PAULA
Healthcare policy intelligence that tells you what to do
What it does
PAULA converts CMS, payer, and regulatory policy changes into role-specific operational decision intelligence for hospitals and health systems. Instead of policy summaries, PAULA delivers financial risk exposure, documentation impact, UM implications, and compliance guidance — tailored for physician advisors, RCM leaders, CFOs, and compliance teams. Every brief is evidence-linked, physician-authored, and vendor-neutral.
Executive intelligence organized by problem: prevent avoidable denials, quantify reimbursement exposure, prepare for CY2027 policy change, and assess AI/UM compliance risk.
Not a content library. Four executive problems, the assets that resolve each, and a route into a quantified review of your own exposure. 46 HCPCS codes carry new status indicator O1 effective January 1, 2027. Ten services shift from CLFS to OPPS liability. If the revenue cycle is not remapped before then, the exposure lands as unreimbursed volume. Physician-authored. Vendor-neutral. Cross-referenced against CMS-1850-P and CY2027 Addendum B. The recurring MA denial mechanics that clear internal audit but never reach the appeal queue — costing 7 figures annually at typical hospital scale. The recurring MA denial mechanics that clear internal audit but never reach the appeal queue. The…from paula.clinefficiency.pro
Does the same job
all alternatives →
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com