Informatics System for Rapid Prognosis via Data Standardization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating evidence-based medical prognosis and treatment recommendations from electronic health records are time-consuming and manual, involving multiple steps and complications when dealing with diverse data sources and standards, making rapid informatics-based prognosis and treatment development challenging.
Innovation Solution
A system and method that utilize a cohort engine, interpreter, and analytics engine to select and process patient data from multiple sources, apply a knowledge graph for standardization, and generate consult outputs based on completed study templates, including timeframe, phenotype, and demographic fields, to provide rapid informatics-based prognosis development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual retrospective observational studies are performed to generate evidence-based prognosis and treatment recommendations, then the analysis can be rigorous and comprehensive, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing patient data from multiple sources into a unified format before studies are requested. Data is extracted, transformed, and loaded into a standardized schema in advance, so when a consult request arrives, the heavy lifting of data preparation is already complete, enabling rapid analysis without sacrificing rigor
Solution Approach 2:
The patent introduces an intermediary layer (the informatics system with standardized data models and pre-built analytical frameworks) between the raw EHR data and the research questions. This intermediary automatically maps diverse data sources to a common schema and applies standardized analytical methods, maintaining rigor while eliminating manual data wrangling time
2Adaptability or versatility
If data is extracted from multiple sources with differing standards and formats to ensure comprehensive analysis, then the study can be more thorough, but the process becomes more complicated and time-consuming
Solution Approach 1:
The system implements a universal data schema that can accommodate multiple data sources with differing standards and formats. The standardized model serves as a multi-functional framework that automatically adapts to various input formats (laboratory data, imaging data, clinical notes) while presenting a unified structure for analysis, reducing process complexity despite handling diverse sources
Solution Approach 2:
The patent introduces an intermediary layer (the informatics system with standardized data models and pre-built analytical frameworks) between the raw EHR data and the research questions. This intermediary automatically maps diverse data sources to a common schema and applies standardized analytical methods, maintaining rigor while eliminating manual data wrangling time
3Measurement precision
If manual phenotyping and analysis steps are performed to characterize patient clinical characteristics accurately, then the prognosis and treatment recommendations can be more precise, but the process takes several weeks to complete
Solution Approach 1:
The system implements self-service by automatically performing phenotyping and patient characterization using pre-configured algorithms and data models. The informatics system autonomously extracts relevant clinical characteristics, applies appropriate analytical methods, and generates results without requiring manual intervention for each step, maintaining precision while dramatically increasing productivity
Solution Approach 2:
The patent changes the parameters of the analysis process by transforming it from a manual, step-by-step procedure to an automated computational process. This involves changing the operational parameters (time, human involvement) while maintaining the analytical rigor through standardized algorithms and validated data models
Data Source
AI summary
In certain aspects of the present disclosure, a method for rapid informatics-based prognosis development includes receiving a consult request. The method includes selecting a study template configured to design a study to develop a consult output in response to the consult request. The method includes receiving a completed study template, including a completed field in the study template based on information from the consult request, the completed field comprising a variable provided in at least one of a timeframe field, a phenotype field, a cohort field, and a demographic field. The method includes obtaining cohort data based on the completed study template, the cohort data derived from a plurality of patient objects. The method includes generating the consult output based on the cohort data, the consult output comprising a result from an analysis of the cohort data according to a criteria and an instruction in the study template.