🔥 "The clinicians that have adopted Microsoft Dragon Copilot have increased the number of patients that they're able to see every day, and their overall cognitive burden really seems to be less." — Dr. Scott Eshowsky, Chief Medical Information Officer, Beacon Health System Beacon Health didn't just cut back on scribes. They eliminated them entirely from the Emergency Room — and their providers are seeing more patients with less burnout. ✅ 100% elimination of scribes in Beacon Health's Emergency Room ✅ 5–7 additional patients per provider per week ✅ 50% reduction in scribe reliance across multiple clinics At $159/user/month, Microsoft Dragon Copilot delivers what a full-time scribe can't — ambient AI documentation, 99%+ accuracy speech recognition, and clinical intelligence, all in one platform that goes wherever the clinician goes. 👉 Schedule a consultation: https://lnkd.in/ea-tv26w

Microsoft Dragon Copilot delivers what a full-time scribe can't — ambient AI documentation, 99%+ accuracy speech recognition, and clinical intelligence, all in one platform that goes wherever the clinician goes.

Microsoft Dragon Copilot delivers what a full-time scribe can't — ambient AI documentation, 99%+ accuracy speech recognition, and clinical intelligence, all in one platform that goes wherever the clinician goes.

112% ROI. Northwestern Medicine more than doubled their return with Microsoft Dragon Copilot. This ambient-AI medical assistant eliminates the dictation step, provides a full transcript for verification, and creates comprehensive encounter notes for you. Transform your workflow 👉 https://lnkd.in/ea-tv26w

A model that predicts hospital length of stay isn't worth much if a case manager can't ask "why is this patient flagged?" and get a real answer. That constraint shaped this whole project. Instead of optimising for accuracy, I built a depth-4 decision tree with 16 readable leaves, mapped each leaf to a recommended operational action (and a governance note), and audited the result by gender, facility, readmission count, and month before the deployment conversation. The honest finding: even with class weighting, the constrained tree misses ~52% of true long-stay patients. The notebooks make that trade-off visible — side-by-side with a random forest (which catches more long stays but explains nothing) and a McNemar's test confirming the gap is real, not noise. This is what I think a healthcare ML project should look like: decision first, model second, error analysis before the deployment conversation. 7 notebooks, executed end-to-end. Reproducible with make data && make pipeline. 🔗 https://lnkd.in/dSe9q4iH (Built on Microsoft's public hospital LOS dataset — methodology generalises, the synthetic numbers don't.) #DataScience #HealthcareAnalytics #Interpretability #MachineLearning

As someone building local AI infrastructure for healthcare, I keep coming back to a simple question: Why should a patient wait weeks for access to their own complete medical record in 2026 — especially inside a major hospital system using Epic/MyChart? With properly structured local infrastructure, indexed records, vectorized search layers, and modern retrieval workflows, much of this information should be retrievable in minutes rather than weeks. To patients, these delays can begin to feel less like technical limitations and more like institutional drag-their-feet processes that create unnecessary friction between people and their own health information. If this sounds like an experience you, a family member, friend, colleague, or loved one has gone through, I would genuinely like to hear your story and perspective.

Dictation at cursor, in any EHR or application, with built-in macros and AI-assisted editing. Donald Lazure, PA demonstrates how Commure Dictation gives clinicians a locally installed speech-to-text tool that fits directly into the workflows they're already using. Learn more about Commure Dictation: https://lnkd.in/gxKpqghH

#ClevelandClinic is the pioneer of expanding the use of Artificial Intelligence in Healthcare. Great professionals insights about the bias and challenges related to this field.

As healthcare continues to digitize, software competency is no longer optional—it’s essential. Both clinical and non-clinical professionals must be equipped to navigate health systems, AI tools, and data-driven workflows. In this month's #blog post, we discuss how investing in software training isn’t just about efficiency; it’s critical to delivering safe, high-quality patient care in today’s evolving landscape: https://lnkd.in/gUTftbkY

Operating room utilization at many health systems still hovers around 65–75%, leaving revenue and patient access on the table. The issue isn’t data; it’s the Operational Intelligence Gap: insights arrive after schedules are locked, when it’s too late to release unused block time or backfill cases. With Databricks Genie, leaders can query real-time scheduling and outcomes data in natural language to uncover specific opportunities by surgeon, block, or facility — turning hidden capacity into millions in value each year. If you’re responsible for surgical operations, your next efficiency gains are already hiding in your scheduling data.