Patient Feedback for Atrial Fibrillation Burden Attenuation
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Solution Overview
Problem
Existing medical devices struggle to effectively identify patient behaviors contributing to increased atrial fibrillation (AF) burden, leading to inadequate attenuation or elimination of such patterns.
Innovation Solution
A medical device system that includes a patient interface to ask patients to identify behaviors and provides personalized suggestions to change behaviors likely contributing to increased AF burden, using machine learning models trained on parametric data to determine risk levels and suggest behavior modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system automatically detects AF burden patterns without patient behavior input, then the monitoring process is simpler and faster, but the identification accuracy of contributing behaviors is insufficient
Solution Approach 1:
The system implements a feedback loop where it automatically detects AF burden patterns, presents relevant behavior questions to patients, receives patient responses, and uses these responses to refine and update the AF burden analysis. This iterative feedback process improves identification accuracy by incorporating patient self-reporting data with automated physiological monitoring.
Solution Approach 2:
The system segments the AF burden analysis into distinct components: automated physiological signal processing, patient behavior questionnaires, and integrated analysis. By separating these functions, the system can maintain simplicity in each component while achieving high overall accuracy through their coordinated operation.
2Reliability
If the system provides personalized behavior suggestions to patients, then the effectiveness of attenuating AF burden increases, but the device complexity and operational burden increase
Solution Approach 1:
The system changes parameters such as question timing, suggestion personalization level, and notification frequency based on detected AF burden patterns and patient responses. This adaptive parameter adjustment allows the system to maintain high effectiveness in attenuating AF burden while avoiding unnecessary complexity through context-aware operation.
Solution Approach 2:
The system enables patients to self-report behaviors through simple interface questions and receives personalized suggestions autonomously without requiring clinician intervention. This self-service approach improves reliability of AF burden attenuation while minimizing operational complexity by empowering patients to participate in their own monitoring and management.
3Measurement precision
If the system collects detailed patient behavior data through interfaces, then the accuracy of AF burden analysis improves, but the operational burden on patients increases
Solution Approach 1:
The system applies local quality by tailoring the specific questions and interaction depth to the local context of each patient's AF burden pattern and individual needs. Rather than uniformly collecting all possible behavior data, the system adapts the scope and type of questions to what is most relevant for each patient's specific situation, maintaining precision while improving ease of operation.
Solution Approach 2:
The system uses partial action by collecting only the most relevant behavior data needed for accurate AF burden analysis at any given time, rather than requiring comprehensive continuous data collection. This selective data collection approach maintains measurement precision for the critical AF burden patterns while significantly reducing operational burden on patients.
Data Source
AI summary
A medical device system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to receive parametric data for a plurality of parameters of a patient, determine, based on the parametric data, an atrial fibrillation (AF) burden of the patient over a period of time, wherein the AF burden of the patient over the period of time includes a pattern of increased AF burden; output, for display by a user device, a request to identify whether the patient engaged in each patient behavior of a set of patient behaviors during the period of time; and determine, based on receiving a response indicating that the patient engaged in one or more patient behaviors of the set of patient behaviors, a suggestion to change at least a subset of the one or more patient behaviors to attenuate the pattern of increased AF burden.