Healthcare has so many problems: declining reimbursements, increasing administrative burden, regulatory burden, skyrocketing costs, workforce shortage, increasing chronic disease burden… the list goes on. Insurance companies and government entities have always thought they know healthcare better than us. They think they can solve these problems. But we know that isn’t true because they created them in the first place.

Thanks to AI, we finally have the tools to take back control. AI can help relieve administrative burden, reduce cost, and give us more time to actually treat our patients. More tools come out every week, we just need to learn how to use them to our advantage.

What’s happening this week:

AI is supposed to change that! But how? Stay tuned to see what I’m (we’re) building

Let’s dive in.

Three large health systems reported measurable financial gains and reduced documentation burden after deploying an ambient clinical documentation platform, per a new KLAS analysis.

The report focuses on real-world ROI, tying documentation reduction to financial outcomes, which is exactly how health systems make decisions when margins are tight.

If you are evaluating scribes, this is a reminder to ask for operational metrics upfront: time saved, downstream coding capture, and what the rollout did to note quality and clinician satisfaction.

Why this Matters: When AI can show hard operational outcomes, it moves from pilot to platform. That is when you see system-level change, not just a few early adopters.

Also,

🚀 23 Real-World AI Use Cases in Healthcare - A roundup of current AI use cases shows automation aiding everything from administrative tasks to clinical decision support.

🔒 Health systems are betting on “secure workspace” GenAI: Becker’s reported health systems piloting or committing to ChatGPT Health, positioning it as a centralized, governed environment for clinicians and operational teams.

A new AI-powered electrophysiology method is making it possible to capture intracellular-level cardiac action potentials without continuously invasive techniques. The researchers introduce an approach they call “inherited noninvasive intracellular recording,” which uses artificial intelligence to reconstruct high-fidelity intracellular signals from longer-term recordings. The big breakthrough is prolonged, stable monitoring of cardiac electrical activity at a level of detail that usually requires fragile, short-lived intracellular methods. This could dramatically expand how we study heart-cell behavior over time, especially in research environments where maintaining intracellular access is notoriously difficult.

Why it matters: Intracellular cardiac signals reveal subtle, clinically meaningful changes in electrophysiology that standard extracellular recordings can miss. If AI can reliably “recover” these signals over longer periods, it could enable more accurate cardiac research, safer drug testing, and better disease modeling at scale.

Key Takeaways:

Also,

🎯 A Commonwealth Fund brief synthesizes evidence on administrative burden’s drivers, like prior authorizations and EHR complexity and potential solutions, including AI, workflow redesign, and policy simplification.
Key Takeaway: Reducing administrative burden requires both technology and systemic reform.

📋 Ambient scribes may change what gets documented and what gets done: A cohort study in JAMA Psychiatry examined primary care annual visits and compared notes produced with an AI ambient scribe versus human or no scribe. Ambient scribe use was associated with modestly greater documentation of neuropsychiatric symptoms, but also a lower likelihood of depression-related intervention or diagnostic coding in the study’s comparisons.
Key Takeaway: Treat ambient documentation as a clinical workflow intervention, not just a typing shortcut. Monitor for unintended changes in assessment and follow-through.

Healthcare executives from more than two dozen leading organizations are calling for a federal artificial intelligence policy framework that would preempt disparate state AI laws and create a consistent national standard for healthcare AI. The coalition argues that the current patchwork of regulations hinders safe, interoperable deployment of AI and increases legal and operational uncertainty.

Why this Matters:
A national framework could reduce compliance burdens, encourage innovation, and enhance patient safety, but it also raises questions about the balance between federal oversight and local safeguards.

Also,

☎️ Updated AI Policy Principles from the American Telemedicine Association: The ATA released refined policy principles for AI deployment in healthcare, emphasizing accountability, performance monitoring, transparency, and bias evaluation. These principles aim to guide providers, developers, and policymakers as AI use expands.
Why this Matters: “Set it and forget it” is not a defensible posture for clinical or operational AI. Monitoring needs an owner and a cadence.

💾 FDA lists a new Clinical Decision Support Software guidance: FDA’s “Recently Issued Guidance Documents” list includes “Clinical Decision Support Software” dated January 2026, and the page content was current as of 01/21/2026.
Why this Matters: Even if your AI is “just workflow,” if it nudges decisions you need to map where you might cross into CDS expectations.

The Problem: Too many administrators making it difficult for us to care for patients.

The Solution: Use AI to systematically reduce administrative burden

My Plan: Each week, I’ll present a new tool to automate a different area of your practice. I’ll show you how to use the tool through tutorials and get you started with a working product.

What Can YOU Do Now? Fill out the survey to let me know what is the most important thing AI can fix in your practice. I will start with the most important and work my way

The best part is, even people (like me) who have no prior coding experience can create amazing AI tools to improve their practice. I, for one, can’t wait to get started!

Healthcare has issues, and healthcare professionals are the only ones who can fix it. Not the government. Not AI companies. Definitely not insurance companies. So many of us have amazing ideas for fixing healthcare, but no idea how to implement it.

Thats why I started this newsletter and why I’m building this community. My goal is to teach healthcare professionals how to use AI in their practice effectively to improve healthcare.

Stay tuned for more information in upcoming newsletters.