HeyDonto FHIR Data Mapper
How the HeyDonto FHIR Data Mapper Standardizes Healthcare Data
The HeyDonto FHIR Data Mapper is a self-optimizing neural system that converts EHR and PMS data into FHIR-compliant resources. Designed for extensibility, the mapper adapts to evolving data schemas using population-based model training and evolutionary tuning.
By supporting field-level mapping across multiple systems, including Dentrix, Open Dental, and Eaglesoft, the engine enables consistent use of healthcare data for scheduling, billing, reporting, analytics, and AI applications.
HeyDonto FHIR Data Mapper
How the HeyDonto FHIR Data Mapper Standardizes Healthcare Data
The HeyDonto FHIR Data Mapper is a self-optimizing neural system that converts EHR and PMS data into FHIR-compliant resources. Designed for extensibility, the mapper adapts to evolving data schemas using population-based model training and evolutionary tuning.
By supporting field-level mapping across multiple systems, including Dentrix, Open Dental, and Eaglesoft, the engine enables consistent use of healthcare data for scheduling, billing, reporting, analytics, and AI applications.
Why FHIR Mapping Matters
FHIR (Fast Healthcare Interoperability Resources) provides a consistent way to structure healthcare data. Most EHR and PMS platforms do not output FHIR directly. Their data is often fragmented, loosely structured, or not designed for integration.
The HeyDonto FHIR Data Mapper addresses this by:
Schema Extraction from Any System
Extracts schemas from fragmented or legacy EHR/PMS platforms like Dentrix, Open Dental, and Eaglesoft — even when data isn’t FHIR-native.
Field-Level FHIR Mapping
Matches each field to valid FHIR-compatible resources, ensuring structured outputs that are compliant, clean, and ready for use.
Reliable Output for Any Workflow
Delivers consistent FHIR outputs for downstream applications like analytics, billing, scheduling, and AI — no brittle scripts or manual exports needed.
Core Mapping Flow
The mapping engine operates as a pipeline of evolving models. Instead of relying on one static configuration, it continuously improves through feedback and schema detection.
Processing Flow:
Schema Parser
Understands and analyzes the structure of input data from EHR and PMS systems.
Model Population
Generates initial mapping strategies using pre-learned knowledge and schema context.
Fitness Evaluation
Evaluates mapping performance and scoring accuracy of data alignment.
Crossover & Mutation
Continuously evolves the mappings by combining and refining high-performing models.
Mapping State Repository
Stores the best-performing mapping configurations for stable, reusable output.
FHIR Resource Generation
Produces validated, FHIR-compliant data ready for integration and downstream use.
Validation Layer
Each mapped output is validated for structural consistency and semantic alignment before being passed to downstream systems.
This ensures high-quality output even when source data is inconsistent or incomplete.
Evolutionary Optimization Engine
The engine includes a self-improving loop that selects the highest-performing models and evolves them through a cycle of testing, mutation, and replacement.
This loop allows the system to adapt to:
Schema changes in EHR and PMS platforms
The mapping engine automatically adapts to structural changes across platforms like Open Dental, Dentrix, and Eaglesoft. Even when data formats evolve or fields shift, the system re-evaluates schema alignment without needing a manual update.
Real-world mapping feedback
The system continuously learns from usage patterns and feedback. It refines its mappings based on real-world inputs, improving performance where mismatches or inconsistencies are detected — making it smarter over time.
Continuous accuracy improvements
Through its evolutionary optimization loop, the engine constantly improves mapping accuracy. High-performing models are selected and evolved through testing, mutation, and replacement — resulting in more precise, reliable outputs with every generation.
RAPID LEARNING
Few-Shot Schema Adaptation
The mapper can learn from small datasets. In many cases, it can adapt to new schemas using only 5 to 10 mapped examples.
This capability is supported by:
Transfer learning across known mappings
Feedback-based refinement
Evolutionary strategies that preserve generalizability
Integration and Deployment
Once models reach production quality, they are packaged for use through:
The HeyDonto API
Direct pipeline integration
Model deployment via Hugging Face
Supported FHIR Resources
The mapper currently outputs validated FHIR R4 resources, including:
Patient
Appointment
Practitioner
Procedure
Encounter
Coverage
Claim
Condition
DocumentReference
QuestionnaireResponse
Observation
The default output follows the FHIR R4 specification. Compatibility with DSTU2 is available where required for integration with legacy systems.
System Characteristics
Neural models trained using population-based evolutionary strategies
Continuously optimized for mapping completeness, accuracy, and FHIR conformance
Capable of generalizing across domains (dental, medical, legacy, modern)
Built for real-time performance within HeyDonto’s internal API infrastructure