When we talk to our clients, there is one issue that always persists, no matter the client: lab tests are ordered in seconds, but results take time, delaying diagnosis. So, I decided to dig a little deeper to understand the cause of this.

And the result of this research was this blog, because many lab-EHR integrations don’t connect these systems in the right way. This then leads to fragmentation, poor connectivity, and manual reconciliation, increasing the after-hours manual work.

In fact, a report on the National Library of Medicine found that manual data entry can increase 15-20 hours per week for each staff member. And the biggest challenge is that it has an error rate of 1-4%, if you apply this for lab workflows, even a small error like incorrect test-codes or spelling mistakes can lead to mismatched records, misdiagnosis, and delayed follow-ups.

However, this issue is easily fixable, and we have already solved this problem for many of our clients. The simple solution is to implement lab EHR HL7 integration, which can automate ORM and ORU messages using HL7 ORM ORU messages, streamlining both lab test orders and EHR patient record updates.

With this, you and other healthcare organizations can move away from fragmented workflows to closed-loop diagnostic workflows. So, connecting labs is not enough; we have to shift them from manual to automated result delivery and from delayed diagnosis to real-time point-of-care decision-making.

That’s why, in this bidirectional lab EHR integration guide, we will break down how to automate HL7 lab results in EHR and how to get faster result delivery by automating lab orders and results delivery using HL7.

Understanding the HL7 Handshake: ORM vs ORU Messages

The introduction might have given you a basic idea of the problem and its consequences. Now, here we will understand where it actually happens and how we can fix it. In any clinic, the process of ordering is simple and the same. A clinician sends out an order to the contracted lab, the order is received, tests are done, and are sent back to the clinic.

In this process, there are two critical components working together: ORM messages and ORU messages. ORM (Order Message) is a message type that sends order requests, and ORU (Observation Result Message) is the message type that returns the results to the EHR system.

These work in an HL7-based system, and the process is like a handshake where ORM says run the tests, and ORU responds with results. Let’s take a look at the step-by-step process of how this handshake works when integrated properly:

1. A clinician sends out a lab test in the EHR.
2. An ORM message is generated and sent to the lab (LIS).
3. The lab processes the order and performs the test.
4. Once complete, an ORU message is sent back.
5. The result is automatically updated in the patient’s EHR.

And when this handshake happens only halfway, the workflow starts to break, leading to data duplication, missing orders, and delays in reconciliation. Having bidirectional integration is essential for this process to work seamlessly.

When the lab EHR integration is bidirectional, meaning EHR requests flow to LIS via ORM and LIS responses flow back to EHR via ORU, this becomes a real-time lab results automation, reducing delays and improving accuracy.

Architecture & Workflow: Automating Lab Orders & Results

After understanding the ORM-ORU handshake and where it can break, it is time to understand how it works in real clinical practice and how to automate it for the right impact. Here is a quick snapshot of the entire process:

What powers this whole process is an interface engine such as Mirth Connect, and it is the bridge that connects ORM with ORU and EHR with LIS systems. It ensures that HL7 messages are routed correctly, helps transform data formats, handles errors and retries, and makes sure that requests reach the right destination. Without this layer, integration becomes inconsistent.

Additionally, there are three other HL7 message types that work along with ORM and ORU. These message types are for specific data, such as PLD for patient details, OBR for order information, and OBX for test results. They help systems understand and process lab data automatically without manual intervention.

However, when it comes to automating lab orders and results delivery using HL7, you need to follow a step-by-step process:

1. Enable HL7 interface between EHR and LIS.
2. Use an interface engine such as Mirth Connect to route messages.
3. Map HL7 segments (PIC, OBR, OBX) for accurate data exchange.
4. Set up real-time triggers for order and result events.
5. Validate and normalize incoming data before storing it in the EHR.

By automating this process, it significantly reduces the time required for diagnosis as providers get updated patient data without wasting time on manual data entries. Most importantly, clinicians receive ready-to-use data, abnormal data trigger alerts, and decisions are made faster and on accurate data.

Workflow Optimization: Bidirectional vs Unidirectional Integration

Another issue in this entire process is the type of integration used, whether it is unidirectional or bidirectional. Because if the LIS to EHR integration is only unidirectional, then only ORU messages or lab results are automatically sent back to EHR, while ORM messages or orders are not fully integrated.

While on the surface this seems enough and sufficient, in the long run, there are many gaps, as orders and results may not match, and clinicians have to reconcile them manually. Moreover, this leads to:

• Missing or duplicated data.
• Mismatched patient records.
• Delays in diagnosis and follow-ups.

However, when the integration is bidirectional, both orders and results are fully integrated and flow to LIS and EHR effortlessly. This creates a closed-loop system in which every lab order is tracked from request to result.

Moreover, to ensure consistency across different systems, the tests must be standardized with LOINC (Logical Observation Identifiers Names and Codes). This makes sure that different systems understand data correctly without misinterpretation, reducing errors and improving interoperability.

In short, bidirectional integration makes the lab result automation truly automated and not just a partial one.

Security & Data Integrity in Lab Integration

While implementing HL7 integration automates the lab result delivery, it also opens some security risks that need to be addressed carefully to avoid data breaches and huge penalties. The biggest risk is transferring data and protecting it during transit.

However, if you use VPN tunnels and TLS encryption for end-to-end data exchange, then data exchange between EHR and LIS remains secure and confidential. Moreover, being HIPAA-compliant is also essential for protecting sensitive patient data access and storage.

And for meeting HIPAA, having audit logs is important for tracking every data access, edit, and exchange for accountability and traceability for cases such as data failures or discrepancies. But only maintaining security is not enough; data integrity is also important.

The systems must match correct lab results to the right patients, and to ensure this, you must map and validate data across systems with integration engines. These engines help in validating HL7 message formats, preventing data duplication, and maintaining consistency between LIS and EHR data.

Most importantly, when all this is correct, the automated systems can trigger intelligent alerts rather than false alarms. This enables proactive care, immediate response, and makes care delivery even more accurate and on time.

Challenges & Optimization Strategies

However, this lab EHR integration is not easy, and in real clinical environments, it can face several challenges. That’s why it is important to understand these challenges and how to handle them without breaking consistency and integration to build a reliable integration. Here are some of the key challenges:

• HL7 Format Variations: Not every lab uses similar data formats and HL7 message structures, making it difficult to ensure consistency across systems. To solve this challenge, you must use standardization by implementing LOINC and carefully mapping data within the interface engine.
• Patient & Test Codes Mismatch: There is a chance of data mismatch, especially for patient and lab codes, if there are inconsistent identifiers. For this, the best strategy is to use the master patient index and standardized coding systems to maintain data accuracy.
• Latency in Result Delivery: Delays in message transmission or processing can slow down diagnosis and treatment decisions. Real-time, event-driven workflows can help you ensure results are delivered without unnecessary delay.
• Handling High Transaction Volumes: Large healthcare systems process thousands of lab messages daily. By implementing scalable infrastructure and queue-based processing, you can prevent system overload and ensure consistent performance.
• Security & Compliance Risks: A patient’s lab data is sensitive and must be transferred through secure and encrypted data pipelines. That’s why end-to-end encryption, role-based access control, and audit logs are essential to meet regulatory requirements and prevent breaches.

In short, by identifying challenges and implementing the right strategies, healthcare organizations can easily overcome challenges and build seamless lab EHR integration.

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