Scribematic - AI Automated Medical Documentation

The market just shifted. Embedded AI scribing is no longer a differentiator, it’s table stakes. Clinicians are seeing it, competitors are selling it, and procurement teams are adding it to RFP checklists. The EHRs that move now will lock in loyalty and growth. Those that hesitate will fight to win back customers on someone else’s terms.

Why This Moment Matters

When the largest players in our industry roll out their own AI scribes, they’re not just launching a feature, they’re reshaping buyer expectations. Mid-sized EHRs are about to face a new kind of competition: one that checks every box on innovation, security, and workflow integration.

The opportunity? You don’t have to match their budgets to match, or beat, their offering. But you do have to make the right move, now. 

The Risk of Going It Alone

Building enterprise-grade AI for healthcare is a lot more than coding and prototypes—it’s a high-stakes balancing act between innovation, security, and real-world delivery.

1. High Failure, Long Time to Launch

• A shocking 80% of healthcare AI pilots fail to scale beyond pilot phase, often crashing due to poor data quality, unclear ROI, or compliance roadblocks. Digital Health Technology News
• Less than half (48%) of AI prototypes actually make it into production, and the prototyping-to-launch journey typically takes 8 months or more. Moonshot News

Every month spent building is a month lost in renewing contracts or matching buyer expectations.

2. Security & Compliance Is Not Optional

Handling PHI inside clinical workflows means you’re signing up for a compliance gauntlet: HIPAA, SOC 2, HITRUST, data residency rules, model drift surveillance, audit logging, and more. These aren’t just boxes to check—they can consume months, even a year, especially when building from scratch.

Getting this right isn’t just necessary; it’s a barrier to entry that most mid-sized EHRs can’t clear fast enough.

3. Data & Maintenance Complexity

• Scaling an AI pilot reveals hidden data issues—fragmented sources, poor consistency, missing or stale data, all teaching your models bad habits. Agility at Scale
• Once deployed, AI isn’t “set and forget.” Model drift—changes in input or clinical patterns—necessitates ongoing retraining, monitoring, and quality assurance, adding another layer of operational overhead.

So What’s at Stake for Mid-Sized EHRs?

Choosing to build your AI scribe internally is not just a tech project, it’s a strategic risk. You’re betting your roadmap, your renewals, and your long-term differentiation on navigating this minefield flawlessly.

And let’s be honest—budget, time, and war-room talent are precious. The question isn’t just can you build it? It’s should you—when you can embed proven, secure AI now?

That’s why the next move matters.

Every month you’re building is a month where:

• Competitors are advancing their customer experience.
• Clinicians are getting used to AI-assisted workflows — in someone else’s EHR.
• Your renewal risk grows.

The Bottom Line

This is the competitive inflection point for mid-sized EHRs. Your next move will determine whether you win market share in the AI era or watch it erode.

Matic Inside gives you the fastest path to an enterprise-grade, secure, embedded AI scribe — without the risk, the overhead, or the delay.

Move now. Your customers already expect it.

[Put Matic Inside →]