What AI Means for ePrescribing: Faster, Better Solutions for Specialty and Consumer Practices

For years, electronic medical records (EMRs) and ePrescribing solutions have been dominated by massive, one-size-fits-all platforms. If you were a small, single dermatologist office or a massive nationwide direct-to-consumer telehealth provider, you had to work within the constraints of systems built for a broad audience, designed to serve everyone.

That's starting to change, thanks to artificial intelligence (AI)-driven coding.

From Engineering Teams to Single Developers

At Interra Health, we're seeing a massive increase in the volume of new electronic health record (EHR) systems coming to market. Innovators building the next generation of health care software come to us for electronic prescribing capabilities. With tools like Claude Code and other AI-assisted development tools, what’s possible for smaller teams and specialty practices has shifted.

For example, a dermatologist knows exactly what workflow would make their day more productive, what the workflow should look like for their specialty, and which friction points slow them down. Before AI, translating that vision into software required a full engineering team, significant capital, and months of development.

What once required full engineering teams can now be done by a single person without formal software development training. This democratization of software technology, once more theoretical, has become a new reality as an ever-increasing number of innovators reach out.

The Rise of Specialty and Direct-to-Consumer Solutions

Two trends are converging to drive demand for AI in these smaller, specialty practices. The first is the explosion of direct-to-consumer health care, particularly in areas like glucagon-like peptide-1 (GLP-1) medications, peptides, and compounded pharmaceuticals. Compounding has always been part of pharmacy practice, but the surge in demand for GLP-1 drugs has brought it into the mainstream spotlight.

The second trend is the move toward specialty EMR interfaces. The era of forcing an ophthalmologist, a concierge medicine practice, or a weight loss clinic into the same workflow as a hospital system using a giant, more generic EMR like Epic is over. Physicians want tools tailored to how they actually practice and prescribe, and this effort can now be physician-led rather than engineer-led. Those tools can also be built without multi-million-dollar budgets, which was a dealbreaker for most smaller specialty practices.

For developers building e-prescribing into their solutions, an API-forward approach means the connection to existing prescription infrastructure is no longer a major engineering undertaking. What once would have been resource-prohibitive is now achievable.

Build Versus Buy

There's always a build-versus-buy conversation in health care technology. The calculus is shifting, but the right answer isn't always to build everything from scratch, even when AI makes building easier.

The complexity of electronic prescribing is real. Two-factor authentication, regulatory compliance, prescription drug monitoring integration, and formulary checks—all these requirements accumulate quickly.

The smarter path is to leverage existing marketplace solutions for the foundational requirements while directing your resources toward what makes your solution unique. If you're building, for example, an EMR with ePrescribing for a cardiologist, your competitive advantage is the clinical workflow, the interface, and the deep specialty knowledge baked into the product, not the implementation of identity verification. Leverage what already exists in the market and build on what makes you unique.

AI for Good in ePrescribing

Much of what's been written about AI in health care remains hypothetical—theoretical applications that haven't yet reached the exam room or the prescription pad. What's happening now with ePrescribing is more concrete and useful.

AI earns its place in health care by presenting information in a more usable, actionable form. It reduces administrative burden and helps providers make better-informed decisions faster. It can surface the right data at the right moment in a clinical workflow, rather than burying it in a system designed for someone else's specialty.

The winners in this shift to AI-built solutions are providers, who gain tools actually suited to their practice, and patients, who benefit from more accessible, more responsive care.