Patient Problem Data Structure for Healthcare Interoperability
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Solution Overview
Problem
Current healthcare information systems lack a standardized model for patient problems, leading to inconsistent and unambiguous data representation, which hinders interoperability and effective communication across different healthcare systems.
Innovation Solution
A patient problem data system that utilizes a standardized model with detailed attributes to facilitate consistent data collection, storage, and processing, including a focus term, likelihood term, client term, and attribute properties, enabling decomposition of patient problems into computable definitions for efficient data sharing across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different clinical terminologies and pre-coordinated free text patient problems are used in separate healthcare systems, then each system can maintain its own terminology models and definitions, but different healthcare systems cannot objectively understand or map patient problems between them
Solution Approach 1:
The patent introduces a standardized patient problem data structure as an intermediary model that translates between different clinical terminologies. This structure includes standardized attributes (focus term, patient problem likelihood term, client term) and attribute properties (format, content) that act as a common language, enabling objective mapping and understanding of patient problems across different healthcare systems without losing essential information.
Solution Approach 2:
The patent segments patient problems into discrete, structured components including focus term, patient problem likelihood term, client term, and multiple attribute properties. This segmentation transforms unstructured free text into computable, standardized elements that can be systematically mapped between different terminologies, resolving the interoperability issue while preserving terminology flexibility.
2Device complexity
If simple text expression of patient problems is used, then the system is easy to implement, but it is insufficient to support optimizing clinical practice and managing clinical outcomes
Solution Approach 1:
The patent transforms the parameter representation of patient problems from simple text to a structured model with multiple parameters including focus term, patient problem likelihood term, client term, format attributes, and content attributes. This parameter expansion enables precise measurement and computation of patient problems while maintaining systematic organization that supports both clinical optimization and outcome management.
Solution Approach 2:
The patent replaces the mechanical system of simple text storage with a computational model-based system that uses standardized attributes and properties. This substitution enables automated processing, decomposition into computable definitions, and secondary data use based on specific problem characteristics, while the standardized structure maintains implementation efficiency.
3Ease of manufacture
If Healthcare Information Technology systems do not use model-based implementation, then system development is straightforward, but they cannot decompose problems into consistent, unambiguous, and computable definitions
Solution Approach 1:
The patent creates a universal patient problem data structure model that serves multiple functions: it standardizes problem representation, enables decomposition into computable definitions, supports secondary data use, and facilitates interoperability. This single model-based implementation achieves all these goals simultaneously without complicating system development, as the standardized structure provides a clear framework for implementation.
Data Source
AI summary
A patient problem data system stores data representing a plurality of different patient problems for use in providing healthcare to a patient. An acquisition processor acquires data representing a patient problem for storage in a repository. A repository, electrically coupled to the acquisition processor, includes data representing a plurality of different patient problems; an individual patient problem has a patient problem name and is characterized by patient problem attributes; an individual patient problem has a plurality of attribute properties determining how a patient problem attribute is represented. Patient problem attributes include a focus term indicating a topic of a patient problem, a patient problem likelihood term indicating an assessment of likelihood of the associated corresponding patient problem, and a client term indicating at least one target person for care. The attribute properties include a format attribute property indicating a format constraint of a patient problem attribute and a content attribute property indicating a content constraint of a patient problem attribute. A retrieval processor, electrically coupled to the repository, retrieves data representing at least one patient problem from the repository.