Adaptive Patient Baseline Estimation Using Acuity Scores
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Solution Overview
Problem
Establishing accurate and individual-specific patient baselines for disease prediction and monitoring is challenging due to variations in patient physiology and limited data availability, especially at the beginning of hospital admission.
Innovation Solution
A dynamic model using a computer-implemented method that collects and compares patient health data from a target patient with retrospective clinical data from similar patient subgroups, employing an adaptive selection algorithm to determine whether to use patient health data or subgroup data for baseline calculation based on acuity scores, medical treatment length, variance, and subgroup similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient baselines are determined using individual patient data only, then individual-specific accuracy is improved, but data availability and reliability deteriorate due to limited data especially at beginning of hospital admission
Solution Approach 1:
The patent combines individual patient data with data from similar patient subgroups to establish baselines. When individual patient data is insufficient (especially at admission), the system merges with aggregated data from similar patients to achieve reliable baseline determination, then gradually transitions to individual data as more becomes available.
Solution Approach 2:
The system performs preliminary baseline estimation using similar patient subgroup data before sufficient individual patient data is available. This preliminary action enables immediate disease prediction and monitoring capabilities at admission, which are then refined as individual data accumulates over time.
2Adaptability or versatility
If patient baselines are determined using individual patient data only, then individual-specificity is improved, but reliability deteriorates due to substantial variations in patient physiology and heterogeneous characteristics
Solution Approach 1:
The patent segments the overall patient population into distinct subgroups based on physiological characteristics, demographics, and clinical features. This segmentation allows comparison with similar patients while maintaining individual-specificity through progressive individualization as data accumulates.
Solution Approach 2:
The baseline determination process is dynamic, transitioning from reliance on similar patient subgroup data to increasing reliance on individual patient data as more individual data becomes available. The system adaptively adjusts the weight of individual versus subgroup data over time, making the baseline increasingly individual-specific while maintaining reliability.
3Loss of time
If individual-specific baselines are determined with limited data, then early disease prediction capability is improved, but measurement precision deteriorates
Solution Approach 1:
The system establishes preliminary baselines using similar patient subgroup data immediately upon admission, enabling early disease prediction without waiting for sufficient individual data to accumulate. This preliminary baseline is then continuously refined as individual patient data becomes available, improving precision over time.
4Quantity of substance
If data from similar patient subgroups is used for baseline determination, then data sufficiency is improved, but individual-specificity deteriorates
Solution Approach 1:
The system dynamically adjusts the composition of baseline data over time, starting with heavier reliance on similar patient subgroup data when individual data is scarce, then progressively increasing the weight of individual patient data as it accumulates. This dynamic transition ensures both data sufficiency and growing individual-specificity.
Solution Approach 2:
The system uses feedback from accumulating individual patient data to progressively individualize the baseline. As individual data becomes available, it feeds back into the baseline determination process, adjusting the baseline to better reflect the specific patient's characteristics while maintaining the structural benefits of similar patient subgroup comparisons.
Data Source
AI summary
The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.