Short answer: A skilled nursing mobile app can run AI on-device and remain HIPAA compliant — patient data never leaves the device, so there is no cloud processor to sign a BAA for. Wednesday ships these integrations in 4–6 weeks, $20K–$30K fixed price, money back.
Your CNAs document on shared tablets at the nursing station instead of at the bedside because the AI documentation tool requires WiFi. Your compliance team hasn't approved any cloud AI tool that touches resident records.
Bedside documentation reduces transcription errors, catches missed care items in real time, and gives your charge nurses a cleaner shift handoff. Every workaround that routes CNAs back to the nursing station is a quality and efficiency cost your facility absorbs every shift.
What decisions determine whether this project ships in 6 weeks or 18 months?
Four decisions determine whether this project changes how care is documented or becomes a pilot that never gets past QA.
On-device vs edge server. A true on-device model runs on the tablet with no network dependency. An edge server inside the facility firewall offloads compute but requires the tablet to reach the LAN. SNF networks are unreliable enough that the distinction matters in practice - a model that depends on the LAN will fail on tablets that drift to poor signal near the end of a wing. The connectivity profile of your actual facility determines which architecture is appropriate.
Model size vs device spec. Shared facility tablets are often 3-4 years old. A model that runs within acceptable latency on a new iPad Pro may not perform adequately on a 2021 iPad 8th gen. Scoping the model to the device floor you actually have - not the device floor you'd prefer - avoids a hardware refresh becoming a prerequisite for the AI feature.
CNA workflow fit. CNAs document ADL completions, vitals observations, and behavioral notes. These are short, structured inputs - not long-form clinical narratives. A general-purpose summarization model is the wrong tool for this task. The model has to be configured for the specific input types your CNAs produce, or the outputs will require more correction time than just typing the note manually.
HIPAA audit logging. Local processing still requires a log that proves the model didn't transmit resident data outside the device or the facility boundary. The logging architecture has to be designed and reviewed before the compliance team signs off, not added as a patch after the first audit question.
Most teams spend 4-6 months discovering these decisions by building the wrong version first. A team that has shipped this before compresses that to 1 week.
On-Device AI vs. Cloud AI: What's the Real Difference?
| Factor | On-Device AI | Cloud AI |
|---|---|---|
| Data transmission | None — data never leaves the device | All inputs sent to external server |
| Compliance | No BAA/DPA required for inference step | Requires BAA (HIPAA) or DPA (GDPR) |
| Latency | Under 100ms on Neural Engine | 300ms–2s (network + server queue) |
| Cost at scale | Fixed — one-time integration | Variable — $0.001–$0.01 per query |
| Offline capability | Full functionality, no connectivity needed | Requires active internet connection |
| Model size | 1B–7B parameters (quantized) | Unlimited (GPT-4, Claude 3, etc.) |
| Data sovereignty | Device-local, no cross-border transfer | Depends on server region and DPA chain |
The right choice depends on your compliance constraints, query volume, and task complexity. Wednesday scopes this in the first week — before any code is written.
Why is Wednesday the right team for on-device AI?
We built Off Grid because we hit every one of these problems in production. Off Grid is the fastest-growing on-device AI application in the world, with 50,000+ users running it today.
It's open source, with 1,650+ stars on GitHub and contributors from across the world. It has been cited in peer-reviewed clinical research on offline mobile edge AI.
Every decision named above - model choice, platform, server boundary, compliance posture - we have made before, at scale, for real deployments.
How long does the integration take, and what does it cost?
The engagement is four sprints. Each sprint is fixed-price. Each sprint has a named deliverable your team can put on a roadmap.
Discovery (Week 1, $5K): We resolve the four decisions - model, platform, server boundary, compliance posture. Deliverable: a 1-page architecture doc your CTO can take to the board and your Privacy Officer can take to Legal.
Integration (Weeks 2-3, $5K-$10K): We ship the on-device model into your app behind a feature flag. Deliverable: a working build your QA team can test against real workflows.
Optimization (Weeks 4-5, $5K-$10K): We hit the performance and compliance targets from the discovery doc. Deliverable: benchmarks signed off by your team.
Production hardening (Week 6, $5K): Edge cases, OS version coverage, app store and compliance review readiness. Deliverable: shippable build.
4-6 weeks total. $20K-$30K total.
Money back if we don't hit the benchmarks. We have not had to refund.
"Retention improved from 42% to 76% at 3 months. AI recommendations rated 'highly relevant' by 87% of users." - Jackson Reed, Owner, Vita Sync Health
Is on-device AI right for your organization?
Worth 30 minutes? We'll walk you through what your version of the four decisions looks like, what a realistic scope and timeline would be for your app, and what your compliance posture and on-device target mean in practice.
You'll leave with enough to run a planning meeting next week. No pitch deck.
If we're not the right team, we'll tell you who is.
Book a call with the Wednesday team
Frequently Asked Questions
Q: Can a skilled nursing mobile app use AI without violating HIPAA?
Yes. If inference runs on-device and PHI never transmits to an external server, there is no cloud processing covered under HIPAA's Business Associate rules. The compliance posture depends entirely on where data flows — Wednesday resolves this in week one.
Q: What is the HIPAA risk of cloud AI vs. on-device AI in a clinical app?
Cloud AI sends every prompt — including any PHI in a note or query — to a third-party server. That server becomes a Business Associate requiring a BAA, which many Privacy Officers won't sign for consumer cloud providers. On-device AI processes locally. Nothing leaves. No BAA required for the inference step.
Q: How long does HIPAA-compliant on-device AI take to ship for a skilled nursing app?
4–6 weeks. Week 1: model selection, platform sequence, server boundary, audit trail format. Weeks 2–3: model ships into app behind a feature flag. Weeks 4–5: performance and compliance benchmarks. Week 6: OS coverage, store submission, compliance review readiness.
Q: What does HIPAA-compliant on-device AI cost?
$20K–$30K across four fixed-price sprints: Discovery ($5K), Integration ($5K–$10K), Optimization ($5K–$10K), Production hardening ($5K). Money back if benchmarks aren't met.
Q: Which on-device AI models are appropriate for clinical use?
Documentation assistance: 2B–7B parameter quantized model (Mistral, Gemma, Phi). Decision support: larger model or RAG architecture. Triage screening: under 1B parameters. Model selection is the first decision in the discovery sprint — before any code.
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