AI Diagnostic Model for Multi-Source Mental Health Screening
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Solution Overview
Problem
Current diagnostic screening for mental health disorders, such as depression, is methodologically basic and underutilized, particularly in primary care settings and among vulnerable populations, leading to missed diagnoses and inadequate treatment.
Innovation Solution
The implementation of asynchronous telepsychiatry (ATP) and AI-assisted screening, diagnosis, and treatment systems, which utilize artificial intelligence models to simulate patient diagnoses and treatment plans, and integrate patient-reported data with physiological markers to improve diagnostic accuracy and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple validated questionnaires are used for depression screening, then the screening process is easy to implement, but the diagnostic accuracy and reliability are insufficient leading to missed diagnoses
Solution Approach 1:
The patent combines multiple data sources including patient-reported questionnaire data, physiological markers (heart rate, blood pressure, temperature), and behavioral data into a single integrated AI diagnostic system. This merging of diverse data types enhances diagnostic accuracy while maintaining ease of implementation through automated processing.
Solution Approach 2:
The diagnostic system uses a composite approach by integrating multiple types of information (subjective patient reports, objective physiological measurements, and behavioral observations) into a unified diagnostic model, similar to how composite materials combine different substances to achieve superior properties.
2Productivity
If more healthcare providers are deployed to increase screening capacity, then the screening coverage improves, but the cost and complexity of the healthcare system increases
Solution Approach 1:
The system enables patients to complete screening questionnaires and provide data independently without requiring direct provider involvement for each screening. The AI automatically processes the data and generates diagnostic assessments, allowing the system to serve itself in the screening process while maintaining high capacity.
Solution Approach 2:
The patent replaces the mechanical system of manual provider-based screening with an automated AI-based diagnostic system that processes patient data electronically. This substitution dramatically increases screening capacity while reducing the need for additional healthcare providers and simplifying system complexity.
3Reliability
If traditional in-person psychiatric consultations are conducted, then the diagnostic quality is high, but the accessibility and reach to vulnerable populations is limited
Solution Approach 1:
The AI diagnostic system is designed to be universally applicable across multiple settings including in-person clinics, telehealth platforms, and community-based programs. It can process the same type of data regardless of the delivery mode, making it adaptable to various contexts while maintaining consistent diagnostic quality.
Solution Approach 2:
The patent introduces an AI-based intermediary system that mediates between patient-reported data and professional diagnostic judgment. This intermediary can be deployed in various settings from resource-rich to resource-poor environments, bridging the gap between limited healthcare access and quality diagnostic services for vulnerable populations.
4Measurement precision
If AI models integrate multiple data sources including physiological markers, then the diagnostic precision improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system extracts and processes only the most relevant features from multiple data sources using AI algorithms. Rather than analyzing all raw data, the system identifies and focuses on key diagnostic indicators, reducing processing complexity while maintaining or improving diagnostic precision through targeted analysis.
Data Source
AI summary
Disclosed herein are methods and systems for a training a model for real-time patient diagnosis. A system may include a computer configured to receive audio data and video data of a clinical encounter, the audio data comprising spoken words by an entity and the video data depicting the entity; retrieve clinical data regarding the entity; execute a model using the words of the audio data and the retrieved clinical data regarding the entity as input, the execution causing the model to output a plurality of clinical diagnoses for the entity; concurrently render the corresponding video data and audio data and the plurality of clinical diagnoses via a computing device associated with a user; and store an indication of a selected clinical diagnosis of the plurality of clinical diagnoses responsive to receiving a selection of the clinical diagnosis at the computing device.