broken manual financial processes. He explains that RCM's vast structured and unstructured data, rule-based workflows, and direct financial impact make it ripe for AI, contrasting it with more publicized clinical AI applications. Wan details how Ensemble uses AI to predict and prevent claim denials—often caused by technical errors like missing data—and to accelerate clinical denial appeals via GenAI, achieving a 40% reduction in processing time and improved overturn rates. He emphasizes that Ensemble's end-to-end view of the revenue cycle, supported by their EIQ data platform, enables longitudinal error correction and agent-driven automation, turning upstream fixes into measurable ROI for providers facing increasing payer denial rates.

Harnessing the Power of LLMs Locally: Mithun Hunsur

Nov 22, 2023 · 17:09

Mithun Hunsur presents llm.rs, a Rust library for running large language models locally, arguing it gives developers ownership, lower latency, and privacy compared to cloud APIs. He explains how quantization makes inference viable on consumer hardware, and shows that llm.rs supports architectures like LLaMA and Falcon through a unified interface. Practical code examples demonstrate customization, and community projects like LocalAI and LLMchain illustrate real-world use. Hunsur shares his own date-extraction pipeline, fine-tuning a small model with GPT-3 data to replace expensive cloud calls. He also cautions about hardware requirements, trade-offs between speed and quality, and ecosystem churn from rapid innovation.