『Nelly MD』のカバーアート

Nelly MD

Nelly MD

著者： Nelly Tan
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What happens when a radiologist gets curious about… pretty much everything?

Welcome to Nelly MD — my podcast about the ideas, papers, technologies, and projects that make me stop and think.

I’m a radiologist, but this show goes well beyond radiology. Expect a hodgepodge of peer-reviewed research, my own publications, artificial intelligence, quality improvement, healthcare innovation, interesting blog posts, lessons from projects, and whatever else catches my attention.

Some episodes will unpack a research paper: Why did we do the study? What did we actually find? What surprised us? And does any of it matter in the real world? Others may explore AI, process improvement, new technology, patient care, or an idea I simply want to understand better.

A quick note about AI: this podcast is intentionally co-created with AI. I choose the topics, papers, source material, questions, and overall direction. I bring the medical context, experience, interpretation, and judgment—and I decide what ultimately gets published. AI tools including ChatGPT, Claude, and Gemini help me explore ideas, summarize and synthesize information, organize material, and draft or refine content. I review, edit, and take responsibility for the final product.

In other words: human curiosity and judgment, amplified by AI.

You can find more of my writing, projects, experiments, and assorted interests at nellymd.com.

Connect with me on LinkedIn: https://www.linkedin.com/in/nelly-tan-092b5bb/ Follow me on Bluesky: @nellytan.bsky.social

Nelly MD is curious by design. Papers, radiology, AI, quality improvement, research, ideas—and probably a few things that don’t fit neatly into any category.

This podcast is for educational and informational purposes only. It is not medical advice and does not represent the official position of any institution.

Nelly Tan 衛生・健康的な生活 身体的病い・疾患
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  • How A Health System Improve Care

    How A Health System Improve Care
    2026/08/30

    How do you improve healthcare without blaming the people working inside it? In this episode, I explore how Mayo Clinic turns systems problems into measurable change—using DMAIC, root cause analysis (RCA), simulation, frontline observation, and an institutional culture of quality improvement.

    Drawing on four recent Mayo Clinic projects, we look at:

    • how structured education, participation tracking, and simulated RCA increased radiology residents’ participation in safety-event investigations by 33%;

    • how a one-hour simulated RCA improved residents’ comfort with RCA participation, understanding of what to expect, and ability to identify system issues;

    • how low-cost, workflow-embedded changes to privacy, wait-time communication, and physical comfort raised top-box nuclear medicine waiting-area comfort scores from 76% to 85% while the check-in wait-time measure remained stable; and

    • what 1,106 Mayo Clinic Quality Academy projects involving 10,063 team members reveal about teamwork, efficiency, multidisciplinary collaboration, and sustainable improvement.

    References and access:

    1. Reyes C, Ponce LM, Hannafin CL, et al. Improving patient safety education for radiology residents: Using a quality improvement approach. Current Problems in Diagnostic Radiology. 2025;54(5):568-573. https://pubmed.ncbi.nlm.nih.gov/40517116/

    2. Fishleder MH, Hannafin CL, Ponce LM, et al. Exploring the feasibility and effectiveness of simulated root cause analysis for radiology training. Current Problems in Diagnostic Radiology. 2026;55(2):181-184. https://pubmed.ncbi.nlm.nih.gov/41177709/

    3. Tan N, Hannafin CL, Ponce LM, et al. Improving comfort in the nuclear medicine waiting area: A quality improvement initiative. Current Problems in Diagnostic Radiology. 2026;55(4):501-504. https://pubmed.ncbi.nlm.nih.gov/41912369/

    4. Tan N, Rohila V, Reyes C, et al. Cultivating a Quality Improvement Culture With Mayo Clinic Quality Academy. American Journal of Medical Quality. 2026;41(3):133-138. https://pubmed.ncbi.nlm.nih.gov/41961077/

    AI disclosure: This episode was co-created with AI. I supplied a brain dump and the source papers; ChatGPT, Claude, and Gemini helped create and shape the final content.
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