Remote Computing Device for Health Event Data Filtering
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Solution Overview
Problem
Medical systems face challenges in accurately distinguishing between true and false health events, leading to incorrect data that can negatively impact patient care.
Innovation Solution
The implementation of a medical system that includes an implantable medical device and a remote computing device, which analyzes health event data to identify false detections and removes them from patient records, thereby improving the accuracy of diagnostic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the medical device monitors and records all detected health events, then the quantity of diagnostic data increases, but the accuracy of the diagnostic record deteriorates due to inclusion of false detections
Solution Approach 1:
The system extracts and removes false detection events from the recorded health event data. The remote computing device compares detected health events against patient-specific criteria and removes events that do not meet the criteria, thereby extracting only the relevant true positive events from the complete dataset.
Solution Approach 2:
The system performs preliminary filtering of health events by storing patient-specific criteria in advance and using these criteria to pre-screen detected events before final diagnostic review. This preliminary action prevents false detections from being included in the final diagnostic record.
2Productivity
If the medical system uses algorithms to detect health events, then the productivity of health monitoring increases, but the reliability of detection deteriorates due to false positives
Solution Approach 1:
The system implements feedback by comparing algorithm-detected health events against stored patient-specific criteria and using the results to refine future detections. The remote computing device provides feedback by identifying false positives and adjusting or removing detections based on criterion mismatches, thereby improving reliability while maintaining productivity.
3Loss of information
If the medical device records comprehensive patient data for future analysis, then the information completeness improves, but the data quality deteriorates due to inclusion of false health events
Solution Approach 1:
The system segments the health event data into true positive events and false positive events based on comparison with patient-specific criteria. By dividing the complete dataset into these segments, the system preserves comprehensive information while isolating and removing the low-quality false detections.
Data Source
AI summary
This disclosure is directed to systems and techniques configured to apply at least one criterion to health event data stored in a record for a patient for determining whether to remove at least a portion of the health event data from the record or retain that portion as an accurate reflection of patient health for that point-in-time. The health event data includes adjudicated health events and non-adjudicated health events over a first time period. Based on a determination that the health event data satisfies the at least one criterion, the example technique may direct the example system to remove the health event data corresponding to the adjudicated health events and the non-adjudicated health events from the record and then, adjust longitudinal diagnostic information of a second time period that includes the first time period.