Healthcare Supply Chain Data Harmonization for Product ID Matching
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Solution Overview
Problem
Healthcare supply chains face inefficiencies due to inconsistent product and organizational data across different technology systems, leading to overstocking, understocking, delayed responses, transactional errors, and miscommunication, which hinder timely decision-making and optimal resource allocation.
Innovation Solution
An enhanced healthcare supply chain management system utilizing machine learning and artificial intelligence to enrich, harmonize, and align product information across multiple enterprises, enabling real-time data integration, advanced matching analytics, and strategic collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes and ad-hoc solutions are used to manage product data, then organizations can maintain flexibility in their internal systems, but data integration efficiency deteriorates and errors increase
Solution Approach 1:
The patent introduces a standardized data framework acting as an intermediary layer between disparate internal systems and supply chain partners. This framework includes standardized data elements, attributes, and translation rules that enable automatic data harmonization without requiring organizations to change their internal systems, thus maintaining flexibility while improving integration efficiency
Solution Approach 2:
The system transforms product data by changing its parameters through automated translation and harmonization processes. Disparate data from different sources is converted into standardized formats using predefined mapping rules, enabling efficient data integration across the supply chain while organizations retain their original data structures internally
2Adaptability or versatility
If disparate technology systems with different standards are used across organizations, then each organization can optimize its own operations, but translation and harmonization capability deteriorates
Solution Approach 1:
The standardized data framework provides universal data elements and attributes that can represent products across all organizations in the supply chain. The framework includes comprehensive product attributes that can capture diverse product information from different sources while maintaining a common structure, enabling translation between disparate systems without losing information
Solution Approach 2:
The system incorporates feedback mechanisms where translation rules and mapping relationships are continuously refined based on data quality metrics and harmonization success rates. This allows the framework to adapt to organizational-specific requirements while maintaining overall standardization, preserving translation capability across diverse systems
3Reliability
If data is siloed within each organization, then organizational data security and control are maintained, but comprehensive supply chain visibility deteriorates
Solution Approach 1:
The framework segments supply chain data into standardized organizational-level data elements that can be securely shared while maintaining organizational boundaries. Each organization's data is structured into standardized product attributes and organizational attributes that can be exchanged through the framework without compromising security, enabling comprehensive visibility while preserving control
Solution Approach 2:
The standardized data framework serves as a secure intermediary that facilitates controlled data exchange between siloed organizational systems. The framework's standardized structure enables automatic data harmonization and translation while maintaining security protocols, allowing comprehensive supply chain visibility without requiring organizations to expose their internal systems
4Productivity
If automated systems with AI and machine learning are implemented, then data harmonization efficiency is improved, but system complexity increases
Solution Approach 1:
The system incorporates AI and machine learning capabilities that enable automated data harmonization without requiring complex manual configuration. The AI models automatically learn mapping relationships between disparate data sources and standardized attributes, performing self-service data translation and reducing the need for complex system integration logic
Solution Approach 2:
The patent replaces complex mechanical data integration systems with AI-driven automated processes. Instead of relying on intricate mapping rules and manual data transformation logic, the system uses machine learning models to automatically harmonize data across diverse sources, reducing system complexity while improving harmonization efficiency
Data Source
AI summary
A system for managing healthcare supply chain product and organization information for an organization is provided. The system may include a processor and a memory in communication with the processor. The memory includes a user interface module to receive a product identifier (ID), and an integration module to generate a new product item when the integration module determines that the product ID is not associated with an existing product item, or integrate the product ID with the existing product item when the existing product item does not include the product ID. The memory includes an organization module that receives an organization identifier (ID) and matches the organization ID and organization account to an organization master. The memory includes a PIM module that receives the product item associated with the product ID from the integration module. The PIM module matches the product ID and product item to a system PIM.