Patient Data Trigger Detection for Personalized Message Generation
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Solution Overview
Problem
Current healthcare communication systems lack efficient methods for generating personalized and timely messages in response to specific patient data triggers, leading to suboptimal patient engagement and care coordination.
Innovation Solution
A patient management system that utilizes a machine learning model to generate system-composed messages in response to detected data triggers, incorporating real-time dataset monitoring, feedback loops, and personalized message generation tools to ensure relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated message generation is implemented using machine learning models, then patient engagement and care coordination are enhanced, but system complexity and computational resources required increase
Solution Approach 1:
The system segments the message generation process into distinct modular components: dataset monitoring module, trigger detection module, machine learning model processing module, and message composition module. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity through automated end-to-end message generation from patient data to personalized communications
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw patient datasets and final personalized messages. This intermediary component transforms unstructured patient data into structured, context-aware message content, thereby enhancing message generation efficiency while encapsulating complexity within the model layer rather than requiring complex system-wide changes
2Loss of time
If real-time dataset monitoring is performed to detect triggers for message generation, then timeliness of patient communications is improved, but computational energy consumption and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing patient datasets before trigger detection is needed. Patient data is structured, validated, and organized in advance, allowing the trigger detection mechanism to operate efficiently on pre-prepared data rather than raw data, thereby reducing real-time computational energy consumption while maintaining rapid response times when triggers occur
Solution Approach 2:
The machine learning model operates autonomously to monitor datasets and detect triggers without requiring continuous human intervention or manual system activation. Once deployed, the system self-monitors patient data streams, automatically identifies trigger conditions, and initiates message generation workflows, thereby reducing operational overhead and energy consumption associated with manual monitoring while ensuring timely response to patient events
3Adaptability or versatility
If personalized messages are generated based on patient-specific data triggers, then patient engagement is enhanced, but data processing complexity and message customization requirements increase
Solution Approach 1:
The system applies local quality by customizing message content, tone, and formatting based on specific patient characteristics, data trigger types, and communication preferences. Each message is locally optimized for its intended recipient rather than using uniform templates, thereby enhancing patient engagement while the modular architecture manages the resulting processing complexity through targeted, context-specific customization rules
Solution Approach 2:
The machine learning model dynamically adjusts message parameters such as content, length, tone, and formatting based on patient data attributes and trigger conditions. By changing message parameters adaptively rather than creating entirely different message templates for each scenario, the system achieves high personalization versatility while managing data processing complexity through parameter-based customization of base templates
Data Source
AI summary
Techniques for generating a system-composed message in response to detecting a dataset that satisfies a trigger for generating a message are disclosed. Initially, the system monitors, in real-time, datasets that are presented in a dashboard and/or are received by the system. The system applies a set of rules or a machine learning model to the datasets that are presented within the dashboard to determine whether the dataset satisfies a trigger for generating a message. In an example, a data type of the dataset is used to determine whether the trigger for generating the message is met. In another example, a date associated with the dataset is used to determine whether the trigger for generating the message is met.