Automated Patient Data Extraction for Medical Records
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Solution Overview
Problem
Current systems for storing and organizing patient data often rely on unguided human interpretation, leading to errors and inconsistencies in determining accurate data, which complicates treatment decisions and prognosis predictions.
Innovation Solution
A method and system that access patient data records, extract candidate facts, categorize them based on elements, and apply reduction rules to identify the best facts for each element, providing accurate and succinct data for treatment options and prognosis predictions, with user verification and graphical interfaces for acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human interpretation is used to determine accurate patient data, then flexibility in data selection is maintained, but errors and inconsistencies increase
Solution Approach 1:
The patent replaces manual human interpretation with an automated computer system that processes patient data. The system automatically extracts candidate facts from medical records, categorizes them into elements, applies reduction rules to select best facts, and generates accurate patient data outputs, eliminating human error while maintaining processing flexibility
Solution Approach 2:
The system transforms unstructured medical record data into structured patient data by changing the state of information through automated processing. The system processes data according to defined parameters and reduction rules, converting raw medical records into standardized accurate data representations
2Loss of information
If all patient data is stored for future analysis, then comprehensive data availability is improved, but data management complexity increases
Solution Approach 1:
The patent extracts only the necessary accurate patient data from comprehensive medical records through automated fact extraction and reduction processes. The system identifies and stores only the best facts that are essential for treatment decisions and prognosis, rather than storing all raw data, thereby reducing management complexity while maintaining information availability
Solution Approach 2:
The system segments patient data into distinct elements and categories through automated classification. By organizing data into structured elements with specific meanings, the system makes data management more manageable while preserving comprehensive information for future analysis
3Adaptability or versatility
If different standards are applied to data from different patients, then data relevance to individual patients is improved, but consistency and comparability decrease
Solution Approach 1:
The patent applies local quality by customizing data extraction and processing according to each patient's specific medical condition and treatment needs. The system adapts its processing rules to the individual patient context while maintaining overall consistency through standardized reduction rules and automated processing frameworks
Data Source
AI summary
Described herein is a system, method, and non-transitory computer-readable medium, to provide accurate patient data corresponding with diagnosis and/or progression milestones for a patient with a medical condition and/or illness. Also described herein are methods and systems for providing a graphical user interface including an interactive patient information timeline.