Clinical Trial Data Security via Real-Time Risk Analysis
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Solution Overview
Problem
Clinical trials face challenges in managing and securing sensitive patient data while minimizing the impact on data processing efficiency, requiring a comprehensive and effective data privacy solution.
Innovation Solution
A clinical trial network utilizing digital data processors, user interfaces, and databases with a workflow that includes patient data attribute classification, risk weighting, and real-time privacy analysis using natural language processing and semantic keyword extraction to anonymize and protect patient data, allowing for flexible and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive data security measures are implemented to protect sensitive patient data, then data privacy is improved, but data processing efficiency deteriorates
Solution Approach 1:
The system performs preliminary classification of data attributes and pre-computation of risk weightings during data ingestion, before actual data processing occurs. This allows security measures to be pre-configured and applied efficiently during subsequent processing operations, reducing the computational burden in real-time operations.
Solution Approach 2:
The system implements automated risk assessment and dynamic security policy application without requiring manual intervention for each data processing operation. The automated classification and risk weighting mechanisms enable the system to self-regulate security measures based on the sensitivity and context of the data being processed.
2Reliability
If real-time privacy analysis is performed on patient data, then data anonymization is improved, but processing time increases
Solution Approach 1:
The system pre-identifies and classifies sensitive data attributes and pre-computes risk weightings for different data types. This preliminary analysis creates a framework that enables faster real-time privacy decisions without performing complete re-analysis of all data attributes during processing.
Solution Approach 2:
The system applies different levels of privacy analysis and security measures to different data attributes based on their classified sensitivity and risk weightings. High-risk attributes receive more rigorous analysis while low-risk attributes receive streamlined processing, optimizing the balance between anonymization quality and processing speed.
3Reliability
If strict data filtering is applied to protect sensitive information, then data security is improved, but data utility for clinical research deteriorates
Solution Approach 1:
The system dynamically adjusts security measures and filtering strictness based on computed risk weightings and contextual parameters. Rather than applying uniform strict filtering, the system modulates security intensity according to the sensitivity classification and risk assessment of each data element, preserving utility for lower-risk data while maintaining security for high-risk data.
Solution Approach 2:
The system implements dynamic security policies that adapt to the specific context of each data processing operation. Security measures are adjusted in real-time based on the classified attributes, risk weightings, and the purpose of data processing, allowing flexible balancing of security and utility requirements.
Data Source
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AI summary
A clinical trial support network (1) has data processors (3, 10-15) which perform patient data attribute classification during clinical trial setup (120) by identifying attributes which are sensitive, the attributes being instantiated as terms in clinical trial records. For each identified attribute a risk value or weighting associated with the attribute is stored (124, 135). After this initialization, in real time a potential data posting is received, such as from a patient using an online portal or from a doctor managing a clinical trial group. The data processors perform real time privacy analysis (503) for the potential data posting, by using the risk values of attributes for which there are terms in the potential posting, to determine an overall risk level for the potential data posting. Once the analysis is performed the potential posting is transmitted or posted according to the result: blocked (511), full publication (508), or partial publication (507). The data processors, when executing algorithms to analyse a potential posting, use additional criteria including term frequency in the posting, count of instances of each term in the posting, and a total count of instances of terms which are potentially sensitive.