Longitudinal Trajectory Clustering for Patient Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to effectively identify and manage complex patient cases with multiple comorbid conditions, leading to inefficient healthcare resource utilization and suboptimal patient outcomes due to neglect of time-series information and interconnections between conditions.
Innovation Solution
A system and method for characterizing patient case and care complexity by analyzing longitudinal patterns in health service utilization, using time-series analysis to cluster similar trajectories and prescribe specific interventions, reducing inappropriate healthcare use and improving disease management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventive interventions are implemented for complex patients with multiple comorbid conditions, then patient outcomes should improve, but current non-specific interventions fail to achieve desirable outcomes due to lack of personalization
Solution Approach 1:
The patent segments patients into distinct trajectory clusters based on their longitudinal patterns of comorbid conditions and healthcare utilization. By dividing the heterogeneous population of complex patients into homogeneous subgroups with similar disease trajectories, the system enables targeted interventions tailored to each cluster's specific needs, thereby improving outcome reliability while maintaining adaptability.
Solution Approach 2:
The patent changes the parameter of intervention specificity by moving from generic preventive maneuvers to cluster-specific targeted interventions. By using statistical trajectory analysis to identify distinct patient groups with different patterns of comorbid conditions, the system adjusts intervention parameters to match each cluster's unique characteristics, resolving the contradiction between reliable outcomes and personalized adaptability.
2Measurement precision
If comprehensive longitudinal analysis of multiple comorbid conditions is performed, then identification of effective preventive interventions improves, but current methods neglect time-series information and interconnections between conditions
Solution Approach 1:
The patent replaces complex manual analysis methods with automated statistical computing systems. By using computer-based trajectory analysis algorithms to process longitudinal data on multiple comorbid conditions, the system achieves high measurement precision in identifying patient patterns without requiring proportionally increased analytical complexity, as the computational system handles the complexity automatically.
Solution Approach 2:
The patent creates statistical models that copy and represent the complex interconnections between multiple comorbid conditions over time. By developing trajectory clusters that replicate the temporal patterns and relationships among comorbid conditions, the system captures comprehensive longitudinal information in a simplified, analyzable format that maintains measurement precision while reducing analysis complexity.
3Productivity
If specific trajectory-based interventions are prescribed, then healthcare resource utilization decreases, but identifying which patients receive which interventions requires complex trajectory analysis
Solution Approach 1:
The patent performs preliminary trajectory analysis to pre-identify patient clusters and their characteristic patterns of comorbid conditions before interventions are needed. By calculating trajectory clusters in advance and storing them in a database, the system enables rapid matching of patients to appropriate interventions without requiring complex real-time analysis, thereby improving healthcare efficiency while maintaining the necessary level of automation.
Solution Approach 2:
The patent introduces a statistical trajectory analysis system as an intermediary between patient data and intervention selection. This intermediary automatically processes longitudinal data, identifies trajectory clusters, and matches patients to appropriate interventions, resolving the contradiction by automating the complex analysis process and enabling efficient resource utilization through systematic, rather than manual, intervention assignment.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.