Macro-personalization Engine for Virtual Care Triage
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Solution Overview
Problem
Current behavioral health care systems face challenges such as inaccessible and low-quality care due to provider shortages, stigma, and inefficient treatment paradigms, leading to unmet needs and suboptimal patient outcomes, particularly in virtual care settings.
Innovation Solution
A macro-personalization engine within virtual care platforms that uses a sequence of personal needs questions and clinical surveys to segment patients by level of need, provide real-time tracking, and recommend appropriate care levels, including self-management options and licensed provider interventions, to enhance treatment fidelity and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional behavioral health care systems are used, then care can be provided, but accessibility and quality suffer due to provider shortages and inefficient treatment paradigms
Solution Approach 1:
The patent segments the behavioral health care system into multiple tiers: AI-driven self-management tools for mild cases, virtual coaching for moderate cases, and licensed provider intervention for severe cases. This segmentation allows each component to handle appropriate cases efficiently, improving overall system reliability while enabling scalable productivity through automated triage and personalized care pathways.
2Productivity
If more licensed providers are added to increase capacity, then more patients can be served, but costs increase and provider burnout persists
Solution Approach 1:
The patent introduces AI-driven virtual coaches and self-management platforms as intermediaries between patients and licensed providers. These intermediaries handle routine monitoring, motivation, and basic interventions, allowing licensed providers to focus on complex cases. This mediator layer expands patient capacity without proportionally increasing provider resources, reducing burnout while maintaining care quality.
3Reliability
If personalized care is implemented for each patient, then treatment quality improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent uses parameter changes by dynamically adjusting care intensity and modality based on patient response to treatment. The system monitors multiple parameters (symptom severity, engagement level, progress metrics) and automatically modifies the care plan, transitioning between self-management, virtual coaching, and provider intervention as needed. This automated parameter adjustment achieves personalized care without manual complexity.
4Measurement precision
If comprehensive monitoring and tracking are implemented, then unmet needs are identified better, but data processing complexity and privacy concerns increase
Solution Approach 1:
The patent extracts and focuses on specific, clinically relevant data points needed for behavioral health assessment rather than collecting comprehensive data. The system selectively monitors key parameters such as mood ratings, symptom severity, and treatment adherence, processing only this essential information to identify unmet needs. This extraction approach maintains high measurement precision while minimizing data management burden and privacy risks.
Data Source
AI summary
A computer-implemented method for personalizing a care program for a telehealth platform includes displaying a sequence of questions on a display of a user device including a plurality of personal needs questions and a plurality of questions associated with a clinical survey, receiving responses from a user to each question in the sequence of questions via an input device of the user device, storing the responses in a memory of the user device, assigning a primary concern to the user based on a response to at least a first question in the sequence of questions, assigning a severity score to the user based on the responses to the clinical survey, using a segmentation model to assign a recommended program to the user based on the primary concern and the severity score of the user, and notifying the user of the recommended program.