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Oncology Implementation Guide for Kenya (Oncology-IG-Kenya)

Official URL: http://example.org/ImplementationGuide/medby Version: 0.1.0
Draft as of 2026-06-23 Computable Name: oncologyfhirigkenya

Oncology FHIR Implementation Guide

Demonstrating Interoperable Oncology Care Using HL7® FHIR®, HL7 v2, OpenEMR, and Mirth Connect

This Implementation Guide (IG) is a proof-of-concept demonstration showing how modern healthcare interoperability standards can be combined to improve oncology care workflows. It is not an official production system, but a technical demonstration of how HL7® FHIR®, HL7 v2 messaging, OpenEMR, and Mirth Connect integration engine can work together to enable practical, data-driven cancer care.

Home

  • Version: 0.1.0
  • Status: Proof of Concept / Demonstration
  • FHIR Version: R4
  • Architecture: OpenEMR + Mirth Connect + HL7 v2 + FHIR R4
  • Computable Name: oncology-fhir-poc
  • Build Info: Local IG Publisher + SUSHI-generated artifacts

Purpose & Background

Cancer care requires coordinated, longitudinal, and data-driven workflows across multiple systems—laboratory, imaging, pharmacy, clinical documentation, and billing systems.

This Implementation Guide demonstrates how interoperability can be achieved using:

  • OpenEMR as the core Electronic Health Record (EHR)
  • Mirth Connect as the integration engine for message routing and transformation
  • HL7 v2 messaging for legacy hospital system integration
  • HL7® FHIR® R4 for modern, structured clinical data exchange
  • Clinical Quality Measures (CQM) for evidence-based care tracking and outcome measurement

The goal is to demonstrate how these technologies can be combined to make oncology care more practical, connected, and measurable across the patient journey.

Note: This is a technical demonstration project intended for learning, prototyping, and innovation. It is not a certified clinical system.

Why This Matters

Oncology care is complex, fragmented, and data-intensive. Many healthcare environments still rely on disconnected systems that limit continuity of care.

This Proof of Concept demonstrates how interoperability can solve key challenges:

  • Fragmented patient records across departments
  • Lack of standardized oncology data models
  • Limited integration between lab, imaging, and clinical systems
  • Difficulty tracking outcomes and quality metrics
  • Weak support for longitudinal cancer care pathways

By combining FHIR + HL7 v2 + OpenEMR + Mirth Connect, this project demonstrates a unified digital ecosystem for oncology workflows.

System Architecture Overview

Component Role
OpenEMR Primary EHR system for patient records and clinical workflows
Mirth Connect Integration engine for HL7 v2 ↔ FHIR transformation
HL7 v2 Messaging standard for labs, radiology, and legacy systems
HL7® FHIR® Modern API-based interoperability layer
CQM Engine Quality measurement and evidence-based oncology tracking

Oncology Care Workflow (Interoperability Mapping)

Care Stage Data Standards Used
Patient Registration OpenEMR + FHIR Patient
Screening & Risk Assessment FHIR Observation, CQM metrics
Diagnosis (Lab/Imaging) HL7 v2 ORU messages → Mirth → FHIR DiagnosticReport
Tumor Classification FHIR Condition, Observation
Treatment Planning FHIR CarePlan, OpenEMR workflows
Chemotherapy Administration FHIR MedicationRequest, MedicationAdministration
Radiology & Procedures HL7 v2 ORM → FHIR Procedure
Follow-up Monitoring FHIR Observation, CQM tracking
Outcomes Measurement Clinical Quality Measures (CQM)
Reporting & Analytics FHIR + aggregated CQM datasets

Clinical Quality Measures (CQM) Integration

This IG demonstrates how oncology outcomes can be measured using structured data:

Examples include:

  • Time from diagnosis → treatment initiation
  • Stage at diagnosis distribution
  • Treatment adherence rates
  • Survival follow-up tracking
  • Chemotherapy adverse event monitoring
  • Screening coverage rates

These metrics support evidence-based oncology care improvement.

Interoperability Flow (HL7 v2 → FHIR)

  1. Lab system sends HL7 v2 ORU message
  2. Mirth Connect receives and transforms message
  3. Data is mapped into FHIR DiagnosticReport + Observation
  4. OpenEMR stores and displays structured clinical data
  5. CQM engine evaluates outcomes and quality indicators

This enables real-time transformation of legacy data into modern interoperable formats.

Implementation Notes

This IG includes conceptual models and profiles supporting:

  • Oncology patient lifecycle management
  • Laboratory and imaging integration workflows
  • Chemotherapy and treatment tracking
  • Structured clinical documentation in OpenEMR
  • Data transformation pipelines using Mirth Connect
  • Evidence-based oncology performance measurement

It demonstrates how interoperability standards can reduce fragmentation and improve clinical decision support in oncology environments.

Contact & Repository

  • Email: medbyTech254@gmail.com
  • GitHub: https://github.com/steve-macharia
  • Project Type: Open Demonstration / Proof of Concept

Disclaimer

This Implementation Guide is a non-production, educational and experimental system design. It is intended to demonstrate how interoperability standards (FHIR, HL7 v2), healthcare systems (OpenEMR), and integration engines (Mirth Connect) can be combined to support oncology care workflows and clinical quality measurement.

It is not an approved medical system and should not be used for clinical decision-making in production environments.