Building EHR Integrations: 6 Things to Get Right Before Writing Code

Connecting an application to an Electronic Health Record (EHR) system sounds straightforward.

Find the API. Authenticate. Fetch patient records. Send updates. Done.

Except healthcare integrations rarely work that way in practice.

You might connect successfully but receive incomplete data. A request might time out and create duplicate records when retried. Two systems might use different formats for the same clinical information. Or your application might retrieve patient data without enforcing the right access permissions.

These problems often start before the first line of integration code is written.

If you're building a telehealth platform, patient portal, healthcare SaaS product, or clinical application, here are six things to get right before starting an EHR integration.

1. Understand the Data Flow Before Choosing the API

Start by identifying what information your application needs and where that information comes from.

For example, a telehealth platform might need to:

• Retrieve patient demographics and medical history.
• Read medications, allergies, and clinical observations.
• Access encounter details.
• Send consultation notes to the EHR.
• Create or update supported clinical records.

These workflows can involve different API resources, permissions, and write operations.

Before implementation, document three things for every data type:

1. Source: Which system owns the information?
2. Direction: Does your application read, write, or synchronize it?
3. Timing: Does it need to be available immediately, periodically, or only when requested?

This helps establish the integration boundary and prevents the application from treating every connected system as an interchangeable database.

A useful rule: define the workflow first, then identify the API operations needed to support it.

2. Understand FHIR and the EHR's Actual Implementation

FHIR (Fast Healthcare Interoperability Resources) provides a standard for exchanging healthcare information.

It defines resources such as:

• Patient — patient demographics and identifying information.
• Observation — measurements and clinical observations.
• Encounter — a healthcare interaction.
• Condition — a health condition or diagnosis.
• MedicationRequest — a medication order or prescription request.
• DiagnosticReport — diagnostic findings and report information.

FHIR makes interoperability easier, but it doesn't guarantee that every EHR implements every resource in the same way.

One vendor may support a particular resource but restrict certain operations. Another may require specific profiles, search parameters, or authorization scopes.

Before writing code, check:

• Which FHIR version is supported?
• Which resources and operations are available?
• Are there vendor-specific implementation guides?
• Which fields are mandatory?
• What search parameters and pagination behavior are supported?
• Which authorization scopes are required?

Also review the vendor's sandbox and test data, where available.

Don't build against the FHIR specification alone. Build against the specification and the target EHR's implementation.

3. Design Patient Matching and Data Mapping Carefully

One of the most important integration decisions is determining whether two records represent the same patient.

Your application may use an internal patient ID, while the EHR uses its own identifier. Names and dates of birth alone may not reliably establish identity.

A safer design uses the appropriate identifiers and matching rules agreed upon for the integration.

For example, maintain a mapping between your internal patient record and the corresponding identifier in the external system. Account for multiple identifiers where the workflow requires them.

Data mapping deserves the same attention.

A field in your application may not correspond directly to a field in the EHR. Clinical concepts may use standardized codes, specific units, or vendor-defined profiles.

Before implementing transformations, establish:

• Identifier mapping rules.
• Field-level mappings.
• Required and optional values.
• Terminology and unit handling.
• Missing or conflicting data behavior.
• Validation rules for incoming and outgoing records.

Avoid silently filling gaps with assumptions.

If a record cannot be matched confidently, the system should follow a defined exception-handling process rather than attaching the information to a potentially incorrect patient.

4. Plan Authentication, Authorization, and Audit Logging

An integration that can retrieve patient information must also enforce appropriate access restrictions.

Depending on the EHR and application, authentication may use OAuth 2.0, SMART on FHIR, or another supported mechanism.

But successful authentication doesn't mean every request is authorized.

Consider these implementation details:

Token management

• Store credentials securely.
• Handle token expiration and refresh where supported.
• Avoid exposing access tokens in logs or client-side code.
• Request only the scopes the application needs.

Authorization

• Enforce permissions on the server.
• Verify access to the requested patient and resource.
• Separate user-authorized access from background service operations.
• Handle revoked access and failed authorization explicitly.

Audit logging

• Record relevant access and modification events.
• Capture enough context to investigate problems.
• Restrict access to audit logs.
• Avoid unnecessarily copying protected health information into log messages.

For US healthcare applications subject to HIPAA, these controls should be designed in the context of the applicable security and privacy requirements. An API integration alone does not establish compliance.

5. Make Retries Safe and Handle Failures Explicitly

External systems can be slow or unavailable. Requests can time out, rate limits can be reached, and network connections can fail.

The difficult part is that a timeout doesn't always tell you whether the operation succeeded.

Imagine your application sends a request to create an encounter-related record. The EHR processes it, but the response never reaches your application.

Your application retries the request.

Without suitable safeguards, you may create a duplicate.

A robust integration should consider:

• Timeouts: Avoid requests waiting indefinitely.
• Retry policies: Retry transient failures with bounded exponential backoff and jitter.
• Rate limits: Respect vendor limits and retry guidance.
• Idempotency: Use supported idempotency mechanisms or another reliable duplicate-prevention strategy.
• Error classification: Distinguish temporary failures from invalid requests and authorization errors.
• Reconciliation: Detect and resolve records that may have been missed or processed inconsistently.

Not every API supports idempotency keys, so the implementation must follow the actual vendor's capabilities.

For workflows that can tolerate asynchronous processing, background jobs and queues can help separate user-facing requests from external-system availability.

The goal isn't to prevent every failure. It's to make failures predictable and recoverable.

6. Test the Complete Workflow, Not Just Individual Endpoints

A successful API response proves that one request worked. It doesn't prove that the entire healthcare workflow is correct.

Suppose your application can retrieve a patient record successfully. You still need to validate what happens when the record is incomplete, the patient has multiple identifiers, authorization changes, or a downstream system becomes unavailable.

A useful test plan includes:

Test area	What to validate
Data mapping	Fields, identifiers, codes, and units are mapped correctly.
Authorization	Users and services cannot access unauthorized records.
Pagination	Large result sets are processed without missing records.
Error handling	Timeouts, rate limits, and server errors are handled safely.
Duplicate prevention	Retried operations don't create unintended duplicates.
Data freshness	Updates and synchronization delays are handled as expected.
Audit logging	Relevant access and modification events are recorded.
End-to-end workflows	Data moves correctly across the complete user journey.

Use vendor sandboxes and synthetic test data where available. Test against realistic edge cases, not only successful responses.

Also verify what happens when an integration is disconnected, credentials expire, or a vendor changes its API behavior.

These scenarios often reveal issues that simple endpoint tests miss.

A Practical Checklist Before Development

Before committing to an EHR integration, make sure you can answer these questions:

• [ ] Do we know which workflows the integration must support?
• [ ] Have we confirmed the target EHR's API capabilities and supported FHIR version?
• [ ] Have we documented data mappings and patient-matching rules?
• [ ] Are authentication scopes and authorization rules defined?
• [ ] Do we have a plan for timeouts, retries, duplicate prevention, and reconciliation?
• [ ] Have we defined integration tests and end-to-end acceptance criteria?

If several answers are still unclear, the next step is requirements discovery, not more code.

Final Thoughts

EHR integration is not simply an API task. It sits at the intersection of data modeling, identity, security, clinical workflows, and distributed-system reliability.

FHIR gives development teams a shared foundation, but reliable interoperability still depends on understanding the systems on both sides of the connection.

The best time to discover an identifier mismatch, missing permission, or unsupported API operation is before the integration becomes a dependency for your entire product.

Map the workflow. Verify the API. Define the data rules. Plan for failure. Then start coding.

If you're building a healthcare product, these decisions can make the difference between an integration that works in a demo and one that holds up in real-world use.