Contextual Assessment Engine for Patient Data
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Solution Overview
Problem
The growing amount of data generated daily leads to inefficiencies in decision-making processes due to the time required to sort through stored data, with much of it being ignored or abandoned, 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 a unified format for contextual evaluations and suggestions, enabling informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored for future reference and analysis, then data availability is improved, but data processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by generating context-based evaluations and storing them in advance. The transformative processing engine continuously processes incoming data and pre-computes contextual evaluations, so when users need information, it is already prepared and available, eliminating the need for time-consuming on-demand analysis.
Solution Approach 2:
The system changes the parameter of data representation by transforming raw data into contextual evaluations with assigned scores. Instead of storing and retrieving raw data that requires complex processing, the system stores pre-transformed evaluations that can be quickly accessed and used for decision-making.
2Reliability
If all generated data is retained and analyzed, then decision accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system extracts only the essential contextual information from vast amounts of raw data. The transformative processing engine identifies and extracts relevant context-based evaluations rather than processing all raw data, reducing system complexity while maintaining decision accuracy by focusing on the most important information.
Solution Approach 2:
The context-based evaluation system acts as an intermediary layer between raw data and decision-making processes. This intermediary transforms complex multi-source data into simplified contextual evaluations with scores, making the data more usable and reducing the complexity of downstream decision-making systems.
3Reliability
If contextual evaluations are generated for all data, then decision quality is improved, but processing speed decreases
Solution Approach 1:
The system applies partial action by generating contextual evaluations selectively rather than for all data uniformly. The transformative processing engine focuses computational resources on generating evaluations for the most relevant and impactful data points, achieving high decision quality without the overhead of processing every single data point exhaustively.
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.