AI in Healthcare

AI in Healthcare

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When the Title Outruns the Study: General-Purpose vs. Healthcare-Specific AI
A Brief Communication in Nature Medicine made the rounds last month under a headline that is hard to misread: “General-purpose large language models…
 • David Talby
The case for bringing HCC coding in-house: what generative AI changes about the outsourcing math
Originally published in Health IT Answers and MedCity News, November 2025.
 • David Talby
Three Healthcare AI Frameworks, one governance backbone: RUAIH, URAC, and CHAI
U.S. healthcare now has two AI certifications and a set of governance playbooks, but they all rest on one backbone you must sustain.
 • David Talby
The real AI governance gap isn’t missing regulation. It’s missing literacy.
Originally published in CIO, October 2025.
 • David Talby

June 2026

A cost model for patient-level healthcare AI: $1M for locally deployed Medical LLM vs. $13M to $30M via frontier APIs
Healthcare AI budgets break on one assumption: that per-token API pricing works at patient-population scale.
 • David Talby
Why cancer registries stay years out of date - and what regulatory-grade oncology AI changes
Originally published in Forbes, July 2025.
 • David Talby
Why the 2026 Medicare Advantage rate decision raises the bar on HCC coding accuracy
Originally published in MedCity News, Rama on Healthcare, and Gene Online — June 2025.
 • David Talby
When smaller wins: the size calculus for generative AI in regulated work
Originally published October 2024 in CIO.
 • David Talby
The agreeable AI problem: why LLMs echo wrong answers back to you, and what it costs in healthcare
Originally published August 2024 in CIO.
 • David Talby
Where AI is actually changing pharma: four workflows that are already producing results
Originally published July 2024 in PharmaPhorum and Pharma Compliance Monitor.
 • David Talby

May 2026

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