Medical Information Anonymization via Idiosyncratic Data Detection

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Solution Overview

Problem

Conventional anonymization processing struggles to effectively delete idiosyncratic information, requiring manual judgment by users, which is inefficient and prone to errors, especially after the amendment of the Personal Information Protection Law.

Innovation Solution

A medical information management system that automates the anonymization process by using a processing circuitry with search, determination, and anonymization functions to identify and delete personal information, employing threshold-based matching rates and essential key settings to determine and handle idiosyncratic data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional anonymization processing is used, then basic personal information can be removed, but idiosyncratic information cannot be effectively deleted requiring manual judgment

Engineering Contradiction:

Improveanonymization processing automationVSAvoidmanual judgment burden

Core Design Contradiction:

Extent of automationVSEase of operation

Solution Approach 1:

The system enables self-service anonymization by automatically detecting and deleting idiosyncratic information without requiring user intervention. The determination unit autonomously identifies idiosyncratic descriptions by comparing medical information against anonymization templates and statistical thresholds, eliminating the need for manual judgment while maintaining high accuracy in removing identifying information.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical judgment with an automated information processing system. The determination unit uses computational algorithms to analyze medical information, compare it with anonymization templates, calculate matching rates, and automatically delete idiosyncratic information based on predetermined thresholds, substituting human cognitive processes with machine-based automated decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If manual judgment is performed for idiosyncratic information deletion, then accuracy can be maintained, but processing efficiency decreases significantly

Engineering Contradiction:

Improveanonymization processing efficiencyVSAvoididiosyncratic information detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system uses parameter-based automated detection by calculating matching rates between medical information and anonymization templates, comparing these rates against predetermined thresholds to determine whether information is idiosyncratic. This quantitative parameter-based approach maintains detection accuracy while enabling high-speed automated processing of large volumes of medical data.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The determination unit incorporates feedback mechanisms by continuously comparing medical information against anonymization templates, calculating matching rates, and using threshold comparisons to make automated decisions. This feedback loop ensures accurate identification of idiosyncratic information while maintaining efficient automated processing throughput.

Inventive Principle:

Principle #23Feedback

3Reliability

If anonymization processing is insufficient, then processing speed can be maintained, but personal information protection is compromised

Engineering Contradiction:

Improvepersonal information protection reliabilityVSAvoidanonymization processing throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-defining anonymization templates with multiple fields and predetermined deletion thresholds before processing medical information. This preparation enables the determination unit to quickly compare incoming data against established criteria and automatically delete idiosyncratic information, ensuring reliable personal information protection without sacrificing processing speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the anonymization process into distinct functional units: the determination unit that identifies idiosyncratic information using template matching and threshold comparison, and the deletion unit that removes identified information. This segmentation allows each unit to specialize in its function, maintaining high reliability in personal information protection while achieving efficient automated processing throughput.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11948669B2Medical information management apparatus and medical information management system

Publication Date: 2024.04.02 CANON MEDICAL SYST CORP

AI summary

A medical information management apparatus has processing circuitry configured to search medical information in a storage, when accepting a search request of the medical information including a key being an item of information regarding a patient and a value being contents of the key, and extract the medical information matching a search condition as condition-matched medical information; extract the key and the value from the condition-matched medical information, calculate a matching rate being a proportion of the value included in the condition-matched medical information to the entire medical information in the storage, and determine that the value is idiosyncratic information when the matching rate is equal to or less than a threshold set with respect to the key; and determine whether the key of the value determined to include the idiosyncratic information corresponds to an essential key which is essential in terms of a purpose of utilization.