Progression Analytics System for Clinical Data Segmentation
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Solution Overview
Problem
Current healthcare systems face challenges in identifying and mitigating adverse health outcomes, as these can be latent and occur unexpectedly during medical treatment, leading to increased healthcare costs and suboptimal patient care.
Innovation Solution
A progression analytics system that extracts electronic clinical data from historical healthcare encounters to define patient groups based on similar data patterns, derive hypothesized etiological explanations for outcome differences, and identify clinical interventions to modify the likelihood and consequences of adverse or favorable outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If retrospective analysis of historical healthcare data is performed to identify adverse outcomes, then insights into patient care trajectories and etiological explanations can be derived, but the complexity of data extraction, processing and analysis increases
Solution Approach 1:
The system segments the historical healthcare data into distinct patient groups based on similar data patterns, allowing complex data to be analyzed in manageable segments. This segmentation enables the system to derive etiological explanations for specific patient groups without being overwhelmed by the complexity of analyzing all patient data simultaneously.
Solution Approach 2:
The system introduces an intermediary processing layer that extracts and structures clinical data from historical healthcare encounters before analysis. This intermediary layer includes components for data extraction, pattern recognition, and group definition, which simplify the subsequent analytical processes and reduce the overall system complexity.
2Reliability
If patient groups are defined based on similar data patterns to differentiate likelihood and consequences of outcomes, then targeted clinical interventions can be identified, but the difficulty of detecting and measuring data patterns increases
Solution Approach 1:
The system replaces manual pattern detection with automated computational methods. The progression analytics system uses algorithms to automatically detect and measure data patterns in electronic clinical data, identifying similarities among patient cases without requiring manual analysis. This substitution of mechanical/manual processes with automated systems reduces the difficulty of pattern detection while improving reliability.
Solution Approach 2:
The system creates simplified representations or copies of complex patient data patterns through structured data extraction and normalization. By creating standardized data models that replicate essential patient characteristics and outcomes, the system makes pattern detection more manageable while maintaining the reliability needed for accurate patient group classification.
3Loss of information
If comprehensive electronic clinical data is extracted from historical healthcare encounters, then systematic analysis of patient care can be performed, but the quantity of data to be processed increases
Solution Approach 1:
The system selectively extracts only the relevant clinical data elements needed for progression analysis from historical healthcare encounters. Rather than processing all available data, the extraction process identifies and removes essential information about patient care trajectories, outcomes, and relevant clinical events, reducing the quantity of data to be processed while maintaining completeness of essential information.
Solution Approach 2:
The system applies partial action by focusing analysis on specific patient groups and outcome types rather than attempting to analyze all patient data comprehensively. This selective approach processes a subset of data that is sufficient to derive meaningful etiological explanations and identify clinical interventions, reducing the overall data processing burden while maintaining analytical rigor.
Data Source
AI summary
A method of identifying insights related to the occurrence of an adverse health outcome of interest, comprises extracting electronic clinical data associated with historical healthcare encounters. The method also comprises defining patient groups based upon similar data patterns present in the extracted electronic clinical data wherein the patient groups have varying likelihood for the adverse health outcome. Still further, the method comprises deriving hypothesized etiological explanations for why one or more patient groups have higher likelihood when compared to other patient groups. Optionally, the method comprises identifying clinical interventions that are intended to reduce the likelihood of the adverse outcome for certain patient groups.