Clinical Guideline Conversion to Ripple Down Rule Model
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Solution Overview
Problem
Clinical practice guidelines in text format create a gap between research results and actual clinical practice, making it difficult for healthcare providers to remember and apply relevant information in a timely manner, leading to potential errors in model generation due to the reliance on medical experts' knowledge.
Innovation Solution
A method and system that convert clinical practice guidelines into a computer-interpretable model by assigning tags to sentences, identifying condition and result phrases using a knowledge database, and generating a ripple down rule model for improved decision-making and knowledge sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical practice guidelines are converted into computer-interpretable formats by medical experts, then the accuracy of model generation is improved, but the time consumption and complexity of the conversion process increase
Solution Approach 1:
A natural language processing system is introduced as an intermediary between the clinical practice guidelines and the computer-interpretable model. The system automatically processes the text guidelines through NLP techniques to generate structured data, eliminating the need for manual conversion by medical experts while maintaining accuracy through automated semantic analysis and knowledge base integration
Solution Approach 2:
The manual mechanical process of expert conversion is replaced with an automated digital system. The NLP system uses computational algorithms to parse, interpret, and transform the text-based guidelines into structured computer-interpretable formats, substituting human expertise with automated technological processes
2Loss of information
If clinical practice guidelines are provided in text format, then the completeness of information is preserved, but the accessibility and usability by healthcare providers deteriorates
Solution Approach 1:
The text-based clinical practice guidelines are segmented into discrete, structured units through automated processing. The NLP system divides the continuous text into identifiable concepts, conditions, and recommendations, transforming them into structured data formats that are easier to access, search, and apply while preserving the complete information content
Solution Approach 2:
The format and structure of the guidelines are changed from unstructured text to structured computer-interpretable data. The system transforms the information representation parameters, converting narrative text into organized data structures that maintain information completeness while significantly improving accessibility and usability for healthcare providers
Data Source
AI summary
A method of converting a clinical practice guideline into a computer-interpretable model includes assigning at least one tag of a recommendation tag and a non-recommendation tag to each of a plurality of sentences extracted by analyzing the clinical practice guideline, filtering out a sentence to which the non-recommendation tag is assigned among the plurality of sentences and extracting a sentence to which the recommendation tag is assigned, identifying whether a phrase of a first element and a phrase of a second element exist in the sentence to which the recommendation tag is assigned based on a knowledge database and mapping and storing the phrase of the first element and the phrase of the second element, and generating a final model by converting the phrase of the first element and the phrase of the second element into a format corresponding to a specific model.