Service Visualization System for Patient Resource Coordination
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Solution Overview
Problem
The increasing volume of data in various environments leads to inefficiencies and undesirable outcomes due to the time required to sort through stored data, and the tendency to ignore or abandon significant data.
Innovation Solution
A computer-implemented method and system for coordinating user service by classifying users and resources into groupings, generating a visual representation of these groupings, and providing recommendations for service coordination, utilizing a service visualization system that integrates with a service management system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored for future use, then data availability is improved, but time required to sort through data increases
Solution Approach 1:
The patent segments data into different categories using classification models that assign users to groups (e.g., priority levels, service types) and resources to groups (e.g., service unit types, availability status). This segmentation allows the system to quickly identify and present only the relevant data subsets to users, reducing the time needed to sort through stored data while maintaining comprehensive data availability.
Solution Approach 2:
The system performs preliminary classification and organization of data in advance using machine learning models. Users and resources are pre-categorized into groups with associated characteristics, and visual representations are pre-generated. This preliminary action enables rapid retrieval and presentation of relevant information when needed, eliminating the time-consuming process of sorting through stored data from scratch.
2Measurement precision
If comprehensive data is stored, then decision-making quality is improved, but data processing efficiency decreases
Solution Approach 1:
The patent applies local quality by customizing the level of detail and type of data presented to each user based on their specific characteristics and needs. The classification model identifies local requirements (e.g., a user needing detailed resource status vs. a user needing high-level summaries) and adjusts the data presentation accordingly. This maintains decision-making quality for each user while improving overall processing efficiency by avoiding unnecessary data processing for all users.
Solution Approach 2:
The system dynamically adjusts data processing and presentation based on real-time conditions and user characteristics. The classification model continuously refines user groupings and resource allocations, and the visual representations are dynamically generated and updated. This dynamic approach ensures that comprehensive data is processed only when and where needed, maintaining decision-making quality while improving processing efficiency.
3Measurement precision
If manual data sorting is performed, then data accuracy is improved, but processing time increases
Solution Approach 1:
The patent replaces manual data sorting with automated machine learning classification models. The system automatically classifies users into groups, determines resource allocations, and generates visual representations without human intervention in the sorting process. This substitution maintains data accuracy through sophisticated algorithms while dramatically reducing the time required compared to manual sorting methods.
Solution Approach 2:
The classification model performs self-service by automatically organizing and presenting data without requiring manual sorting intervention. The system self-adjusts to user needs and resource availability, autonomously generating accurate data presentations. This self-service approach eliminates the time-consuming manual sorting process while maintaining high data accuracy through automated intelligent classification.
Data Source
AI summary
The techniques may include receiving patient data of patients of a service facility, each patient assigned to a service unit. In addition, the techniques may include inputting patient data into a classification model trained to output classifications. The techniques may include determining a classification for each patient. Moreover, the techniques may include assigning each patient to a patient group based at least in part on a classification common with members of the patient group. The techniques may include determining a recommendation associated with at least one of: (1) servicing a first service resource associated with a third service unit, or (2) procuring a second service resource associated with a fourth service unit. This recommendation can be based at least in part on the classifications. Further, the techniques may include presenting on a user device of a patient care provider for coordinating patient care among service units of the service facility.