Medical Device Data De-Identification for Rescue Event Trend Analysis
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Solution Overview
Problem
Current systems lack efficient methods for large-scale collection and analysis of medical device use and operational data due to the privileged nature of the data, making it difficult to identify misuse and optimize device performance, especially in emergency situations involving rare cardiac events or conditions.
Innovation Solution
Medical devices are configured to de-identify data at the device level, encrypting it with a unique encryption key before transmitting it to an external computing device for analysis, allowing for the collection and analysis of trends in device use and performance without exposing personally identifiable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If medical device data is collected and analyzed at scale, then insights into device misuse and performance can be identified, but patient privacy and data security are compromised
Solution Approach 1:
The patent extracts personally identifiable information (PII) from medical device data before analysis. A de-identification system removes or generalizes sensitive patient identifiers while preserving clinically relevant data patterns, enabling large-scale analysis without compromising patient privacy. This extraction approach separates useful analytical data from sensitive personal information.
Solution Approach 2:
The patent introduces an intermediary de-identification layer between data collection and analysis processes. This intermediary system transforms raw medical device data into anonymized datasets that can be analyzed at scale while acting as a protective barrier against patient privacy exposure. The intermediary maintains data utility for research while eliminating direct patient identification capabilities.
2Reliability
If manual data gathering processes are used, then data security is maintained, but the process is costly and time-consuming
Solution Approach 1:
The patent implements self-service automated de-identification systems that operate without manual intervention. The system automatically detects, removes, and generalizes PII elements from medical device data streams in real-time, eliminating the need for manual data review while maintaining security standards. This automation dramatically reduces time and cost while preserving security through programmed privacy protocols.
Solution Approach 2:
The patent replaces manual mechanical data review processes with automated computational de-identification systems. Machine learning algorithms and automated data processing tools substitute human analysts, enabling rapid secure data processing at scale while maintaining consistent privacy protection standards that are difficult to achieve through manual processes alone.
3Object-affected harmful factors
If de-identification is performed at device level with encryption, then patient privacy is protected, but data processing complexity increases
Solution Approach 1:
The patent performs de-identification and encryption as preliminary actions at the point of data generation on the medical device itself. By removing PII and applying encryption before data leaves the device, the system protects privacy early in the data lifecycle, preventing sensitive information from entering complex transmission and storage channels. This preliminary protection simplifies downstream processing while maintaining security.
Solution Approach 2:
The patent transforms data parameters by changing the state of information from identifiable to anonymized through systematic parameter modifications. The system applies consistent transformation rules that convert specific patient identifiers into generalized categories or pseudonyms, maintaining data analytical value while fundamentally changing the privacy parameters of the dataset.
Data Source
AI summary
An example method includes generating, at a medical device, first data comprising (i) an identifier of a subject being monitored or treated by the medical device, (ii) data indicating a state of the medical device during a rescue event, (iii) data indicating a user of the medical device during the rescue event, and storing the first data in a memory associated with the medical device. The example method further includes generating second data by de-identifying the first data and transmitting the second data to an external device configured to receive the de-identified data from a fleet of medical devices and identify a trend associated with the de-identified data using a computing model(s).