Coordinated Patient Care Platform for Real-Time Data Streaming
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Solution Overview
Problem
Current telehealth practices lack comprehensive real-time patient data transmission and centralized patient data management, leading to limited information access for healthcare providers and fragmented patient care across different providers.
Innovation Solution
A coordinated patient care platform utilizing wearable medical devices that continuously monitor patient data, integrate artificial intelligence for analysis, and store data centrally with the patient, enabling real-time data streaming and sharing among authorized providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telehealth visits are used to provide convenience and efficiency, then patient access to care is improved, but providers have limited access to patient's current vital signs and recent medical data
Solution Approach 1:
A centralized patient data repository acts as an intermediary between wearable medical devices and healthcare providers. The repository receives data streams from multiple devices and makes them accessible to authorized providers during telehealth visits, eliminating the information gap while maintaining the convenience of remote care.
Solution Approach 2:
The system transitions from episodic data collection (snapshot at visit time) to continuous data collection (real-time streams), adding the dimension of temporal continuity. This allows providers to access historical and current patient data without requiring in-person visits.
2Reliability
If patient data is maintained with each provider, then data security and control are improved, but data cannot be easily accessed by other providers for comprehensive care
Solution Approach 1:
The system segments data access rights by provider and patient authorization levels. Each provider receives tailored data streams relevant to their specific patient interactions, while maintaining a comprehensive centralized repository. This allows selective access that ensures security while enabling coordinated care across multiple providers.
3Quantity of substance
If wearable devices collect data continuously, then comprehensive patient monitoring is improved, but data collection is currently limited to short periods only
Solution Approach 1:
The system enables continuous data collection from wearable medical devices by establishing persistent data streams that transmit patient information indefinitely to the centralized repository. This eliminates the short-term limitation and allows for long-term longitudinal analysis of patient health data.
4Productivity
If real-time data streaming is implemented, then informed decision-making is improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The centralized patient data repository serves multiple functions: it stores historical data, receives real-time streams from various wearable devices, provides data to multiple providers, and maintains security protocols. This multi-functional approach consolidates infrastructure requirements while enabling real-time access for informed decision-making.
Data Source
AI summary
A platform for coordinated patient care is disclosed herein. The platform comprises at least one remote monitoring medical device with at least one sensor and processor for transmitting a patient's biological sensor data in real time. The patient's data may be accessed by a medical provider in real time. The present invention may be used for telehealth visits, short term acute treatment or for long term monitoring of chronic disease. The biological data that may be collected includes vital signs, such as the patient's heart rate, blood oxygen levels, blood pressure, and temperature. Methods for use of the medical device are also disclosed herein, the methods comprising real-time transmission and storage of a patient's data streams to a medical provider for the diagnosis and treatment of a medical condition. Methods disclosed herein may also include artificial intelligence and machine learning models to train on the patient's data to interpret immediate sensor readings and predict future medical outcomes for the patient.


