Operating Room Microphone Audio De-Identification Using Machine Learning
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Solution Overview
Problem
Existing computer-assisted surgery systems fail to adequately de-identify audio data captured in operating rooms, potentially compromising patient and healthcare provider privacy by retaining identifiable information.
Innovation Solution
A computer-implemented method using machine learning systems to detect and remove patterns in audio data from microphones that could identify specific entities, such as patients or healthcare providers, by generating de-identified data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If audio data is captured and stored for archival and analysis purposes, then the utility and value of the data increase, but patient and provider privacy is compromised due to retained identifiable information
Solution Approach 1:
The system extracts and removes identifiable information from audio data using machine learning models. The de-identification process specifically targets and extracts personally identifiable information (PII) such as names, while preserving the diagnostic and training value of the remaining audio content.
Solution Approach 2:
Machine learning models serve as an intermediary between raw audio data and stored de-identified data. These models automatically detect, redact, and replace identifiable information with placeholders or generic terms, enabling safe storage and sharing of audio data without direct human intervention in the de-identification process.
2Object-affected harmful factors
If manual de-identification methods are used to remove identifiable information, then privacy protection is achieved, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system replaces manual mechanical de-identification processes with automated machine learning models. These models automatically analyze audio data, detect identifiable information patterns, and perform redaction without human intervention, reducing processing time from hours or days to minutes while maintaining or improving de-identification accuracy.
Solution Approach 2:
The de-identification system performs self-service by automatically processing audio data through trained machine learning models that detect and redact identifiable information without requiring manual review. The system handles the entire de-identification workflow autonomously, from input processing to output generation.
3Object-affected harmful factors
If traditional de-identification approaches are used, then some identifiable information is removed, but subtle patterns and contextual information that could still identify entities are not detected
Solution Approach 1:
The system changes the parameter of analysis from simple keyword matching to complex pattern recognition using machine learning. The models analyze contextual patterns, speech characteristics, and linguistic features that traditional methods miss, significantly improving detection of subtle identifying information while preserving diagnostically relevant content.
Solution Approach 2:
The de-identification system uses a composite approach combining multiple machine learning models and analysis techniques. Different models detect different types of identifiable information (names, locations, contextual patterns), and their results are combined to achieve comprehensive de-identification that captures subtle patterns missed by single-method approaches.
Data Source
AI summary
An aspect includes a computer-implemented method that de-identifies data received from microphones. The method includes receiving data from one or more microphones and de-identifying the data. The de-identifying includes inputting the data into a machine learning system that has been trained to detect patterns in the data that are likely to identify a specific entity, and to remove the detected patterns from the data to generate de-identified data. An output from the machine learning system is received, where the output includes the de-identified data. According to some aspects, the microphones can be located in an operating room.