Cardiac Event Rate Limiter Using Bucket-Based Filtering
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Solution Overview
Problem
Mobile cardiac monitoring systems often overwhelm healthcare labs with redundant data from frequent cardiac events of similar severity, leading to inefficient clinical reviews and potential delays in patient care.
Innovation Solution
A cardiac event server uses machine learning to assign cardiac events to buckets based on heart rate, duration, and beat count, limiting the number of events communicated for clinical review by enforcing quotas and considering the time of events, thereby reducing clinically redundant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all cardiac events are communicated for clinical review, then no clinically significant events are missed, but lab efficiency decreases due to redundant data volume
Solution Approach 1:
The patent segments cardiac events into different severity categories (e.g., high, medium, low severity) based on clinical significance criteria. This segmentation allows the system to prioritize which events require immediate clinical review and which can be monitored automatically, thereby improving lab efficiency while ensuring that clinically significant events are not missed.
Solution Approach 2:
The patent applies different review policies to different types of cardiac events based on their local characteristics (severity, clinical significance). High-severity events receive immediate clinical review attention, while low-severity events are handled through automated monitoring. This local quality approach ensures that clinical resources are focused on the most critical events.
2Reliability
If frequent cardiac events are monitored and reviewed, then patient safety is improved, but unnecessary reviews increase causing delays
Solution Approach 1:
The patent performs preliminary filtering and prioritization of cardiac events before they reach clinical reviewers. By pre-processing events to identify and flag only those with high clinical significance, the system prepares the data in advance, ensuring that patient safety is maintained while preventing unnecessary reviews that would cause delays.
Solution Approach 2:
The patent introduces an intermediary automated filtering system between the cardiac event detection and clinical review processes. This intermediary layer analyzes events using predefined criteria to determine clinical significance, acting as a mediator that ensures patient safety by catching critical events while filtering out routine events that would otherwise create delays.
3Reliability
If comprehensive cardiac monitoring is implemented, then patient care quality is improved, but data redundancy increases overwhelming healthcare labs
Solution Approach 1:
The patent extracts and removes redundant data from the comprehensive cardiac monitoring output by applying filtering criteria that identify and eliminate duplicate or clinically insignificant events. This extraction process retains only the essential, clinically relevant information, maintaining patient care quality while reducing the data volume overwhelming healthcare labs.
Solution Approach 2:
The patent discards redundant cardiac event data that does not meet clinical significance thresholds while recovering and preserving events that do meet the thresholds. This selective discarding and recovering process maintains the quality of patient care by ensuring important events are retained while eliminating the overwhelming volume of redundant data.
Data Source
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AI summary
A method includes assigning a first instance of a cardiac event that occurred in a patient during a period of time to a first bucket of a first plurality of buckets based on a first measured heart rate of the patient during the first instance. A first heart rate threshold of the first bucket is less than the first measured heart rate. The method also includes determining whether the first heart rate threshold exceeds heart rate thresholds of all buckets of the plurality of buckets to which an instance of the cardiac event that occurred in the patient during the period of time is assigned, determining whether a number of instances of the cardiac event that occurred in the patient during the period of time exceeds an event threshold, and preventing the first instance of the cardiac event from being communicated for clinical review.