Machine Learning Content Selection for Clinical Care Guidance
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Solution Overview
Problem
In clinical settings, individuals providing care to patients, such as family members or friends, often lack the necessary training and understanding of the care procedures, leading to sub-optimal care, especially in end-of-life situations.
Innovation Solution
A system utilizing machine learning to generate and curate content that guides users in clinical settings. This system processes clinical assessments and user characteristics to select relevant content from a library, which is then delivered to users, improving their understanding and ability to provide care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to generate content selections, then content relevance and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that sits between the clinical assessment data and the content library. This mediator processes the unstructured assessment data, extracts meaningful features, and generates personalized content selections without requiring the entire system to become complex. The ML model handles the complexity of data processing and pattern recognition, while the rest of the system remains relatively simple and maintainable.
Solution Approach 2:
The system is divided into distinct functional modules: data collection module, machine learning processing module, content library, and delivery module. By segmenting the system this way, each component has a specific, well-defined function. The ML module can be trained and updated independently, while other components remain simple and easy to implement. This modular approach reduces overall system complexity while maintaining high content relevance.
2Reliability
If personalized content is generated for each user, then user engagement and care quality are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and content pre-processing before actual content selection is needed. Clinical assessment data is processed in advance to extract meaningful features and characteristics. Content items in the library are pre-tagged and organized by relevance criteria. When a new user needs content, the system can quickly match pre-processed features against pre-organized content without performing complex analysis in real-time, significantly reducing processing time while maintaining personalized care quality.
Solution Approach 2:
Instead of processing all possible content items in the library for each user, the system uses the machine learning model to identify and process only the most relevant subset of content. The ML algorithm efficiently filters and selects top candidate content items based on user characteristics and clinical data, avoiding unnecessary processing of irrelevant content. This partial action approach maintains high care quality personalization while dramatically reducing computational resources and processing time compared to exhaustive processing.
Data Source
AI summary
Techniques for improved machine learning are provided. Assessment data for a patient is accessed, the assessment data generated by a clinician associated with the patient in a clinical setting. A set of machine learning features is generated based on performing feature extraction on the assessment data, and a content selection, from a library of content, is generated based on processing at least a subset of the set of machine learning features using a machine learning model. Delivery of the content selection to a user is initiated, where the user cares for the patient in the clinical setting.