Conversational AI Health Guidance From Decentralized EHR Data
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Solution Overview
Problem
Current healthcare systems lack effective methods for engaging patients in lifestyle modifications and preventive care, particularly for chronic conditions, and there is a need for personalized and decentralized communication of health-related data using AI and NLP to address lifestyle-related health risks.
Innovation Solution
A decentralized and distributed system utilizing AI and NLP, integrated with electronic health records, wearable devices, and questionnaires, provides personalized health insights and recommendations through a conversational avatar named 'Jill', offering proactive care and prescription management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional healthcare communication methods are used, then patient engagement in lifestyle modifications is limited, but implementing AI and NLP systems increases device complexity and data integration requirements
Solution Approach 1:
The patent introduces an AI-powered virtual health coach as an intermediary between patients and healthcare providers. This virtual coach uses NLP to communicate with patients in natural language, making health guidance more accessible and engaging while shielding patients from the underlying system complexity. The intermediary translates complex medical data into actionable, personalized recommendations that patients can easily follow.
Solution Approach 2:
The system enables patients to proactively manage their own health by providing them with autonomous access to their health data, personalized recommendations, and lifestyle guidance through the AI virtual coach. Patients can independently review their health metrics, receive tailored advice on lifestyle modifications, and track their progress without requiring direct intervention from healthcare providers for every decision, thus improving engagement while reducing operational complexity.
2Adaptability or versatility
If decentralized data retrieval from multiple sources is implemented, then personalized health insights are improved, but data integration and verification complexity increases
Solution Approach 1:
The patent segments the health data ecosystem into distinct modular components: electronic health records from providers, data from wearable devices, patient-reported outcomes from mobile apps, and lifestyle information. Each data source connects to the AI system through standardized, pre-built interfaces. This segmentation allows the system to retrieve and integrate personalized health data from multiple sources without creating a monolithic integration challenge, as each module can be developed and maintained independently.
3Measurement precision
If comprehensive health data from multiple sources is aggregated, then health assessment accuracy is improved, but data security and privacy management complexity increases
Solution Approach 1:
The patent implements local quality control by applying different security and privacy protection measures to different types of health data based on their sensitivity and regulatory requirements. Critical personal identifiers receive encryption and access controls, while less sensitive lifestyle data can be processed with lighter protection. This differentiated approach maintains high security standards for sensitive information while reducing unnecessary complexity for less critical data, enabling comprehensive data aggregation with manageable security overhead.
Data Source
AI summary
Embodiments of the present disclosure may include a method of mapping patient data and representing data from electronic health records of an individual through a pictorial representation of their human body including receiving, over at least one communication network from each of a plurality of user computing devices operated by each of a plurality of users, electronic health records (EHR) respectively representing information from the plurality of user's electronic medical records, procured from the user's health care provider or the user's electronic health records (EHR) partners. Embodiments may also include providing, a decentralized and distributed patient facing method of communicating and providing robust critical formulation of health-related clinical data of a patient using artificial intelligence (AI) and natural language processing (NLP) to engage a patient that encourages a change in a patient's behavior to improve the patient's overall health.