Distributed Healthcare Data Management for Personalized Patient Services
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Solution Overview
Problem
Healthcare entities face pressure to improve service efficiency and effectiveness without increasing costs, necessitating a more integrated and efficient management system for patient data and services.
Innovation Solution
An intelligent healthcare management system utilizing a distributed computing system with integrated modules for data collection, analysis, and management, including an internet-of-things platform server, business server, physiological parameter data analysis platform, business-end management platform, and customer-end personal data management platform, enabling seamless data exchange and personalized healthcare services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional healthcare management systems are used, then implementation costs are lower, but service efficiency and effectiveness remain insufficient
Solution Approach 1:
The system divides healthcare management into distinct functional modules: patient registration module, physiological parameter monitoring module, data analysis module, and treatment management module. Each module operates independently but communicates through standardized interfaces, enabling improved service efficiency without proportionally increasing overall system complexity.
Solution Approach 2:
The platform is designed as a universal healthcare management system that can serve multiple functions: patient registration, real-time physiological monitoring, data analysis, treatment planning, and family member management. This multi-functionality allows a single system to address various healthcare needs, improving productivity without requiring separate specialized systems for each function.
2Adaptability or versatility
If comprehensive patient data collection and analysis is implemented, then personalized healthcare services are improved, but data management complexity increases
Solution Approach 1:
The system introduces a centralized data management platform that acts as an intermediary between various data collection devices (wearables, medical instruments) and analysis applications. This platform standardizes data formats, handles data storage, and provides unified access interfaces, enabling personalized healthcare services while managing data complexity centrally rather than distributing it across multiple components.
Solution Approach 2:
The system transforms raw physiological parameter data into meaningful health indicators through automated analysis algorithms. By changing data parameters from raw measurements to interpreted health metrics, the system enables personalized service capabilities while reducing the complexity of managing raw data volumes.
3Reliability
If real-time physiological parameter monitoring is implemented, then patient management quality is improved, but operational costs increase
Solution Approach 1:
The system enables patients to self-monitor their physiological parameters using wearable devices and mobile applications, reducing the need for continuous professional medical intervention. Automated algorithms analyze the collected data and generate health assessments, allowing the system to maintain high patient management quality while reducing operational costs associated with manual monitoring.
Solution Approach 2:
The system implements automated feedback loops where physiological data is continuously collected, analyzed, and used to generate real-time health recommendations. This automated feedback mechanism improves patient management quality by providing timely interventions while reducing operational costs by eliminating the need for continuous professional review of all data.
Data Source
AI summary
An intelligent healthcare management system is provided. The intelligent healthcare management system includes a distributed computing system including one or more networked computers configured to execute in parallel to perform at least one common task; and one or more computer readable storage mediums storing instructions that, when executed by the distributed computing system, cause the distributed computing system to execute software modules. The software modules includes an internet-of-things platform server; a business server; a physiological parameter data analysis platform configured to provide one or more user interfaces for point-of-care physiological parameter data collection; a business-end management platform configured to store and manage historical physiological parameter data of customers associated with a business entity; and a customer-end personal data management platform configured to manage personal historical physiological parameter data specific for an individual customer. The internet-of-things platform server and the business server are configured to exchange data between each other.