Automated Clinical Data Completeness Verification for Implantable Blood Pumps
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Solution Overview
Problem
Existing implantable blood pumps face challenges in ensuring the completeness of data extraction, leading to incomplete data transmission and delayed analysis, as manual inspection is required to detect missing data after transmission.
Innovation Solution
An automated method and apparatus that evaluate the completeness of clinical data from implantable devices like blood pumps by comparing timestamp data associated with continuously and intermittently logged data, issuing alerts for incomplete data and facilitating complete data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to verify data completeness after transmission, then data completeness can be detected, but analysis is delayed and additional manual work is required
Solution Approach 1:
The patent applies preliminary action by performing data completeness verification before data transmission. The system calculates expected data volume based on stored metadata (patient count, event count, waveform count, etc.) and compares it with the actual transmitted data volume. This pre-transmission check ensures complete data extraction and eliminates the need for post-transmission manual inspection, thereby preventing analysis delays.
2Reliability
If automated verification is implemented before transmission, then data completeness is ensured, but system complexity increases
Solution Approach 1:
The patent uses copying by maintaining a separate metadata structure that mirrors the expected data organization. Instead of complex real-time verification during extraction, the system creates a copy of the expected data inventory (patient data, event data, waveform data, etc.) and compares it with the actual extracted data. This metadata copying approach simplifies the verification mechanism while ensuring reliability.
Solution Approach 2:
The patent introduces an intermediary metadata layer that mediates between the raw clinical data and the verification process. The metadata contains pre-calculated counts and expected volumes for different data types, serving as an intermediary reference that simplifies the comparison process. This intermediary structure eliminates the need for complex direct verification of raw data while maintaining high reliability.
3Measurement precision
If continuous monitoring of data extraction is performed, then completeness can be verified in real-time, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary calculation of expected data volumes during the data logging phase, storing this metadata for later verification. This preliminary action allows the verification process to simply compare pre-calculated values with actual transmitted data, avoiding the need for computationally intensive real-time analysis during data extraction.
Solution Approach 2:
Instead of verifying every single data point during extraction, the system performs a partial verification by checking the aggregate metadata (total patient count, event count, waveform count, expected data volume). This partial verification approach provides sufficient confidence in data completeness while consuming minimal computational resources.
Data Source
AI summary
An automated method for evaluating completeness of clinical data being transferred from a clinical device. The clinical data includes continuously logged data, intermittently logged data, and timestamp data associated with each of the continuously logged data and intermittently logged data. The method is executed by a processor and includes receiving the clinical data from the clinical device and based on the timestamp data associated with the continuously logged data, determining a time span over which the continuously logged data was obtained. The timestamp data associated with the intermittently logged data is compared with the determined time span. If any timestamp data associated with the intermittently logged data does not occur within the determined time span, an alert is issued indicating that the continuously logged data of the clinical data is incomplete.


