Adaptive Neurostimulation Workflow for Patient Feedback Collection
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Solution Overview
Problem
Current neurostimulation systems face challenges in collecting consistent and useful user feedback from patients, as some patients are reluctant to provide frequent interactions, leading to incomplete data sets that hinder personalized and closed-loop programming.
Innovation Solution
A computing system configured to analyze user interaction data from neurostimulation treatments, generating customized tasks and workflows to collect additional input based on patient-specific attributes, and controlling the presentation of questions to optimize feedback collection, which can lead to automatic adjustments in neurostimulation programming settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If standardized questionnaires and fixed interaction workflows are used to collect user feedback, then data collection consistency is improved, but patient engagement and completion rates deteriorate due to excessive interaction burden
Solution Approach 1:
The system dynamically adapts interaction workflows based on individual patient characteristics, treatment stage, and historical engagement patterns. The questionnaire structure, question selection, and interaction frequency are automatically adjusted to match each patient's preferences and needs, transforming fixed standardized processes into flexible adaptive ones that maintain consistency while reducing burden.
Solution Approach 2:
Different interaction approaches and questionnaire types are applied to different patients based on their specific characteristics. Instead of using a uniform approach for all patients, the system tailors the interaction style, question depth, and feedback requests to each individual's preferences, treatment progress, and engagement history, optimizing data collection for each patient locally.
2Measurement precision
If extensive and frequent user feedback is requested to enable personalized programming, then customization accuracy is improved, but patient burden and interaction complexity increase
Solution Approach 1:
The system collects feedback at optimized frequencies and depths based on treatment stage and patient needs rather than requesting maximum possible data continuously. Questionnaires are administered selectively at clinically relevant timepoints, and question complexity is adjusted to match treatment phase, avoiding unnecessary interaction burden while gathering sufficient data for effective personalization.
Solution Approach 2:
The feedback collection process is segmented into multiple stages with different question sets and interaction types appropriate for each treatment phase. Instead of presenting all questions simultaneously, the system divides feedback collection into progressive segments that build upon previous responses, reducing perceived complexity while maintaining comprehensive data gathering.
3Ease of operation
If minimal interaction is allowed to reduce patient burden, then ease of use is improved, but data completeness deteriorates hindering closed-loop programming
Solution Approach 1:
The system uses real-time feedback from patient responses to dynamically adjust subsequent interaction requirements. Based on the quality, quantity, and type of feedback received, the system adapts future questionnaire content and frequency to achieve target data completeness thresholds. This feedback loop ensures minimal necessary interaction while maintaining sufficient data quality for closed-loop programming decisions.
Solution Approach 2:
The system automatically processes and analyzes patient feedback without requiring additional clinician time or manual data entry. Intelligent algorithms automatically interpret responses, update patient profiles, and generate programming recommendations, allowing the system to maintain high data completeness standards while keeping patient interaction simple and self-directed.
Data Source
AI summary
A system (e.g., computing system) for analyzing user interaction associated with a neurostimulation treatment may include functionality to: receive user interaction data associated with a patient undergoing the neurostimulation treatment, with the user interaction data indicating attributes of one or more user interactions in a software platform (e.g., patient computing device) that collects user feedback relating to the neurostimulation treatment; identify attributes of the user interactions from the user interaction data; generate a task to collect additional user input relating to the neurostimulation treatment, with the task being customized to the patient based on the attributes of the user interactions; and control an interaction workflow to perform the task in the software platform. Further functionality may: modify the interaction workflow for the neurostimulation treatment; identify a patient state resulting from the neurostimulation treatment; or cause a change in a closed-loop programming therapy for the neurostimulation treatment.