Predictive Threshold Calibration for Volatile Cut Point Forecasting

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Solution Overview

Problem

Existing threshold-based predictive data analysis solutions are inefficient and unreliable due to the computational expense of setting optimal threshold values and sensitivity to real-world developments, particularly in domains like healthcare quality ratings where cut points change frequently and unpredictably.

Innovation Solution

Utilize historical data simulations to calibrate distributions, generate projected distributions, and simulate these to determine optimal threshold ranges, employing probabilistic automated programming to quantify variability and risk.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional threshold-based predictive data analysis is used, then simplicity of implementation is maintained, but computational expense increases and reliability decreases due to sensitivity to real-world developments

Engineering Contradiction:

Improvereliability of predictive data analysisVSAvoidcomplexity of threshold optimization process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary calibration by analyzing historical data distributions and determining optimal threshold ranges before actual predictive analysis is needed. This advance preparation stores calibrated distributions that can be quickly applied during runtime, eliminating the need for expensive real-time threshold optimization while maintaining reliability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adapts threshold calculations by incorporating projected trends and simulated future distributions. Rather than using static historical thresholds, the system updates threshold predictions based on evolving data patterns and calibrated historical distributions, making the analysis reliable in changing real-world conditions.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If optimal threshold values are calculated using traditional methods, then measurement precision is improved, but productivity decreases due to computational expense

Engineering Contradiction:

Improveprecision of cut point threshold predictionsVSAvoidefficiency of predictive data analysis

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system pre-calculates calibrated historical distributions and optimal threshold ranges during a calibration phase using historical data. These pre-computed results are stored and reused during predictive analysis, achieving high precision without repeating expensive computational operations, thus maintaining productivity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the approach from calculating exact optimal thresholds to determining threshold ranges based on calibrated distributions. This parameter shift from precise point estimates to probabilistic ranges maintains measurement precision while significantly reducing computational expense and improving productivity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If frequent threshold updates are performed to adapt to changing conditions, then adaptability is improved, but loss of time increases due to repeated calculations

Engineering Contradiction:

Improveadaptability to changing cut pointsVSAvoidtime for threshold recalculation

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs threshold calibration in advance using historical data and stores the calibrated distributions. When adapting to new conditions, it combines these pre-computed calibrated distributions with projected trends through simulation, achieving adaptability without repeating the full expensive calibration process, thus minimizing time loss.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of performing complete threshold recalibration when adapting to changes, the system uses partial updates by combining calibrated historical distributions with projected trend simulations. This partial action approach maintains adaptability to changing conditions while avoiding the time cost of full recalculation.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12547915B2Predictive data analysis techniques using predictive threshold optimization and probabilistic automated programming

Publication Date: 2026.02.10 UNITEDHEALTH GROUP INC

AI summary

There is a need for more effective and efficient threshold-based predictive data analysis. This need can be addressed by, for example, solutions for performing predictive threshold optimization using probabilistic automated programming. In one example, a method includes determining an initial historical distribution for a measure based on historical measurement data associated with the measure; determining a calibrated historical distribution for the measure based on the initial historical distribution; determining projected distributions for the measure based on a projected trend for the predictive distribution, wherein the projected trend is determined based on the calibrated historical distribution; determining a projected cut point threshold predictions for the measure based on a predefined number of projected sampled simulations, wherein the predefined number of projected sampled simulations are determined based on the projected distributions; and performing one or more prediction-based actions based on the projected cut point threshold projections.