Electronic Patient Record Access Control With Dynamic Data Masking
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Solution Overview
Problem
Existing methods for managing access to electronic patient records with personally identifiable information (PII) and protected health information (PHI) are manual, prone to human error, and inefficient, leading to potential data leakage and inadequate sharing control.
Innovation Solution
Implementing automated data classification combined with dynamic database policies and stored procedures to manage access control, allowing selective masking or encryption of data based on entity roles and data classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review by a specialist is used to determine data sharing, then data security can be maintained through human judgment, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated system using machine learning models and database stored procedures. The system automatically classifies data elements, determines sharing eligibility, and applies appropriate masking rules without human intervention, thereby eliminating time loss while maintaining security through algorithmic decision-making.
Solution Approach 2:
The system enables self-service automation where the database automatically reviews and classifies data tables upon insertion or update. The stored procedures autonomously determine which data can be shared and apply appropriate access controls without requiring specialist intervention, making the process efficient and scalable.
2Measurement precision
If manual data classification is performed, then accurate identification of PII and PHI can be achieved, but human error increases the risk of misclassification
Solution Approach 1:
The patent replaces human specialists with machine learning models that automatically classify data elements. These models consistently apply classification rules without fatigue or distraction, eliminating human errors such as mis-marking data types or providing unredacted tables to wrong persons, thereby improving both accuracy and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from classified data and adjusts its classification accuracy. The automated review process provides consistent feedback on data patterns, enabling the system to improve its classification precision over time while maintaining reliable error-free operation.
3Reliability
If data tables are restricted from view to prevent potential leakage, then data security is maintained, but useful data sharing opportunities are lost
Solution Approach 1:
The patent applies different access qualities to different data elements within the same table. Instead of restricting entire tables, the system identifies specific PII and PHI elements and applies selective masking rules. This allows non-sensitive data to be shared freely while protecting only the sensitive portions, thereby maximizing data sharing productivity without compromising security.
Solution Approach 2:
The system dynamically determines access rights based on the specific data content and the requesting entity's role. The stored procedures automatically adjust the level of masking applied to each data element, enabling flexible data sharing where appropriate while maintaining security where needed, thus improving overall data sharing efficiency.
4Productivity
If automated data classification is implemented, then processing efficiency increases, but system complexity increases
Solution Approach 1:
The patent segments the automated classification system into distinct modular components: machine learning models for data element classification, database stored procedures for access control logic, and masking rules for data transformation. This segmentation allows each component to be developed, maintained, and optimized independently, managing system complexity while maintaining high processing efficiency.
Data Source
AI summary
Systems and methods herein provide for access control to information in electronic patient records. One method includes receiving a request from an entity for access to one or more of a plurality of electronic patient records, the records having been machine learned to identify patient information in the electronic patient records including personally identifiable information and protected health information. The method also includes determining a level of access of the entity, retrieving, from a database, the one or more electronic patient records requested by the entity, applying a rule to the retrieved one or more electronic patient records based on the determined level of access of the entity to mask, encrypt, show, etc. one or more elements in the one or more electronic patient records. In response to applying the rule to the retrieved one or more electronic patient records, the electronic patient records are transferred to the entity.