HeyDonto Data Mapper
How the HeyDonto Data Mapper Translates and Normalizes Healthcare Data Across Systems
The HeyDonto Data Mapper is a self-optimizing neural engine that converts fragmented healthcare data into structured, normalized outputs. It supports both structured and semi-structured data across dental and medical systems, including non-FHIR formats. Built for adaptability, the mapper evolves using population-based model training and evolutionary tuning to keep pace with changing schemas.
By translating inconsistent or undocumented inputs into clean, standardized formats, the engine enables seamless data reuse for scheduling, billing, reporting, analytics, and AI pipelines, regardless of whether the source system follows a formal data standard.
HeyDonto Data Mapper
How the HeyDonto Data Mapper Translates and Normalizes Healthcare Data Across Systems
The HeyDonto Data Mapper is a self-optimizing neural engine that converts fragmented healthcare data into structured, normalized outputs. It supports both structured and semi-structured data across dental and medical systems, including non-FHIR formats. Built for adaptability, the mapper evolves using population-based model training and evolutionary tuning to keep pace with changing schemas.
By translating inconsistent or undocumented inputs into clean, standardized formats, the engine enables seamless data reuse for scheduling, billing, reporting, analytics, and AI pipelines, regardless of whether the source system follows a formal data standard.
Normalizing Data Across Unstructured Sources
Most EHR and PMS platforms do not produce data in a consistent or reusable format. In many cases, the schema is undocumented, the structure varies by installation, or the data is fragmented across multiple tables.
The HeyDonto Data Mapper addresses this by eliminating the need for brittle mapping scripts, manual exports, or one-off connectors with
Schema Parsing Engine
Extracts structure from legacy, proprietary, or cloud-based data systems for streamlined integration.
Intelligent Field Mapping
Aligns incoming data fields with internal normalized formats for consistency and accuracy.
Clean Output Generation
Delivers standardized, downstream-ready data optimized for further processing and analytics.
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
Analyzes the structure of incoming healthcare data to detect fields, types, and relationships — even in undocumented or fragmented schemas.
Model Population
Generates initial mapping strategies using past learning and schema patterns to kick-start the normalization process.
Fitness Evaluation
Scores each mapping configuration based on how accurately and completely it aligns the input data with the desired format.
Crossover & Mutation
Combines high-performing mappings and evolves new variants to improve accuracy through intelligent iteration.
Mapping State Repository
Stores optimized mapping models that are validated and ready to be reused or deployed across different data systems.
Normalized Output Generation
Formats the final mapped data into structured, clean outputs—ready for reporting, analytics, or system integration.
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 updates from 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 is made possible by:
Transfer learning from existing mappings
Feedback-driven refinement
Evolutionary strategies that promote generalization
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
Output Format Flexibility
The HeyDonto Data Mapper supports:
Custom Schemas
Normalized JSON
Reporting Integration
Analytics Compatibility
Automation Tools
Structured & Semi-Structured Formats
FHIR Output (Optional)
When paired with our FHIR module, the Data Mapper can output fully validated FHIR R4 resources. On its own, it supports any structured or semi-structured format defined by the integration environment.
System Characteristics
Neural models trained using population-based evolutionary strategies
Continuously optimized for mapping completeness, accuracy, and schema consistency
Capable of generalizing across domains (dental, medical, legacy, modern)
Built for real-time performance within HeyDonto’s internal API infrastructure