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What happens when a sudden moment rearranges your priorities without permission? Radiologist Dr. Nelly Tan shares her personal journey through her mother's cancer diagnosis, confronting a sobering question: why give your best hours to work while leaving so little time for those who matter most?

We explore saṃvega—a spiritual jolt combining shock at complacency with urgent resolve to realign our lives. Dr. Tan bridges ancient wisdom with modern research on mortality, burnout, and time perception, offering a roadmap to convert urgency into steady action (pasāda) before tragedy strikes.

Key Takeaways

• Saṃvega ( sahm-VAY-gah ): The sudden realization of life's fragility, combining urgency and effort.
• Pasāda: Calm confidence that prevents urgency from turning into dread, converting shock into steady action.
• The Four Goads ( Patoda Sutta ): The Buddha's parable on how close suffering must get—shadow, coat, hide, or bone—before we change.
• Specific Mortality Reflection: Personal reflections on mortality cause us to choose generously and align with core values.
• Socioemotional Selectivity: When time feels limited, we naturally prioritize meaningful relationships over wealth.
• The Cost of Overworking: Strong social ties increase survival likelihood by 50%, and buying time boosts life satisfaction.
• Intention-Behavior Gap: Enduring long-term regrets stem from what we never did for our ideal selves.

6 Steps When the Goad Touches You

1. Name it: Identify saṃvega to turn vague anxiety into action.
2. Stay with discomfort: Resist the urge to rationalize the jolt away.
3. Make it specific: Map where your hours go versus who matters most.
4. Act while it stings: Take one concrete step this week while clarity is fresh.
5. Find your pasāda: Seek calm clarity that turns dread into resolve.
6. Revisit on purpose: Reflect on life's brevity—don't wait for the bone.

📖 Read the Original Blog: nellymd.com 🎙️ Production Credit: Created by Gemini Notebook, based on Dr. Nelly Tan's research and writing.

Connect with Dr. Nelly Tan:

• 🔗 LinkedIn: Nelly Tan on LinkedIn
• 🦋 Bluesky: @nellytan.bsky.social
• 🌐 Blog: www.nellymd.com

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Palliative care is not hospice In this deep dive, we explore how specialized palliative care works in parallel with active, curative treatment to relieve the physical and emotional stress of serious illness from day one[1].

💡 Key Takeaways

• Parallel Care Model: Palliative care is given alongside disease-directed treatment, rather than replacing it when treatment stops[1][2].
• The Awareness Gap: An estimated 71% of U.S. adults have never heard the term "palliative care," and globally only about 14% of people who need it receive it[3].
• Beyond Cancer: Cancer accounts for only 34% of people who need palliative care; the majority have cardiovascular disease (38.5%), chronic respiratory disease (10.3%), or other chronic conditions[3][4].
• Clinical Evidence: A landmark 2010 study published in the New England Journal of Medicine showed that early palliative care significantly improved patient quality of life and reduced depressive symptoms[1][5].
• Telehealth Accessibility: Recent research confirms that video visits can deliver equivalent quality-of-life benefits as in-person appointments, making care far more accessible[6][7].

📋 Questions to Ask Your Care Team

If you or a loved one are facing a chronic or serious diagnosis, bring these practical questions to your next appointment:

1. "Can we request a palliative care consult alongside our ongoing treatment plan?"[7]
2. "Can we focus on targeting specific symptoms like pain, cough, nausea, and sleep disruption?"[8]
3. "Are virtual/telehealth visits available for routine symptom check-ins?"

🔗 Links & Resources

• Read the Full Article: Explore the original story and deeper analysis on the Blog Post
• Find a Local Provider: Search for palliative care specialists by ZIP code at GetPalliativeCare.org
• Connect with the Author:
  • LinkedIn Profile: https://www.linkedin.com/in/nelly-tan-092b5bb/
  • Bluesky Profile: https://bsky.app/profile/nellytan.bsky.social

🎙️ Audio Disclosure: This podcast deep dive was generated by Gemini Notebook based on the source analysis and medical literature review[10]*.*

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🤖 AI is not replacing my clinical judgment. It is clearing the ground so I can focus on the work that actually needs me.

In this episode, I share what a year of building AI workflows in radiology really looks like:

🩻 Faster reporting when the tool fits the task

🔍 A systematic second review of prior findings

📚 Less friction in teaching and research

📅 Automated scheduling and administrative work

❤️ More time and attention for patients, and for life outside medicine

⚖️ Why AI still requires measurement, oversight, and human judgment

The biggest lesson? 🎯 Workflow fit matters more than hype. AI worked well for standardized CT reporting, but the benefit was unclear for complex MRI. The goal is not automation for its own sake. It is giving clinicians more capacity for the work only they can do.

📝 Read the original blog post: https://www.nellymd.com/2026/08/what-year-of-building-ai-workflows.html

📚 References

1️⃣ Tan N. Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens. Abdominal Radiology. 2026. 🔗 https://doi.org/10.1007/s00261-026-05524-y

2️⃣ Mayes CJ, Reyes C, Truman ME, et al. Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports. Abdominal Radiology. 2025;51(1):513-520. 🔗 https://doi.org/10.1007/s00261-025-05079-4

3️⃣ Twilt JJ, Saha A, Bosma JS, et al. Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study. Radiology: Imaging Cancer. 2026;8(3):e250461. 🔗 https://doi.org/10.1148/rycan.250461

💬 Opinions are my own. This episode is for educational purposes and does not constitute medical advice.

🎙️ AI disclosure: This podcast episode was generated using Google's NotebookLM by Gemini, based on my original blog post and the referenced research. I reviewed the episode before publication.

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Can AI make sure great care gets noticed?

Patient compliments often remain buried in thousands of survey responses. This episode explores how natural language processing can identify positive feedback and quickly deliver it to the healthcare professionals who earned it.

The results: praise could be shared in a median of 0.7 minutes per case, while 94% of surveyed staff felt appreciated and 90% felt valued.

📚 Featured research

🤖 Automating patient praise. Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff. A hybrid NLP workflow made sharing patient compliments faster and supported staff recognition and morale. 🔗 DOI: 10.1016/j.jacr.2023.12.024

🧲 What shapes the MRI experience? Effects of Patient Demographics and Examination Factors on Patient Experience in Outpatient MRI Appointments. Longer waits and examinations were associated with worse ratings, while service, communication, and comfort mattered. 🔗 DOI: 10.1016/j.jacr.2023.02.032

💡 The takeaway: AI does not replace gratitude. It helps make sure gratitude reaches the right people.

🤖 AI disclosure: I selected the source papers and shaped the framing for this episode. Google NotebookLM generated the podcast audio. I reviewed and approved the audio and this episode listing before publication.

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My mother needed hospital care, and she got it. But the hospital also took her sleep, her movement, and every last bit of control over her day. Then she qualified for a hospital-at-home program, which brought hospital-level care into her home.

In this episode, I describe what changed when the medicine stayed the same but the environment changed: scheduled visits, more movement, uninterrupted sleep, and the ability for our family to care for her without living in a hospital room. I also examine the evidence on hospital-at-home programs, including their safety, patient experience, costs, caregiver burden, and equity challenges.

Hospital at home is not for every patient or every family. But for carefully selected patients, it offers a compelling alternative: keep the medicine and give people back more control over their lives.

Read the full blog post: https://www.nellymd.com/2026/09/when-hospital-came-home.html

Sources mentioned:

Maniaci MJ, et al. J Hosp Med. 2025. https://doi.org/10.1002/jhm.70076

Levine DM, et al. Ann Intern Med. 2020. https://doi.org/10.7326/M19-0600

Edgar K, et al. Cochrane Database Syst Rev. 2024. https://doi.org/10.1002/14651858.CD007491.pub3

Krumholz HM. N Engl J Med. 2013. https://doi.org/10.1056/NEJMp1212324

Duhamel S, et al. J Am Geriatr Soc. 2026. https://doi.org/10.1111/jgs.70573

AI disclosure: I wrote the blog post from my own experience and point of view and co-drafted it with Claude (Anthropic), which helped research, organize, and edit the piece. Google NotebookLM generated the podcast audio from the blog post. ChatGPT (OpenAI) created the episode-cover illustration and prepared and scheduled the episode on RSS.com. I reviewed and approved the final blog post, audio, artwork, and episode listing before publication.

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How do you improve healthcare without blaming the people working inside it? In this episode, I explore how a large academic health system 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 quality improvement 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 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.