Building reliable connections to EHR systems like Epic, Cerner, DIPS, TakeCare and many others is a complex challenge that many healthtech startups face. When we started Leyr, integrating with a single EHR system would take us up to 2 months. Today, we can do it in 2 weeks. Here's our journey and the technical architecture that made it possible.

Common EHR Integration Challenges

The complexity of healthcare integrations goes beyond simple API connections. Take appointment scheduling - a cornerstone feature for any digital health platform. Each EHR handles this differently, yet your customers expect consistent data.

For example, when fetching available appointment slots, some EHR systems return comprehensive data including practitioner names and service details. Others provide only IDs, requiring additional API calls to compile complete information. Our solution? A smart abstraction layer that handles these differences transparently, ensuring consistent data delivery with any EHR.

Building a Scalable Healthcare API

Our architecture addresses three critical areas that every healthtech startup faces when dealing with EHR integrations:

Data Standardization

We've built robust abstractions using Python's (and actually Pydantic) class hierarchy to handle various EHR data formats. We have our own lean format, which we have discussed before, but this also includes automatic transformation to FHIR resources like Patient and Appointment, making it easier for healthcare applications to maintain compliance while scaling across different EHR systems.

Reliable Testing Framework

Our testing approach uses OpenAPI-generated clients and Pydantic type checking to ensure data consistency. This is crucial for healthcare applications where data accuracy is non-negotiable. For instance, we enforce proper timezone handling across all datetime fields, even when the source EHR doesn't provide it.

Real-time Monitoring

Healthcare systems need reliable uptime. Our monitoring system runs health checks every 3 minutes on all connected EHRs, allowing us to detect and respond to issues before they impact patient care.

EHR Integration Best Practices

Through integrating with major EHR systems, we've developed several key practices:

1. Abstraction First - our architecture uses abstract classes to standardize common operations across different EHRs. This means when we encounter a new system, we often already have default implementations ready for common scenarios.

2. Unified Data Model - when connecting to any EHR, our unified data model ensures consistent output. This dramatically simplifies development for healthcare applications using our API.

3. Knowledge Management - every integration teaches us something new. Our team maintains a comprehensive technical decision log and integration guide, making each subsequent connection faster and more reliable.

Modern Tools for Modern Healthcare

While our foundation is built on solid architectural principles, we've embraced modern tools to speed up integration time. AI copilots have revolutionized our development process, particularly in generating standard integration components and test suites. However, the core integration logic - understanding and implementing EHR-specific behaviors - remains a human task that benefits from our accumulated experience.

From 2 Months to 2 Weeks: The Results

Our approach has transformed what was once a months-long process into a two-week sprint. This isn't just about coding faster - it's about having the right architecture, tools, and expertise in place. Each new integration makes us more efficient at handling the next one.

For Healthtech Startups

If you're building a healthcare application that needs to connect with multiple EHR systems, we can help. Our proven integration process means:

• Faster time to market

• Reliable, consistent data across all EHR systems

• Simplified API that handles complexity behind the scenes

• Built-in monitoring and support

Ready to accelerate your EHR integration? Let's talk about making your application connected and interoperable.