Patient Trajectory Clustering for Comorbidity Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current healthcare systems struggle to effectively manage patients with multiple comorbid conditions, leading to inefficient use of health resources and suboptimal patient outcomes. Existing preventive interventions are non-specific and fail to account for the interconnections between body systems and concomitant conditions.
Innovation Solution
The development of systems, methods, and computer-readable media for characterizing patient case complexity and care complexity, and for detecting matches between individual patient records and collections of other patients' records based on serial, longitudinal patterns (time series "trajectories"). This involves electronically representing health services utilization, calculating time-series K-nearest-neighbor clusters, and prescribing specific interventions relevant to reducing hazards associated with particular trajectory clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventive interventions are provided to patients with comorbid conditions, then patient outcomes may improve, but the interventions are non-specific and do not account for interconnections between body systems and concomitant conditions
Solution Approach 1:
The patent segments patients into distinct trajectory clusters based on their patterns of comorbid conditions and healthcare utilization. By dividing the heterogeneous patient population into homogeneous subgroups with similar condition sequences and utilization patterns, the system enables tailored interventions for each segment rather than applying uniform non-specific preventive measures to all patients.
Solution Approach 2:
The patent applies local quality by providing context-specific interventions matched to each patient's identified trajectory cluster. Instead of generic preventive care, the system delivers targeted case-management and care-coordination programs that address the specific interconnections between body systems and concomitant conditions characteristic of each trajectory group, thereby improving intervention adaptability and effectiveness.
2Adaptability or versatility
If comprehensive analysis of serial conditions and healthcare utilization patterns is performed, then context-specific interventions can be identified, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent creates simplified representations (copies) of complex patient data by generating trajectory cluster profiles that capture essential patterns of comorbid conditions and healthcare utilization. These cluster profiles serve as manageable models that preserve the key characteristics of each patient group without requiring processing of the full complexity of individual patient histories, thereby reducing analytical complexity while maintaining intervention targeting capability.
3Productivity
If trajectory clustering and pattern recognition are implemented, then healthcare resource utilization can be optimized, but the computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing trajectory cluster assignments for patients based on their historical data. By conducting the computationally intensive clustering analysis in advance and maintaining pre-computed patient-to-cluster mappings, the system enables rapid deployment of context-specific interventions without requiring real-time complex calculations, thereby optimizing healthcare resource allocation while minimizing processing delays.
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.