Clinical Sentiment Search Engine for Automated Patient Data Analysis
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Solution Overview
Problem
Current information retrieval systems, such as search engines, face challenges in providing direct and efficient responses to queries regarding clinical patient data, requiring users to manually analyze search results to determine relevant information, which is time-consuming and tedious, especially when seeking aggregated data on drug effectiveness or disease prevalence.
Innovation Solution
A system and method for retrieving clinical information using a federated 'clinical sentiment' search engine that employs AI-based analysis of both structured and unstructured data, incorporating machine learning models and fragment-based searching to provide quantitative and statistically sound answers, similar to internet-style information retrieval tools but specialized for clinical and pharmacological applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search engines are used to retrieve clinical information, then the system is simple and easy to operate, but the search results require manual analysis and do not provide direct quantitative answers
Solution Approach 1:
The patent introduces an intermediary component (clinical information retrieval system with AI analysis module) between the search engine and the user. This intermediary automatically analyzes search results, extracts quantitative clinical information, and presents synthesized answers, eliminating the need for manual analysis while maintaining simple search operations.
Solution Approach 2:
The system enables self-service by allowing the search engine to automatically perform analysis and extraction of clinical information without user intervention. The AI module autonomously processes search results, identifies relevant data patterns, and generates quantitative answers, making the system serve itself rather than requiring manual user analysis.
2Measurement precision
If manual analysis of search results is performed to determine clinical information, then comprehensive analysis can be achieved, but the process is time-consuming and tedious
Solution Approach 1:
The patent replaces the mechanical process of manual analysis with an automated AI-based analysis system. Machine learning models and natural language processing algorithms substitute human cognitive efforts, automatically extracting and analyzing clinical information from search results with high accuracy and speed, eliminating the time-consuming manual process while maintaining or improving precision.
3Loss of information
If aggregated data on drug effectiveness or disease prevalence is sought, then valuable clinical insights can be obtained, but the manual processing required is extremely time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-analyzing clinical data before user queries are submitted. The AI module pre-extracts relevant clinical information, pre-computes statistical metrics, and pre-organizes data structures, so that when aggregated data is requested, the system can quickly retrieve and present results without performing time-consuming analysis at query time.
Solution Approach 2:
The patent applies parameter changes by transforming unstructured clinical text data into structured quantitative parameters suitable for aggregation and statistical analysis. The system converts narrative medical records into standardized data fields with defined parameters, enabling efficient computation of drug effectiveness metrics and disease prevalence statistics while maintaining complete clinical information.
Data Source
AI summary
Disclosed systems, methods, and computer readable media can retrieve clinical information based on clinical patient data. For example, a method for retrieving clinical information based on clinical patient data includes receiving a specification of a patient cohort, receiving a query, retrieving a list of search results based on the query and the specification of the patient cohort, computing one or more inferences for each item in the list of search results, providing an aggregate statistical analysis associated with the one or more inferences, and providing, by the one or more hardware processors, a response to the query that includes the aggregate statistical analysis. Each element in the list of search results comprises at least a portion of a clinical data record associated with a patient in the patient cohort.