Patient Pathway Graph Aggregation for Clinical Outcome Analysis
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Solution Overview
Problem
Clinical guidelines lack insights into real-world patient outcomes and do not provide metrics for comparing treatment options, leading to difficulties in selecting the best treatment regime for individual patients and identifying inefficiencies in clinical care and resource management.
Innovation Solution
A patient pathway reconstruction and aggregation system that generates graphs from medical history data, allowing for the selection and merging of patient pathways based on criteria such as common treatment events and patient cohorts, and provides analytics to visualize trends and metrics like survival rates and costs, enabling clinicians to improve treatment decisions and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If clinical guidelines are used to guide treatment decisions, then treatment options and possible outcomes are provided, but real-world patient outcomes and insights into how to improve patient care are not provided
Solution Approach 1:
The patent merges multiple patient pathway graphs into a consolidated merged graph that integrates real-world patient outcome data with clinical guideline information. This combining of data sources provides both guideline-based treatment options and actual patient outcomes, resolving the information gap without complicating the decision-making process
Solution Approach 2:
The system introduces an intermediary processing layer that retrieves, reconstructs, and analyzes patient pathway data from multiple sources, then presents this information in a unified format. This intermediary layer bridges the gap between raw clinical data and actionable treatment decisions, providing real-world outcomes without directly complicating the clinician's decision process
2Reliability
If multiple patient pathways are analyzed to provide comprehensive insights, then real-world outcomes and treatment comparisons are available, but system complexity increases
Solution Approach 1:
The system segments the complex task of pathway analysis into distinct modules: individual pathway graph construction from patient data, pathway merging logic, and analytics generation. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while providing comprehensive treatment comparisons
Solution Approach 2:
The pathway graph structure serves multiple functions simultaneously: it stores individual patient treatment sequences, enables comparison across patient populations, provides statistical analytics, and supports both clinical and operational decision-making. This multi-functionality reduces the need for separate systems for each analytical task
3Loss of information
If detailed medical history data is processed to reconstruct patient pathways, then comprehensive treatment and outcome information is obtained, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing medical history data during ingestion, organizing it into standardized pathway graph structures. This preliminary organization reduces the computational burden during query and analysis operations, enabling fast retrieval and comparison of patient pathways without sacrificing data completeness
Data Source
AI summary
Techniques for patient data management include obtaining medical history data of patients from a database, the medical history data including a history of one or more diagnosis events, one or more treatment events, and a clinical outcome event for each patient of the patients. For each patient, based on the medical history data, a computing system generates a patient pathway that includes a graph including nodes of one or more diagnosis events, the one or more treatment events, and the clinical outcome event. The computing system receives, via an interface, one or more criteria to select a subset of the patient pathways of the patients. The computing system selects, based on the one or more criteria, graphs representing the subset of the patient pathways. The computing system aggregates the subset of the patient pathways into a merged graph. The computing system displays the merged graph in the interface.