AI Subgroup Interface for Telemedicine Patient Monitoring
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Solution Overview
Problem
Current telemedicine systems face challenges in effectively monitoring patient progress and adapting treatment plans remotely, as healthcare professionals cannot physically observe patients and rely on verbal communication, which is inadequate for comprehensive rehabilitation, especially for conditions like cardiovascular, pulmonary, bariatric, and cardio-oncologic health.
Innovation Solution
A computer-implemented method and system using artificial intelligence and machine learning to analyze patient data, including personal, performance, and measurement information, to identify subgroups and generate tailored treatment plans, which are presented via a display interface, allowing for real-time adjustments and control of treatment apparatuses during telemedicine sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If telemedicine systems use verbal communication and limited remote observation to monitor patients, then device complexity is reduced, but measurement precision and reliability of patient progress monitoring deteriorate
Solution Approach 1:
The patent introduces AI/ML algorithms as intermediary systems that process data from multiple sources (wearables, EHRs, patient inputs) to generate comprehensive progress assessments. This mediator layer enables precise monitoring without requiring complex direct observation infrastructure, resolving the contradiction between system simplicity and measurement precision.
Solution Approach 2:
The patent replaces manual/verbal assessment mechanisms with automated AI/ML-based evaluation systems. Instead of relying on healthcare professionals to verbally assess patient progress, the system uses machine learning models to analyze objective data from wearables, electronic health records, and patient self-reports, achieving higher precision without proportionally increasing complexity.
2Ease of operation
If telemedicine systems rely on verbal communication for patient assessment, then ease of operation is improved, but adaptability of treatment plans deteriorates
Solution Approach 1:
The patent implements continuous feedback loops where AI/ML algorithms analyze patient data in real-time and automatically adjust treatment plans. The system processes data from wearables, EHRs, and patient inputs to generate dynamic progress assessments, enabling treatment adaptation without requiring complex real-time verbal interactions. This maintains ease of operation while significantly improving adaptability.
Solution Approach 2:
The system enables self-service treatment adaptation where the AI/ML model autonomously analyzes patient data and modifies treatment protocols without requiring manual intervention from healthcare professionals for each adjustment. This maintains the ease of operation for patients while achieving high adaptability through automated decision-making.
3Device complexity
If telemedicine systems use basic data collection methods, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent merges multiple data collection sources including wearables, electronic health records, patient self-reports, and clinical observations into a unified AI/ML processing system. This integration approach captures comprehensive patient information across multiple dimensions without requiring any single complex data collection device, thereby reducing overall system complexity while preventing information loss through holistic data aggregation.
Solution Approach 2:
The AI/ML platform serves multiple functions simultaneously: data collection, data processing, progress assessment, and treatment planning. This multi-functional universal system eliminates the need for separate specialized devices for each function, reducing overall device complexity while ensuring complete information capture through integrated processing of diverse data types.
Data Source
AI summary
The embodiments set forth a technique implemented by a computing device. The technique includes the steps of (1) receiving one or more characteristics associated with a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, cohort information, familial information, comorbidity information, healthcare professional information, or some combination thereof; (2) determining, based on the one or more characteristics, one or more conditions of the user, wherein the one or more conditions pertain to cardiac health, pulmonary health, bariatric health, oncologic health, cardio-oncologic health, or some combination thereof; (3) based on the one or more conditions, identifying, using one or more trained machine learning models, one or more subgroups to present via the display, wherein the one or more subgroups represent different partitions of the one or more characteristics; and (4) presenting, via the display, the one or more subgroups.