Healthcare Software Interoperability: Extracting UI Logic for HL7 Integration Projects

Legacy healthcare systems are essentially black boxes where critical patient data goes to die. In the high-stakes world of clinical informatics, the friction between a 20-year-old COBOL-based Electronic Medical Record (EMR) and a modern FHIR-based API isn't just a technical hurdle; it’s a patient safety risk. When a legacy UI cannot communicate its internal logic to a modern interoperability layer, clinical workflows break, and data silos harden.

The core challenge of healthcare software interoperability extracting logic from these aging interfaces is that the documentation is almost always missing. According to Replay's analysis, 67% of legacy systems lack any form of up-to-date documentation, leaving developers to guess how a "Save Patient" button actually interacts with the underlying database or which hidden validation rules are firing before an HL7 message is dispatched.

TL;DR: Modernizing healthcare systems requires more than just data migration; it requires extracting the UI logic trapped in legacy systems. Manual extraction takes roughly 40 hours per screen, but Visual Reverse Engineering via Replay reduces this to 4 hours. By converting video recordings of legacy workflows into documented React components, organizations can accelerate HL7/FHIR integration projects, reduce technical debt, and ensure HIPAA-compliant modernization without rewriting from scratch.

The High Cost of Manual Healthcare Software Interoperability Extracting#

The global technical debt currently sits at a staggering $3.6 trillion, and nowhere is this more visible than in healthcare. When a hospital system decides to integrate a legacy lab module with a new centralized portal, they hit a wall. The legacy UI contains the "truth" of the workflow—the sequence of clicks, the conditional fields, and the specific data formats required for a valid HL7 MSH (Message Header) segment.

Traditionally, this meant a developer sitting with a subject matter expert (SME), recording a screen with a basic tool, and then manually rewriting that interface in a modern framework like React. This manual process is the primary reason why 70% of legacy rewrites fail or exceed their original timelines.

Video-to-code is the process of recording real user sessions within these legacy applications and using AI-driven visual analysis to automatically generate functional, documented React components and architectural "Flows." This technology, pioneered by Replay, bypasses the need for source code access, which is often lost or too convoluted to parse.

Comparison: Manual Extraction vs. Replay Visual Reverse Engineering#

Healthcare Software Interoperability Extracting: Bridging the Gap Between UI and HL7#

When we talk about healthcare software interoperability extracting logic, we are specifically looking for the triggers that initiate data exchange. In an HL7 v2.x environment, a user clicking "Admit" in a legacy Delphi application triggers an ADT (Admission, Discharge, Transfer) message.

If you are building a modern React-based front-end to replace that legacy screen, you must understand exactly which UI fields map to which HL7 segments. For example, the

segment (Patient Name) might be split across three different text boxes in the legacy UI with specific masks.

Industry experts recommend a "Visual-First" approach to this extraction. By using Replay, architects can record the legacy workflow of admitting a patient. Replay’s AI Automation Suite then identifies the input fields, the validation logic (e.g., "Social Security Number must be 9 digits"), and the state changes, outputting a clean TypeScript component that is ready for integration with a modern HL7 engine like Mirth Connect or a FHIR API.

Implementation: Mapping Extracted UI State to FHIR#

Once Replay extracts the UI logic, the next step is mapping that state to a modern resource. Below is a TypeScript example of how a component extracted via Replay might handle patient data mapping to a FHIR Patient resource.

typescript
import React, { useState } from 'react';

// Component logic extracted from legacy "Patient Entry" screen via Replay
interface PatientData {
  legacyId: string;
  firstName: string;
  lastName: string;
  dob: string;
  gender: 'M' | 'F' | 'O';
}

const PatientModernizer: React.FC = () => {
  const [patient, setPatient] = useState<PatientData>({
    legacyId: '',
    firstName: '',
    lastName: '',
    dob: '',
    gender: 'M',
  });

  // This mapping logic was identified by Replay's Flow analysis
  const convertToFHIR = (data: PatientData) => {
    return {
      resourceType: "Patient",
      identifier: [{ system: "http://hospital.org/ids", value: data.legacyId }],
      name: [{ family: data.lastName, given: [data.firstName] }],
      birthDate: data.dob,
      gender: data.gender === 'M' ? 'male' : data.gender === 'F' ? 'female' : 'other'
    };
  };

  const handleSync = async () => {
    const fhirResource = convertToFHIR(patient);
    // Logic to POST to Interoperability Layer
    console.log('Dispatching HL7/FHIR Payload:', fhirResource);
  };

  return (
    <div className="p-4 border rounded shadow-sm">
      <h3 className="text-lg font-bold">Patient Demographics (Extracted)</h3>
      <input 
        value={patient.firstName} 
        onChange={(e) => setPatient({...patient, firstName: e.target.value})}
        className="block w-full mb-2 p-2 border"
        placeholder="First Name"
      />
      {/* Additional fields generated by Replay's Blueprint Editor */}
      <button onClick={handleSync} className="bg-blue-600 text-white p-2 rounded">
        Sync to HL7 Interface
      </button>
    </div>
  );
};

export default PatientModernizer;

Solving the Documentation Vacuum in Healthcare IT#

A major roadblock in healthcare software interoperability extracting is the "Tribal Knowledge" trap. In many healthcare organizations, the only person who knows how the legacy billing system handles insurance secondary-payer logic is a developer who is six months away from retirement.

Replay’s Library and Blueprints features act as a living repository of this knowledge. When you record a workflow, Replay doesn't just give you code; it gives you a visual blueprint of the architecture. This is critical for Legacy Modernization Strategies in regulated environments like healthcare, where every change must be audited and validated.

According to Replay's analysis, enterprise modernization projects that utilize visual reverse engineering see a 70% average time saving. This is achieved by eliminating the "Discovery Phase" where developers spend months trying to understand the legacy system's behavior. Instead of 18 months of manual coding, teams can move from recording to a functional React prototype in days.

Visualizing the Data Flow#

In a typical HL7 integration project, the data flow looks like this:

1. •Legacy UI Action: User updates a prescription.
2. •Logic Extraction: Replay identifies the
   text

   onChange
   events and the hidden
   text

   POST
   request triggered by the legacy app.
3. •Component Generation: Replay generates a React component that mirrors this behavior but uses modern hooks and state management.
4. •Interoperability Mapping: The modern component sends data to a FHIR gateway, which translates it for the rest of the ecosystem.

For more on how to structure these generated components, see our guide on Automating Design Systems.

Security and Compliance in Interoperability Projects#

In healthcare, "moving fast" cannot come at the expense of "breaking things." HIPAA and SOC2 compliance are non-negotiable. One of the risks of traditional healthcare software interoperability extracting—where developers might use unauthorized screen-scraping or third-party logging tools—is the exposure of Protected Health Information (PHI).

Replay is built for regulated environments. It is HIPAA-ready and offers On-Premise deployment options, ensuring that the video recordings of legacy UIs never leave the secure hospital network. The AI Automation Suite processes the visual data locally or within a secure VPC, generating the React code without ever storing sensitive patient records in a public cloud.

Technical Implementation: Handling Legacy Grid Data#

One of the most difficult aspects of healthcare UI logic is the "Grid." Legacy EMRs are often just massive tables of patient vitals or lab results. Extracting the logic for how these grids sort, filter, and eventually export data to an HL7 ORU (Observation Result) message is a nightmare.

Replay's engine recognizes these patterns. It can identify a legacy data grid and generate a modern Tailwind-styled React component that maintains the same data-binding logic.

typescript
// Example of a Vitals Grid component extracted via Replay Visual Reverse Engineering
import React from 'react';

interface VitalSign {
  timestamp: string;
  type: 'BP' | 'HR' | 'Temp' | 'SpO2';
  value: string;
}

const VitalsGrid: React.FC<{ data: VitalSign[] }> = ({ data }) => {
  // Replay identified that the legacy system highlights abnormal values
  const isAbnormal = (vital: VitalSign) => {
    if (vital.type === 'HR' && parseInt(vital.value) > 100) return true;
    return false;
  };

  return (
    <table className="min-w-full divide-y divide-gray-200">
      <thead className="bg-gray-50">
        <tr>
          <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase">Time</th>
          <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase">Type</th>
          <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase">Value</th>
        </tr>
      </thead>
      <tbody className="bg-white divide-y divide-gray-200">
        {data.map((row, idx) => (
          <tr key={idx} className={isAbnormal(row) ? 'bg-red-50' : ''}>
            <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">{row.timestamp}</td>
            <td className="px-6 py-4 whitespace-nowrap text-sm font-medium text-gray-900">{row.type}</td>
            <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">{row.value}</td>
          </tr>
        ))}
      </tbody>
    </table>
  );
};

The Future of Healthcare Interoperability#

As the industry moves toward TEFCA (Trusted Exchange Framework and Common Agreement) and more aggressive interoperability mandates, the ability to rapidly extract logic from legacy systems will become a competitive necessity. Organizations can no longer afford the 18-month average enterprise rewrite timeline.

By leveraging Replay, healthcare IT departments can transform from "maintenance centers" into "innovation hubs." Instead of spending 40 hours per screen on manual documentation and coding, engineers can focus on high-level architecture and patient outcomes.

The goal of healthcare software interoperability extracting is not just to move data—it is to liberate the clinical workflows that have been trapped in aging software for decades. Visual Reverse Engineering is the key to unlocking that cage.

Frequently Asked Questions#

What is the biggest challenge in healthcare software interoperability extracting?#

The biggest challenge is the lack of documentation and the "black box" nature of legacy codebases. Most systems were built before modern API standards like FHIR existed, meaning the logic for how data is processed is hidden within the UI's event handlers. Replay solves this by visually capturing these workflows and converting them into documented React code.

How does Replay handle HIPAA compliance during the extraction process?#

Replay is built for highly regulated industries. It is HIPAA-ready and SOC2 compliant. For maximum security, Replay offers an On-Premise deployment option, allowing healthcare organizations to record and process legacy UI logic within their own firewalled environment, ensuring PHI never leaves their control.

Can Replay extract logic from terminal-based or Citrix-hosted applications?#

Yes. Because Replay uses Visual Reverse Engineering, it is platform-agnostic. As long as a user can interact with the interface on a screen, Replay can record the workflow and extract the underlying component logic, regardless of whether the legacy app is running on a mainframe, a Citrix server, or a local Java environment.

How much time can be saved using Visual Reverse Engineering for HL7 projects?#

On average, Replay reduces the time spent on UI modernization by 70%. In practical terms, a task that typically takes 40 hours of manual analysis and coding—such as recreating a complex clinical form—can be completed in approximately 4 hours using Replay’s automated extraction tools.

Ready to modernize without rewriting? Book a pilot with Replay