About
I’m Aaron 乐 — friends just call me Aaron. I work on the front lines of healthcare, leading technology innovation and AI transformation across strategy, products, and organizational change.
What I do
I’m not interested in adding a thin AI layer to legacy systems. The work is to move healthcare from “healthcare IT” toward “AI-driven.” I keep turning AI First from strategy into practice — anything AI can do, we rethink and design as an AI agent first.
What I work on
It isn’t only AI and data. The questions I care about keep getting broader:
- Data · AI — Embodied AI, Agentic AI, Harness Engineering, AI-driven healthcare IT architecture
- Business redesign — the business model and organizational shape of a healthcare group in the age of AI
- Lean management — operating efficiency, closed-loop processes, and performance systems for a large group
- Business models — synergy and value creation across healthcare, insurance, and health management
They look scattered, but they’re really one thing:
when AI truly enters an industry, organizations, processes, and business models all have to be redesigned.
Where I come from
I came up through engineering — from the front line of build-out to group-level technology management. I’ve shipped, led teams, carried P0 incidents, and recalibrated my judgment over and over inside complex businesses.
After two decades and several waves of healthcare IT, I now spend more time on one question: what should a healthcare group look like in the age of AI?
Why I write this site
I believe in “thinking in public.” Write a judgment down, and three months later you’ll know if it was right. A year later you’ll know how deep it was.
This site isn’t a product page, a résumé, or an influencer channel. It’s a public archive I keep for myself — using constant reflection to iterate on myself, and to learn from AI.
AI is a mirror: it forces you to write your thinking clearly, to state your judgments plainly, and to keep up with its pace of iteration. I publish that process — to light a lamp for myself, and maybe for a few peers along the way.
Cases
What actually got built at work, I try to write up and publish — what was done, how it landed, what results it produced, and where we took the wrong turn.
Each case aims to show checkable evidence: real product interfaces, stage metrics, and the mistakes made along the way. Everything is redacted — the focus is method, judgment and boundaries, not who did it or what it cost. They live in Cases.
Collaboration / contact
If you’re a fellow CTO/CIO, an AI/healthcare researcher, or someone who does serious work, I’d be glad to hear from you: szleying@qq.com.
On business proposals: as a rule I pass, unless it’s a direction I’m genuinely interested in.