Interactive Patient Trajectory Visualization for Multi-Organ Risk Prediction

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Solution Overview

Problem

Existing clinical practices struggle to translate population-level risk factors for rare autoimmune diseases like scleroderma into personalized patient-level predictions, making it difficult to inform targeted screening or early intervention, and there is a lack of tools to effectively aggregate and visualize complex, longitudinal patient data across multiple organ systems.

Innovation Solution

An interactive patient-level data visualization and analysis tool integrates data from electronic medical records and research databases to plot a patient's health trajectory, overlaying it with a user-defined disease cohort for comparison, using filters to compare the patient to a subgroup with similar characteristics, and incorporating personalized risk estimates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If physicians use cognitive skills to integrate information across multiple parameters and organ systems to make personalized risk estimates, then prediction accuracy improves, but time investment and complexity increase tremendously

Engineering Contradiction:

Improverisk prediction accuracyVSAvoidtime investment

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system segments the complex task of risk prediction into modular components: data collection from multiple sources, longitudinal trajectory analysis across organ systems, risk factor identification, and visualization. This segmentation allows automated processing of each component, reducing physician time investment while maintaining comprehensive analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

An automated analytics platform acts as an intermediary between raw clinical data and physician decision-making. The platform integrates data from electronic medical records and research databases, performs complex longitudinal analyses, and presents results through visualizations, thereby reducing the time physicians spend on data aggregation and analysis while improving prediction accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If physicians aggregate complex longitudinal data for clinical use, then personalized care improves, but the process requires tremendous time investment

Engineering Contradiction:

Improvedata aggregation completenessVSAvoidtime investment

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically collecting, organizing, and analyzing longitudinal data from multiple sources before physician review. Data aggregation, cleaning, and initial analysis are completed in advance, allowing physicians to review pre-processed information rather than manually aggregating raw data, thus reducing time investment while maintaining data completeness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates simplified copies or representations of complex longitudinal data through visualizations and summary statistics. Instead of requiring physicians to analyze raw longitudinal datasets, the system generates visual copies that preserve essential information while reducing complexity, enabling efficient review without sacrificing data completeness.

Inventive Principle:

Principle #26Copying

3Quantity of substance

If population-level risk factors are used for screening, then general risk assessment is possible, but translation to patient-level personalized predictions remains difficult

Engineering Contradiction:

Improvedata coverageVSAvoidpatient-level prediction accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The system applies local quality by tailoring risk predictions to individual patients while incorporating population-level data. The analytics platform analyzes each patient's specific longitudinal trajectory, organ system involvement, and risk factor profile to generate personalized predictions, rather than applying uniform population-level risk factors to all patients, thereby achieving both broad data coverage and patient-level precision.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250253050A1Interactive tool to improve risk prediction and clinical care for a disease that affects multiple organs

Publication Date: 2025.08.07 JOHNS HOPKINS UNIVERSITY

AI summary

A method, a system, and a non-transitory computer-readable medium provides an interactive patient-level data visualization and analysis tool that illustrates a patient's health trajectory across multiple organ systems. Data from an electronic medical record system and one or more research databases are integrated into an analytics platform. A visualization tool plots the patient's health trajectory and overlays data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user.