Patient Risk Assessment Function Using Binned Medical Data
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Solution Overview
Problem
Current hospital systems lack a reliable and sensitive method for assessing patient risk, leading to inadequate detection of deteriorating health conditions, which can result in avoidable medical crises and increased mortality due to the limitations of existing Early Warning Systems that focus on limited physiological parameters.
Innovation Solution
A method is developed to create a dataset representing patient medical data, binning and computing average values, and deriving functions to assess risk by subtracting minimum average values, allowing for the generation of Health Scores that combine various medical data types to provide a comprehensive risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current Early Warning Systems use a small number of physiological parameters (pulse, blood pressure, temperature, respiratory rate), then the system is simple to operate, but the measurement precision and sensitivity to general health conditions are insufficient
Solution Approach 1:
The patent segments the risk assessment by dividing patients into different risk strata (low, intermediate, high risk) based on the derived risk score. This segmentation allows the system to process complex multi-parameter data while providing simplified, actionable risk categories that maintain operational simplicity.
Solution Approach 2:
The derived risk assessment function is designed to be universal and can be applied across multiple disease types and patient populations. The single risk score integrates multiple physiological parameters into one comprehensive metric that works across different clinical contexts, eliminating the need for disease-specific assessment tools.
2Reliability
If existing systems trigger medical emergency teams based on single parameter changes, then the response is quick and direct, but the overall reliability of crisis detection is reduced because catastrophic deterioration is often preceded by multiple subtle changes
Solution Approach 1:
The patent merges multiple physiological parameters into a single comprehensive risk score through the derived function. By combining information from pulse, blood pressure, temperature, respiratory rate, and other parameters into one integrated assessment, the system achieves more reliable crisis detection while maintaining operational simplicity through the unified score.
Solution Approach 2:
The system provides continuous feedback through the risk score that reflects the patient's current state and trends. The derived function processes ongoing physiological data and updates the risk assessment, enabling dynamic monitoring and early detection of deterioration patterns before they become critical events.
3Measurement precision
If comprehensive medical data is collected and analyzed to improve risk assessment sensitivity, then the measurement precision improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent transforms multiple physiological parameters into a single risk score through a derived mathematical function. This parameter transformation consolidates complex multi-dimensional data into one interpretable metric, maintaining high sensitivity to health conditions while simplifying the output for clinical use and reducing processing complexity.
Data Source
AI summary
Methods of assessing risk based on medical data are disclosed herein. In an embodiment, a method of assessing risk associated with medical data includes creating a dataset representing a plurality of patients, the dataset comprising (x,y) pairs for each patient, wherein x represents the medical data collected at a first time, and wherein y is an outcome measurement collected at a second time; binning the (x,y) pairs to form a plurality of binned data sets; computing an average value for x and an average value for y for each binned data set; determining a minimum average value of y based on all of the average values of y; subtracting the minimum average value of y from each average value of y to get a new average value of y for each binned data set; and deriving a function for assessing risk associated with the medical data.