Clinical Data Replication Across Multi-Store Offline Trial Sync
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Solution Overview
Problem
The integrity of data collected during clinical trials for digital therapeutic applications is compromised due to transitions between connected and disconnected states, leading to reduced data validity, inefficient resource allocation, and skewed trial results, especially when large amounts of user interaction data are accumulated offline and sent to remote servers.
Innovation Solution
A data replication system that interfaces with multiple data stores and a central repository, using machine learning to validate data integrity by identifying valid data elements and storing them on the repository, ensuring seamless operation even in offline conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is accumulated offline on user device and sent to server in bulk, then productivity is improved, but data integrity deteriorates
Solution Approach 1:
The patent segments data into multiple data elements and replicates each element across multiple data stores before transmission to the server. This segmentation approach allows the system to maintain data integrity even when bulk data is accumulated offline, as each segmented data element is independently validated and stored in multiple locations.
Solution Approach 2:
The patent performs preliminary data replication and validation actions on the user device before data is transmitted to the server. By replicating data elements across multiple data stores and validating them in advance, the system ensures data integrity is maintained even when large amounts of data are accumulated offline and sent in bulk later.
2Reliability
If multiple data stores are used for data replication, then data integrity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically replicates data elements across multiple data stores and validates them without requiring complex external management. The validation process is automated, with the system independently determining data element validity and managing the replication process, thereby reducing the operational complexity despite the multi-store architecture.
3Measurement precision
If machine learning validation is applied to data elements, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent applies machine learning validation selectively rather than uniformly to all data elements. The system validates data elements that are most critical for clinical trial outcomes with high precision using machine learning, while applying lighter validation to less critical data. This partial action approach maintains measurement precision for key metrics while reducing overall computational energy consumption.
Data Source
AI summary
Provided herein are systems and methods for maintaining integrity of data during clinical trials of digital therapeutic applications. A computing system can receive a data element generated based on interactions by a user with a digital therapeutic application during a clinical trial. The computing system can send the data element to each data store of a plurality of data stores. The computing system can access a first data store to retrieve a first instance of the data element. The computing system can identify the first instance of the data element as invalid. The computing system can access a second data store to retrieve a second instance of the data element, responsive to the identifying the first instance of the data element in the first data store as invalid. The computing system can store the second instance of the data element onto a data repository for the clinical trial.