Contextual Assessment Engine for Medical Data Processing
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Solution Overview
Problem
The increasing volume of data generated daily leads to inefficiencies in sorting and decision-making, as much data is either ignored or abandoned, particularly in operational flows, resulting in undesirable outcomes.
Innovation Solution
A system that utilizes a transformative processing engine to manage and process data from various sources, including sensors and user devices, transforming and aggregating it into usable formats for context-based evaluations and suggestions, enabling informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in large volumes for operational flow, then data availability is improved, but time required to sort through and evaluate the data increases
Solution Approach 1:
The system performs preliminary evaluation and contextual analysis of data before it is needed for decision-making. The evaluation engine continuously processes and tags data with contextual information in advance, so when users need to make decisions, the data is already prepared and organized, eliminating the time-consuming sorting process.
Solution Approach 2:
The patent introduces an evaluation engine and contextual tags as intermediaries between the stored data and the users. These intermediaries automatically interpret, evaluate, and annotate the data, providing users with pre-processed information that highlights relevance and context, thus reducing the time users spend evaluating raw data.
2Productivity
If data is processed and evaluated in real-time, then decision-making efficiency is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: data collection components, storage systems, evaluation engines, and user interface elements. Each module performs a specific function, making the overall complex system manageable through clear separation of concerns. The evaluation engine itself is segmented into multiple analysis components that process different aspects of data independently.
Solution Approach 2:
The evaluation engine is designed as a universal component that can handle multiple types of data (sensor data, user input, operational data) and apply various evaluation methods through configurable parameters and machine learning models. This multi-functionality reduces the need for separate specialized systems for different data types, managing complexity while maintaining versatility.
3Loss of information
If contextual evaluation is provided for all data, then information quality is improved, but processing resources are consumed
Solution Approach 1:
The system applies contextual evaluation selectively rather than uniformly to all data. The evaluation engine identifies and applies contextual analysis primarily to data points that are relevant to current operational contexts or user needs, leaving less critical data with minimal or no contextual processing. This localized approach maintains information quality where it matters most while conserving processing resources.
Solution Approach 2:
The system dynamically adjusts evaluation parameters and processing depth based on contextual relevance, user preferences, and system state. When processing resources are constrained, the evaluation engine reduces the depth of analysis or skips evaluation for low-priority data. Machine learning models adapt their processing intensity based on the importance and recency of data, optimizing the balance between information quality and resource consumption.
Data Source
AI summary
In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.