RN PocketPal is live on Google Play
Every post on this blog is drafted from clinical practice and reviewed by working bedside RNs before publishing. No AI-spun listicles. No SEO-optimized fluff. The opening editorial calendar is below — these are the posts dropping in the first eight weeks after launch.
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RN PocketPal is live now on the App Store and Google Play.
Drafted by
Working bedside RNs
No content farms. No outsourced ghostwriters. Authors named on every post.
Reviewed by
Clinical advisory team
Every clinical claim is source-linked. Last-reviewed dates on every page.
Edited for
Real shifts
If a post can’t survive a 12-hour shift on a phone, we don’t publish it.
Editorial calendar
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Why the brain sheet you started with in nursing school stops working in your second year — and what to put on the next one. Field-tested layouts for tele, ICU, ED, and med-surg.
For: New + early-career RNs
They sound interchangeable. They are not. One is a working document; the other is a handoff artifact. Knowing which is which changes how you draft both.
For: All clinical RNs
The five-step method most schools teach, the seven shortcuts experienced nurses actually use, and where a confidence-scored AI reader belongs in that workflow.
For: Tele / step-down / ICU / ED
Apps that "read" rhythms are everywhere now. Calipers are still in the drawer. Both have a place. Here is the honest framework for choosing between them mid-shift.
For: Tele / step-down / ICU / ED
Acuity-aware drag-and-drop is great. The judgment of when to override it is better. Six heuristics from working charge nurses for the moments the math runs out.
For: Charge + house sup
Hourly + per diems + stipends + tax-home math. The contract that looks like $4,500/week and actually pays $2,900 after the fine print. A nurse-built calculator beats your recruiter's spreadsheet.
For: Travel + agency nurses
We built the scaffolding so it cannot write your rationale. Here is what it surfaces, what it deliberately leaves out, and how to use it without crossing the line your school cares about.
For: Nursing students
Core ML on the Neural Engine, why server-side inference is a privacy + latency liability, and what "on-device" actually means once you push through the marketing.
For: Privacy-curious nurses
How we write it
No AI-spun posts.
Drafts are written by working RNs. AI tools edit and fact-check; they don’t generate.
Source-cited clinical claims.
AHA, CDC, FDA, peer-reviewed references. Last-reviewed dates on every page.
No medical advice.
Educational only. We name what we’re doing and why — diagnosis is the clinician’s job.
No patient stories without consent.
Composite cases only, clearly labeled. Real patient details never make it onto this blog.
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