Clinical Trajectory Monitoring With Predictive Risk Tail Visualization
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Solution Overview
Problem
Existing systems struggle to efficiently analyze patient data for predicting future medical states, making it difficult to assess and communicate the trajectory of patient status effectively.
Innovation Solution
A system and method for monitoring event trajectories using processors and computer-readable storage devices to calculate and display a trajectory representative of patient or system status, incorporating a CoMET display that includes a tail representing past values and current status, with predictive models to identify imminent events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional signal-based monitoring systems are used to collect patient vitals and information, then data collection is achieved, but the analysis of such data for predicting future patient state becomes difficult and time-consuming
Solution Approach 1:
The system performs preliminary actions by continuously calculating trajectories and predictive risk scores in advance, rather than waiting for clinicians to manually analyze data when needed. The trajectory calculations and event risk assessments are computed continuously in the background, preparing predictive information before it is required for clinical decision-making.
Solution Approach 2:
The system introduces an intermediary computational layer that automatically processes raw monitoring data and transforms it into meaningful trajectory visualizations and predictive risk scores. This intermediary system bridges the gap between raw data collection and clinical interpretation, eliminating the need for manual analysis while preserving predictive information.
2Loss of information
If detailed patient monitoring data is collected and stored, then comprehensive information is available, but the complexity of analyzing and communicating patient trajectory increases
Solution Approach 1:
The system extracts only the most critical aspects of patient status by calculating specific trajectory metrics and risk scores from the comprehensive monitoring data. Instead of presenting all raw data, the system extracts and displays key trajectory information such as direction, magnitude of change, and predictive risk scores, simplifying the information while maintaining essential predictive value.
Solution Approach 2:
The system uses visual encoding through color changes in the trajectory display to communicate patient status and risk levels intuitively. Different colors represent different risk categories and trajectory directions, allowing clinicians to quickly assess patient status without complex analysis, thereby reducing perceived complexity while maintaining comprehensive information delivery.
3Measurement precision
If manual analysis of patient data is performed to predict future state, then detailed assessment is possible, but the process becomes time-consuming and difficult to communicate
Solution Approach 1:
The system replaces the manual mechanical process of clinical analysis with automated computational algorithms. The trajectory calculation engine and predictive modeling systems automatically perform the analysis that would otherwise require manual clinical assessment, maintaining measurement precision through sophisticated algorithms while eliminating the time requirement for manual data processing and interpretation.
4Reliability
If comprehensive patient monitoring is implemented, then accurate prediction of future state is possible, but the ease of operation and communication of results decreases
Solution Approach 1:
The system enhances ease of operation and communication by implementing color-coded trajectory displays and risk indicators. The visual interface uses color changes to represent different risk levels and trajectory directions, making complex predictive information immediately comprehensible and easy to communicate among healthcare team members, thereby maintaining high prediction reliability while improving operational ease.
Solution Approach 2:
The system adds visual dimensions to the data presentation by displaying trajectories as graphical paths with direction vectors and risk scores. This dimensional transformation converts complex multi-parameter monitoring data into an intuitive visual format that is easier to operate with and communicate, while the underlying computational models maintain comprehensive and reliable prediction capabilities.
Data Source
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AI summary
The invention relates to a system for monitoring clinical trajectories, the system comprising one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors. The program instructions comprise first program instructions to receive data indicative of a current medical status of a patient, the current medical status represented by a current value, which is a measured or derived value from patient monitoring data and is represented by a (x, y) pair in a graph format, wherein the ordinate is based on a risk estimate using a first prediction algorithm and the abscissa is based on a risk estimate using a second prediction algorithm, second program instructions to retrieve data indicative of a previous medical status of the patient from patient monitoring data, third program instructions to calculate, based on the current medical status and the previous medical status, a trajectory representative of the patient's medical status, wherein the trajectory is represented by a tail attached to the current value in the graph format, the current value and the trajectory appearing in one or more zones reflecting a probability of an imminent clinical event, wherein the tail represents prior values measured or derived from patient monitoring data and the rate of change, the rate of change being reflected in the length of the tail, wherein the trajectory includes a magnitude and a direction, wherein the direction is given by aligning the current value with a weighted average of prior values or derived from fits through past data points, wherein the magnitude of the trajectory is based on differences in the data indicative of the previous medical status and is calculated from a difference between recent data points, and fourth program instructions to communicate the trajectory of the patient's medical status.