Multi-Metric Forecasting via Causal Inference and Segmentation
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Solution Overview
Problem
Traditional impact forecasting techniques are limited in complex multi-actor, multi-metric domains, such as healthcare plans, due to their inability to effectively compare or combine predictive insights from disparate metrics, leading to inefficient action evaluation and optimization.
Innovation Solution
The use of a sequence of connected machine learning, causal inference, and probabilistic combinatorial techniques to generate predictive insights that are directly comparable across multiple performance metrics, allowing for the simulation and evaluation of candidate actions and the creation of holistic, multi-metric impact scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional impact forecasting techniques use single-type metrics, then model simplicity is maintained, but the ability to evaluate complex multi-metric domains is limited
Solution Approach 1:
The patent segments the complex multi-metric evaluation problem into multiple single-metric predictive models, each specialized in evaluating a specific metric type. This allows each model to remain simple while the collective system handles complex multi-metric domains effectively.
Solution Approach 2:
The patent creates a universal framework that integrates multiple specialized predictive models, enabling the system to evaluate diverse metric types (clinical, operational, financial, etc.) through a unified multi-metric scoring mechanism that ranks actions across all metric types.
2Measurement precision
If traditional techniques compare disparate metrics individually, then measurement precision for each metric is maintained, but the ability to collectively rank actions across multiple metrics is lost
Solution Approach 1:
The patent merges multiple discrete metric evaluations into a unified multi-metric impact score through weighted aggregation. This combines the precision of individual metric measurements while enabling efficient collective ranking of actions across all metrics simultaneously.
Solution Approach 2:
The patent transforms individual metric scores into a standardized unified impact score by applying weight parameters and aggregation functions. This parameter transformation enables direct comparison and collective ranking of actions across disparate metric types while preserving the precision of underlying measurements.
3Ease of operation
If no integration method is used for multiple metrics, then ease of implementation is maintained, but holistic impact assessment capability is limited
Solution Approach 1:
The patent introduces a unified impact score as an intermediary that aggregates information from multiple discrete metrics. This intermediary mechanism preserves ease of implementation by providing a single ranking output while preventing information loss by systematically integrating all metric contributions through weighted aggregation.
Data Source
AI summary
Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, categorical improvement predictions. The techniques may include generating a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group. The techniques include using an action-specific causal inference model to generate a metric-specific predictive impact measure. The techniques include generating a metric-level categorical improvement prediction and a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics. The techniques include initiating a performance of a prediction-based action based on the categorical improvement prediction.