Real-Time Redirection Score Generation Using Segmented ML Models
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Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies and reliability issues in generating redirection scores for healthcare facilities, requiring computationally expensive operations and lacking real-time adjustments.
Innovation Solution
The use of pretrained diagnosis prediction machine learning models to generate predicted redirection scores by combining generic scores from provider evaluation models with real-time scores from adjustment models, incorporating static and dynamic operational features, and diagnostic point-in-time features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive data analysis solutions are used to generate redirection scores, then comprehensive evaluation can be achieved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the redirection score generation into two independent components: a generic score from provider evaluation models and a real-time adjustment score from adjustment models. This segmentation allows each model to specialize in specific aspects (historical performance vs. current availability), improving overall accuracy while reducing computational burden compared to a single comprehensive model.
Solution Approach 2:
The patent pre-computes generic redirection scores using provider evaluation models based on historical data and static features. These pre-computed scores are stored and can be quickly retrieved during runtime, eliminating the need to re-process historical data for each real-time query, thus significantly improving computational efficiency.
2Reliability
If real-time adjustments are implemented in redirection score generation, then current operational status is captured, but system complexity increases
Solution Approach 1:
The patent extracts real-time adjustment factors (current availability, wait times, dynamic operational status) as separate features from the overall evaluation process. These extracted real-time features are fed into specialized adjustment models that only process current status data, maintaining reliability while avoiding the complexity of integrating real-time processing into the entire evaluation pipeline.
Solution Approach 2:
The patent introduces adjustment models as intermediary components between the generic score generation and final redirection score calculation. These intermediary models specialize in processing real-time data and translating it into score adjustments, simplifying the overall system architecture by creating a clear separation of concerns between historical evaluation and real-time adjustment.
3Measurement precision
If multiple machine learning models are combined for score generation, then evaluation comprehensiveness is improved, but computational operations increase
Solution Approach 1:
The patent merges the output of multiple specialized models (provider evaluation models and adjustment models) into a unified redirection score through a simple additive or multiplicative combination. This merging strategy maintains the comprehensiveness benefits of multiple models while avoiding the computational overhead of complex ensemble methods, achieving a balance between evaluation quality and computation speed.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating predicted redirection scores based at least in part on: (i) generic redirection scores that are generated using provider evaluation machine learning models, and (ii) real-time redirection scores that are generated using real-time adjustment machine learning models.