Automated API Specification Mapping for Heterogeneous Health Systems
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Solution Overview
Problem
Current systems require significant time and effort for medical institutions to redevelop API programs to align with a standard API specification for health data distribution, hindering the widespread adoption of My Health Way services and limiting access to health information across heterogeneous systems.
Innovation Solution
A method and system that automates the standard API specification by using artificial intelligence to map API parameters of different hospitals to a standard, enabling the conversion and relay of existing APIs without additional development, through a cloud server's API definition unit, data collection, term analysis, and decision processing modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical institutions redevelop API programs to align with standard API specification, then data distribution compatibility is improved, but time and effort investment increases significantly
Solution Approach 1:
The patent introduces an API specification conversion service as an intermediary between heterogeneous hospital APIs and the standard API specification. This service automatically converts existing hospital APIs to the standard specification, eliminating the need for hospitals to redevelop their APIs while ensuring full compatibility with the standard. The intermediary handles the transformation work, allowing hospitals to maintain their existing systems while still achieving standard compliance.
Solution Approach 2:
The conversion service enables hospitals to self-convert their existing APIs to the standard specification without requiring manual intervention or redevelopment. The system automatically detects hospital API specifications, generates conversion mappings, and produces standardized API interfaces, allowing hospitals to serve their own needs for standard compliance without external assistance or time investment in redevelopment.
2Measurement precision
If manual mapping of API parameters is performed, then mapping accuracy is improved, but processing time and complexity increases
Solution Approach 1:
The patent replaces manual mechanical mapping processes with an automated AI-based system. The machine learning model automatically analyzes hospital API specifications, identifies parameter relationships, and generates accurate mappings to the standard specification. This substitution eliminates human labor while maintaining or improving mapping accuracy through sophisticated algorithms that can handle complex parameter relationships more effectively than manual processes.
Solution Approach 2:
The system changes the approach to parameter mapping from manual analysis to automated AI-driven processing. By using machine learning models that have been trained on API specification patterns, the system can automatically detect and map parameters with high accuracy, transforming the complex manual parameter analysis into an automated process that scales efficiently without increasing complexity.
3Adaptability or versatility
If existing APIs are converted to standard specification, then system compatibility is improved, but conversion process complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the standard API specification and pre-training machine learning models on API mapping patterns. Before actual conversion is needed, the system prepares conversion rules, data structures, and processing pipelines. This preliminary preparation simplifies the actual conversion process, as the heavy lifting of model training and rule generation is completed in advance, leaving only straightforward execution during conversion.
Solution Approach 2:
The conversion service acts as an intermediary that absorbs the complexity of heterogeneous API specifications and translates them into a unified standard. This intermediary layer isolates the complexity from the standard API interface, allowing the standard to remain simple and clean while the conversion service handles all the nuanced transformations required for different hospital systems. The complexity is contained within the intermediary rather than propagating to the standard interface.
Data Source
AI summary
A method for automating a standard API specification for data distribution between heterogeneous systems. The method includes steps of learning a standard API and automating a standard API specification. The learning a standard API comprises the steps: defining a standard API specification, standard terms, and parameters; requesting, by a cloud server, a hospital API specification from a hospital API server and receives a response of the hospital API specification from the hospital API server; receiving hospital and institutional API specifications and collects terms; analyzing the collected terms; mapping the analyzed terms; and mapping API parameters.