Work
Hands-on enterprise strategy from idea to shipped capability focused on AI that gets adopted, commercialized, and scaled in real clinical environments.
I lead enterprise AI strategy for oncology information systems. The focus is practical: decision support, automation, ambient AI, prediction, and personalization built into real systems with real validation and clear governance.
My perspective comes from multiple angles: I started in clinical practice (dosimetry), spent years educating and supporting care teams, then moved into data/analytics products and global consulting—before stepping into enterprise AI and innovation leadership.
Impact at a glance
Click to expand. One item opens at a time.
Current focus
Six active areas. Click a card for the short “why / what changes / validation” view.
How I work
Pick problems worth shipping: real clinical value, adoption realism, measurable impact, and a credible path to scale.
Start with a strong foundation and phased delivery. Validation isn’t a final step, it’s part of the product capability.
Operationalize globally with monitoring, governance, and configurability—so it works across real variation in sites and geographies.
2005–2014 clinical educator • 2015–2019 global clinical consulting • 2020–2023 enterprise data & analytics leadership • 2023–now clinical AI & innovation leadership
Master’s (Health/Healthcare Administration) • MBA • MIT (Applied AI / AI in Healthcare) • Harvard Business School (Organizational Leadership) • Berkeley (Product Strategy) • Stanford (Medical Informatics)