Doctronic

AI can now renew your prescriptions in Utah, and we're leading the way. Our system verifies identity and prescription history, then processes refill requests for 190 commonly prescribed medications. Approved renewals go directly to Utah pharmacies. Cases requiring clinical judgment escalate to a licensed physician. It's early, narrow in scope, and built with safety guardrails. Every major advance in healthcare starts with a bold first test; Doctronic is proud to be at the forefront. Read more: tinyurl.com/doctronic-pres… #Doctronic #AIDoctor #AIHealthcare #DigitalHealth #Utah

This is why timing matters. Antihistamines work best when taken before exposure, before histamine has a chance to attach. Once symptoms start, you're playing catch-up. 𝐅𝐢𝐫𝐬𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐥𝐢𝐤𝐞 𝐁𝐞𝐧𝐚𝐝𝐫𝐲𝐥): → Fast-acting → Crosses the blood-brain barrier → Causes drowsiness → Lasts 4–6 hours 𝐒𝐞𝐜𝐨𝐧𝐝-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐥𝐢𝐤𝐞 𝐂𝐥𝐚𝐫𝐢𝐭𝐢𝐧, 𝐙𝐲𝐫𝐭𝐞𝐜, 𝐀𝐥𝐥𝐞𝐠𝐫𝐚): → Doesn't cross the blood-brain barrier as easily → Significantly less drowsiness → Lasts 12–24 hours Both block the same histamine receptors (H1). The difference is how they interact with your brain. Not sure which is right for you? Ask our AI doctor: doctronic.ai/allergies #SpringAllergies #AllergyTips #Antihistamines #AIDoctor

Spring is here, and so is your immune system's overreaction. 🤧 Your allergy pill doesn't stop allergies. It mutes them. Here's how: When your immune system detects an allergen (pollen, dust, pet dander), it releases histamine. Histamine binds to receptors on your cells, triggering sneezing, swelling, itching, and congestion. Antihistamines don't stop histamine from being produced. They block it from binding to those receptors. Think of it like a key that fits the lock but doesn't turn it.

A new peer-reviewed study of 155,217 patients found that AI tripled HIV prevention medication uptake and nearly doubled follow-up rates. No diagnosis, no prescriptions. Just showing up, answering questions, and removing friction. That's the kind of healthcare AI we're building at Doctronic. Study: nature.com/articles/s4174… #NNHAAD #HIVPrevention #AIinHealthcare

Today is National Native HIV/AIDS Awareness Day. Between 2018 and 2022, HIV diagnoses in American Indian/Alaska Native communities rose 30% while national rates held steady. Access barriers like rural geography, underfunded clinics, and stigma are a huge part of why.

In January, a Stanford-led team published a study in Nature Medicine showing that an AI foundation model trained on more than 585,000 hours of sleep recordings from roughly 65,000 people could predict 130 conditions from a single night of sleep data. Those signals included risk patterns related to all-cause mortality, dementia, heart attack, heart failure, chronic kidney disease, stroke, and atrial fibrillation. Sleep has always reflected overall health. But the amount of physiological information captured during sleep is too complex and too high-volume for any human to fully interpret on their own. AI changes that. This is a glimpse of where healthcare is heading: systems that can help turn raw physiological data into clinically meaningful insight. The most important part of this research isn't just any one prediction. It's the proof that AI can extract real signal from data streams that are too dense and too layered for traditional interpretation alone. The data already exists. The models are getting better. The future of preventive medicine may be less about ordering more tests and more about getting more meaning from the data we already have. Learn more here: nature.com/articles/s4159…

Since it's still Sleep Awareness Month, did you know that researchers used AI and a single night of sleep data to predict 130 health conditions?

Medication renewal in Utah, broken down step by step. No confusion, no waiting rooms, just a simpler way to get your prescription renewed. 🙌🏼 #AIDoctor

Here's the link to the full podcast episode: youtube.com/watch?v=yU8RV5… #AIDoctor #AIHealthcare

No one confuses a chess engine for Magnus Carlsen. That's exactly how our Chief Medical Officer sees AI in medicine - a powerful complement, not a replacement. Dr. Byron Crowe breaks down the real distinction between an "AI doctor" and your human doctor.

Your AI consult captures your full health story and keeps it current. Every doctor you see walks in already knowing your history, your concerns, and what you're working on. Your care should build on itself, not reset every time something changes. → doctronic.ai/checkup #AIHealthcare #DigitalHealth #Doctronic #AIDoctor

You finally found a doctor you liked. Then they left the practice. Or retired. Or your insurance changed, and you had to start over with someone new. Checkup™ by Doctronic gives you consistency that doesn't depend on one person staying put.

You can read some of the frequently asked questions about it here: doctronic.ai/utah-ai-prescr…

Still skeptical about the Utah AI Prescription Renewal Pilot Program? We’ll be sharing a walkthrough soon. Follow so you don't miss it.

We built Doctronic, so no one has to lose sleep waiting for care. Our AI doctor asks the same questions a physician would and provides a real clinical assessment, diagnoses, treatment plans, and a doctor's note in about 20 minutes. Free. Anonymous. Available 24/7. This #WorldSleepDay, know that you don't have to wait for answers.

The average American waits 31 days to see a doctor. That's 31 nights of wondering. 31 nights of Googling. 31 nights of not sleeping well. And that's the average. In some cities, new patients wait over 2 months. In rural areas, it can be even longer. Sleep suffers when health questions go unanswered. Not because the question is always serious, but because not knowing feels serious at 2 am.

A preprint from Johns Hopkins benchmarked 16 LLMs on correcting physician diagnostic errors. This is important work. While an LLM is only one component of a clinical AI system, it is arguably the most visible, and how we test that component shapes how health systems and regulators think about the whole. Arman Ouveysi, our Senior Medical Context Engineer, immediately noted something: the goal here is to evaluate whether LLMs can "effectively challenge, rather than merely confirm, an erroneous physician diagnosis." But the primary task prompt was "Do you agree with the physician diagnosis? Yes, or No. If No, suggest the most likely diagnosis." Agreement-framed prompts are well-documented for suppressing disagreement in LLMs (see reference 11 in the preprint itself). A prompt structured around confirming or denying a physician's conclusion is a difficult starting point for testing the capacity to independently challenge it. I am very interested in the disease-specific findings. Syphilis, spinal epidural abscess, and MI were missed consistently across all 16 models and this is absolutely worth exploring further. And the paper's conclusion is correct: clinical AI needs "adversarial, multi-agent workflows designed to prioritize skepticism over baseline agreement." No serious clinical AI deployment is a single-turn "do you agree?" query to a naked foundation model. Single models are never sufficient. Clinical AI needs systems where models challenge each other, where clinical knowledge comes from physician-authored guidelines rather than inferred from web text, and where specialized distilled models handle what generalist models handle poorly. But I love this type of work. Kudos. medrxiv.org/content/10.648…

Some faces behind our tech and our clinic. Meet part of the team building the #1 AI doctor.

Last December, WBUR and NPR reported on the primary care crisis - and on one health system's attempt to respond with AI-supported virtual care. Their reporting cited a striking number: about 17% of adults in America do not have a primary care physician. This is happening in the country that spends more on healthcare than any other. The primary care workforce is under enormous strain. PCPs often earn substantially less than specialists while managing increasingly complex patient panels and growing administrative demands. Meanwhile, the pipeline of new primary care doctors is not keeping pace with retirements and rising demand. Some health systems are turning to AI-augmented virtual care as a stopgap. Patients start with an AI interaction, get triaged, and see a physician by video far sooner than they would through a traditional waitlist. We think this shouldn't be viewed as a temporary patch. It should be part of the future standard of care. AI-augmented primary care is not about replacing the doctor-patient relationship. It's about making that relationship more possible by reducing delays, improving intake, and helping physicians focus on judgment, empathy, and decision-making instead of repetitive administrative work. The shortage is real. The demand is real. The need for a better access model is real, too. Link to full article: wbur.org/news/2025/12/2… #AIDoctor #AIHealthcare #Doctronic #DigitalHealth

When her primary care doctor stopped practicing suddenly, a woman in Massachusetts tried to find another one. She called 10 practices. A few told her they could see her in up to two years.

When you finally get answers instead of more questions. See what others say about us at doctronic.ai/testimonials :)