Patient-Controlled Health Record Sync Across Provider Data Silos
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Solution Overview
Problem
Patient medical records are fragmented across different healthcare providers and siloed, leading to incomplete data, increased risk of medical errors, repeated testing, and high healthcare costs, with current integration efforts focusing on facility-to-facility connectivity and lacking patient control.
Innovation Solution
A patient-controlled electronic medical record (PEMR) system that integrates holistic health data, using secure distributed storage and neural gateways, allowing synchronization with healthcare providers through encrypted authentication tokens, ensuring patient control and reducing data transfer risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data are stored in silos across different healthcare providers, then each provider can maintain unique patient databases using their own EMR software, but the data become fragmented and never consolidated in a single data structure
Solution Approach 1:
The system segments patient data into a distributed architecture where each healthcare provider maintains local access to their own EMR data while a centralized data lake consolidates all data. This allows provider autonomy in managing their specific records while ensuring comprehensive data consolidation across the entire healthcare system.
Solution Approach 2:
A centralized data lake serves as an intermediary between disparate provider EMR systems. It receives data from all providers, consolidates it into a unified structure, and makes it available to all providers without requiring direct integration between each provider's system, thus resolving the fragmentation issue while preserving provider independence.
2Ease of operation
If data remain bound to HCPs and stored in their EMR systems, then providers can access patient information locally, but the data are not centered upon or controlled by the patient
Solution Approach 1:
Instead of providers centrally controlling and storing patient data in their EMR systems, the system inverts this model by placing the patient at the center. The patient's data are consolidated in a centralized data lake that the patient controls, and providers access this consolidated data through standardized interfaces, reversing the traditional power dynamic in data management.
3Loss of information
If comprehensive patient data from multiple sources are consolidated, then complete patient history and lifestyle information become available, but the system complexity increases
Solution Approach 1:
The system extracts and isolates data from various complex sources (provider EMR systems, wearable devices, mobile apps, social media) and consolidates them into a standardized format in the centralized data lake. This extraction process simplifies the overall system architecture by creating a uniform data structure that can be easily queried and accessed by all providers without requiring them to understand the complexity of individual data sources.
Data Source
AI summary
A system and method that facilitates the automated replication of electronic medical record information between a patient and a health-care provider (HCP), such as a doctor, pharmacy, drug manufacturer, biologic manufacturer, or medical device manufacturer. The system uses: a cloud-based infrastructure that includes databases, mathematical models, and configuration information; a patient's electronic health record system providing personal data around a patient's individual personal electronic medical record (PEMR); and a server used to coordinate and authenticate the replication of data between the cloud-based infrastructure, the PEMR, and the EMR/EHR system of the HCP. The system provides support and security, such as by geographically distributed data fragmentation, for mobile platforms and web-based platforms and sophisticated mechanisms for the transmission of data between these systems.