Machine Learning Model Retraining for Medical Order Prior Authorization
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Solution Overview
Problem
Existing medical order processing systems face challenges in accurately determining whether a medical order requires prior authorization (PA) due to unpredictable changes in medical order conditions, leading to delays and improper processing.
Innovation Solution
A machine learning system is employed to train a predictive model using historical orders and payer responses, applying data balancing and multi-algorithmic approaches to generate a trained predictor that determines PA requirements for incoming orders, ensuring dynamic adaptation to changes without manual verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static data models are used to determine PA requirements, then the system structure is simple, but the model cannot adapt to changing medical order conditions over time
Solution Approach 1:
The patent implements dynamic model retraining by periodically updating the machine learning model with new historical order data and payer responses. The system transitions from a static model to a dynamic one that adapts to changing medical order conditions through automated retraining cycles, allowing the model to maintain accuracy as conditions evolve over time
Solution Approach 2:
The system performs self-service through automated model retraining without requiring manual intervention. The machine learning system automatically retrieves new historical data, retrains the model, and deploys updated predictions, eliminating the need for manual model updates while maintaining adaptability to changing conditions
2Reliability
If manual verification steps are implemented to improve accuracy, then prediction reliability improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by continuously retraining the model in the background using historical data before predictions are needed. This preparatory retraining ensures the model is already optimized and accurate when processing new medical orders, eliminating the need for time-consuming manual verification during actual order processing
Solution Approach 2:
The system implements feedback mechanisms by using historical payer responses to evaluate and improve model performance. The model learns from past predictions and actual outcomes, continuously refining its accuracy through feedback loops that incorporate real-world results, thereby maintaining high reliability without manual intervention
3Measurement precision
If the model is retrained frequently to maintain accuracy, then predictive accuracy is maintained, but computational resources are consumed
Solution Approach 1:
The system applies periodic action by retraining the model at scheduled intervals rather than continuously. This periodic retraining approach maintains predictive accuracy by updating the model with fresh historical data at appropriate frequencies, while avoiding unnecessary computational waste that would result from overly frequent retraining cycles
Solution Approach 2:
The system changes parameters by adjusting retraining frequency and data sampling strategies to optimize the balance between accuracy and resource consumption. By modifying parameters such as the interval between retraining events and the amount of historical data used, the system achieves acceptable predictive accuracy while minimizing computational resource usage
Data Source
AI summary
A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.