Intelligent Resource Allocation via ML Scoring and Segmentation
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Solution Overview
Problem
Existing resource allocation methods in the service industry, particularly in healthcare, lack accuracy and efficiency due to reliance on generalized data and failure to preemptively identify protocol changes or misapplication of protocols, leading to computational inefficiencies and suboptimal resource utilization.
Innovation Solution
A computer-implemented method and system that utilizes machine-learning models to analyze user and system data, filtering for target systems and users, generating scores to prioritize resource allocation based on likelihood of protocol changes, and initiating actions for optimized resource distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generalized data is used for resource allocation, then the scope of analysis is broad, but the prediction accuracy decreases and noise increases
Solution Approach 1:
The patent segments the data processing into two distinct stages: first filtering systems to identify target systems, then filtering users within those systems to identify target users. This segmentation allows the model to process only relevant data subsets, improving prediction accuracy while reducing overall data volume and noise.
Solution Approach 2:
The patent extracts and removes irrelevant data through filtering operations before applying the machine learning model. By taking out non-critical systems and users from the generalized data set, the model receives only high-quality, relevant input data, thereby improving prediction accuracy without processing unnecessary noise.
2Reliability
If reactive methods based on past data are used, then implementation is simple, but the ability to preemptively identify protocol changes is lost
Solution Approach 1:
The patent applies preliminary action by using the machine learning model to predict future protocol changes and misapplications before they occur. The system proactively identifies at-risk users and systems, allowing resource allocation and interventions to be prepared in advance rather than reacting after problems occur, thus improving both reliability and reducing time loss.
3Productivity
If all user data is processed, then comprehensive coverage is achieved, but computational efficiency decreases
Solution Approach 1:
The patent divides the data processing task into segmented filtering stages: system-level filtering followed by user-level filtering. This segmentation reduces the total data volume that requires intensive machine learning processing, thereby improving computational efficiency and resource allocation productivity while maintaining comprehensive coverage of relevant cases.
Solution Approach 2:
The patent applies partial action by processing only the subset of data that is most relevant to prediction accuracy. Instead of processing all user data equally, the system focuses computational resources on target users within target systems who are most likely to exhibit protocol changes or misapplications, improving productivity by avoiding unnecessary processing of excessive data.
Data Source
AI summary
Systems and methods are disclosed for allocating system resources. One or more processors may receive a plurality of system data sets and a plurality of user data sets. The one or more processors may determine one or more target systems by applying one or more filters to the plurality of system data sets and one or more target users associated. One or more processors may determine one or more target user data sets associated with each of the one or more target users. One or more processors may apply a machine-learning model to the one or more target user data sets to generate a user-level score. One or more processors may generate a system-level score for each of the one or more target systems associated with the one or more target users. One or more processors may initiate performance of one or more actions.