Patient Timeline Generation for Clinical Data Analysis
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Solution Overview
Problem
The current healthcare informatics systems face challenges in generating succinct and uniform patient information, making it difficult to compare and identify similar patients, detect patterns, and assess risks due to data heterogeneity across different healthcare silos, leading to increased costs and time in clinical drug development and analytics.
Innovation Solution
The method involves extracting medical events and associated timestamps from disparate data sources, using natural language processing and machine learning to create timeline data structures that can be used to generate visual timelines for intuitive clinical trajectory analysis, and identify inconsistencies, allowing for the comparison and identification of similar patient cohorts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data manipulation is performed to compare subjects and identify patterns, then data analysis accuracy is improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical data manipulation with automated computational systems. Natural language processing algorithms automatically extract and structure medical events from unstructured text, while machine learning models perform pattern recognition and cohort identification, eliminating the need for manual data processing while maintaining or improving analysis accuracy.
Solution Approach 2:
The patent introduces an intermediary layer of structured data representation between raw medical records and analysis tools. By creating standardized schemas for medical events, timestamps, and patient cohorts, the system enables automated processing while preserving the semantic meaning and analytical value of the original data.
2Loss of information
If data from multiple healthcare silos are integrated, then comprehensive patient information is achieved, but data heterogeneity and system complexity increase
Solution Approach 1:
The patent creates a universal data schema that can represent multiple types of medical events (diagnoses, procedures, medications, lab results) from different healthcare systems using a common structure. This multi-functional schema enables integration of heterogeneous data sources without requiring separate processing pipelines for each data type or source.
Solution Approach 2:
The patent transforms unstructured and semi-structured data from various healthcare systems into a standardized parameterized format. By converting diverse data types into consistent schemas with defined parameters (event type, timestamp, patient ID, clinical context), the system reduces complexity while maintaining information completeness.
3Loss of information
If extensive manual analysis is performed on patient data, then detailed insights are obtained, but resource requirements and operational costs increase
Solution Approach 1:
The patent replaces manual analytical processes with automated machine learning models that can process large volumes of patient data rapidly. These models perform pattern recognition, risk stratification, and cohort identification tasks that would require extensive manual analysis, thereby maintaining insight quality while dramatically improving operational efficiency.
Solution Approach 2:
The patent performs preliminary data processing and structuring automatically before analysis is needed. By pre-extracting medical events, pre-structuring patient timelines, and pre-identifying potential cohorts, the system reduces the computational and human resources required for subsequent detailed analysis while preserving insight quality.
Data Source
AI summary
Techniques disclosed herein relate to generating and applying subject event timelines for various purposes. In various embodiments, data indicative of medical events associated with a subject may be retrieved from various data sources. Each of the medical events may be associated with a respective timestamp. Based on the medical events and associated timestamps, a timeline data structure associated with the subject may be assembled. The timeline data structure may be analyzed to identify inconsistenc(ies) between medical events associated with the subject. A visual timeline indicative of the medical events may be rendered on a display based on the timeline data structure. Each respective medical event may be represented in the visual timeline by a graphical element. Two or more of the graphical elements that are associated with the medical events for which the inconsistenc(ies) were identified may be visually distinguished.