Patient-Triggered Episode Prioritization via Confidence and Alignment Scoring
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Solution Overview
Problem
Current patient management systems face challenges in efficiently managing and prioritizing large volumes of alert notifications from ambulatory medical devices, leading to resource-intensive and costly processes, as patient-triggered episodes may not always accurately predict medical events.
Innovation Solution
A system comprising an event analyzer circuit to determine a confidence score and alignment indicator for medical events, and an event prioritizer circuit to assign priority information based on these metrics, allowing for the efficient ranking and presentation of patient-triggered episodes to clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all patient-triggered episodes are reviewed by clinicians, then diagnostic yield may increase, but time and resource consumption increases significantly
Solution Approach 1:
An automated event prioritization system acts as an intermediary between the medical device and clinician. The system receives physiological data and patient-triggered episodes from the device, automatically analyzes them using predefined criteria (alignment indicators, confidence scores), and prioritizes events before presenting to clinicians. This intermediary filtering layer reduces the volume of episodes requiring clinical review while maintaining high diagnostic yield by preserving truly significant events.
Solution Approach 2:
The system performs preliminary analysis and prioritization of patient-triggered episodes before clinician review. By pre-processing the data stream, calculating alignment indicators, determining confidence scores, and ranking events in advance, the system prepares optimized information for clinicians, eliminating the need for them to manually evaluate all raw episodes and significantly reducing their time investment.
2Measurement precision
If patient-triggered episodes are used to predict medical events, then diagnostic benefits increase, but false alerts increase reducing reliability
Solution Approach 1:
The system incorporates feedback mechanisms where clinician adjudication of prioritized events feeds back into the prioritization algorithm. When clinicians review and validate or reject prioritized events, this information is used to refine and adjust the alignment indicators and confidence score calculations, continuously improving the system's ability to distinguish true medical events from false alerts while maintaining high diagnostic benefits.
3Measurement precision
If manual review of all medical events is performed, then accuracy of event assessment is maintained, but productivity decreases
Solution Approach 1:
The system segments the large volume of patient-triggered episodes into prioritized groups based on automated analysis. By dividing the data stream into high-priority, medium-priority, and low-priority categories using alignment indicators and confidence scores, the system enables clinicians to focus their detailed review on high-priority events that require accurate assessment, while low-priority events are efficiently processed or filtered, thereby maintaining accuracy for critical events while dramatically improving overall productivity.
Data Source
AI summary
Systems and methods for managing machine-generated medical events detected from one or more patients are described herein. A medical event management system includes an event analyzer circuit to detect a medical event using physiological data from a patient-triggered episode acquired from a medical device. The event analyzer circuit determines a confidence score of the medical event detection, and generates an alignment indicator indicating a degree of concordance between the detected medical event and the information about the patient-triggered episode. The system assigns priority information to the patient-triggered episode using the generated alignment indicator and the confidence score of the detection. An output circuit can output the received physiological information to a user or a process according to the assigned priority information.