Physiologic Event Data Prioritization for Faster Clinical Review
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Solution Overview
Problem
The challenge in patient management systems is the substantial time and resource consumption required for human review of large volumes of device-recorded physiologic event data, including false positive detections and the difficulty in identifying data portions of clinical significance, which can lead to inefficient use of medical resources and potential inappropriate therapies.
Innovation Solution
Implementing a system for automatic recognition and prioritized presentation of data subsets of clinical significance using event-specific signal metrics, allowing for efficient adjudication of physiologic events without modifying existing patient medical devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually review all device-recorded physiologic event data, then complete clinical assessment is achieved, but substantial time and human resources are consumed
Solution Approach 1:
The system performs preliminary automated analysis of physiologic event data before clinical review, pre-identifying significant events and generating summaries. This preliminary processing filters and organizes data so clinicians only need to review pre-processed highlights rather than raw data, reducing review time while maintaining assessment quality
Solution Approach 2:
An automated event detection and prioritization system acts as an intermediary between the implantable device and the clinician. This intermediary processes raw physiologic data, applies detection algorithms, prioritizes events by clinical significance, and presents filtered information to clinicians, thereby reducing their time burden while preserving clinical accuracy
2Loss of information
If all physiologic event data is presented to clinicians, then no important information is missed, but data volume becomes unmanageably large
Solution Approach 1:
The system extracts and isolates only the clinically significant portions of physiologic event data using automated detection algorithms. By taking out and separating meaningful events from the bulk of routine or non-significant data, the system presents a manageable subset to clinicians that contains all essential diagnostic information without the overwhelming volume of raw data
Solution Approach 2:
The system applies different processing and presentation qualities to different portions of data based on their clinical significance. High-priority events receive detailed presentation with full data visibility, while low-priority events are summarized or grouped, creating a locally optimized data presentation that adapts to the informational needs of each data segment
3Productivity
If automated detection algorithms are used to filter data, then review efficiency increases, but false positive detections may occur
Solution Approach 1:
The system incorporates feedback loops where clinician adjudications of automated detections are fed back to refine and adjust detection algorithms. This continuous learning process improves algorithm accuracy over time, reducing false positives while maintaining high sensitivity, thereby simultaneously improving both efficiency and reliability
4Reliability
If clinicians spend extensive time reviewing data to avoid false positives, then detection accuracy improves, but resource consumption increases
Solution Approach 1:
The system performs preliminary automated filtering and prioritization that pre-resolves many potential false positive cases before they reach the clinician. By applying detection algorithms and confidence scoring in advance, the system presents only the most uncertain or significant cases for clinical review, allowing clinicians to achieve high accuracy without examining every data point
Data Source
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AI summary
Systems and methods for presenting physiologic data to a user are discussed. An exemplary system includes a presentation control circuit configured to generate signal metrics from data subsets of a physiologic signal corresponding to a device-detected presence of a physiologic event. The signal metrics represent characteristics of the physiologic event. The presentation control circuit may determine, from the plurality of data subsets, a target subset of clinical significance, which has the corresponding signal metric satisfying a specific condition. The presentation control circuit may present the recognized target subset over other subsets of the physiologic data. A user may adjudicate the device detection of the physiologic event or adjust device parameters.