Wearable Medical System Data Record Pre-analysis and Prioritization
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Solution Overview
Problem
Wearable medical systems (WMS) face challenges in efficiently pre-analyzing and characterizing data records from patients, particularly those with noise or low-interest content, which can lead to clinicians spending excessive time reviewing uninterpretable or irrelevant data, hindering timely intervention in critical situations like sudden cardiac arrest.
Innovation Solution
A pre-analyzing computer system that scores and characterizes data records based on a sorting criterion, trained by artificial intelligence, to prioritize records for review, allowing clinicians to focus on high-interest data while filtering out low-scoring records, thereby optimizing the review process and saving time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinicians review all data records from wearable medical systems, then complete patient monitoring is achieved, but time consumption increases excessively
Solution Approach 1:
The system performs preliminary analysis of ECG data records by detecting quality metrics (amplitude, frequency content, artifacts) before clinician review. This pre-characterization filters and prioritizes records, allowing clinicians to focus only on high-quality or critical records while automated systems handle routine monitoring, thus reducing review time without compromising monitoring completeness
Solution Approach 2:
An intermediary automated analysis system is introduced between the wearable medical system and clinician. This intermediary characterizes data records by computing quality metrics and generating summaries, acting as a filter that presents only relevant information to clinicians, thereby reducing the time required for complete review while maintaining monitoring reliability
2Loss of information
If all data records are reviewed without filtering, then no information is lost, but clinician efficiency decreases
Solution Approach 1:
The system applies local quality assessment to individual ECG records by computing specific quality metrics (signal amplitude thresholds, frequency content analysis, artifact detection) for each record. Records are then categorized by quality level, allowing clinicians to efficiently process high-quality records while automated systems handle low-quality or routine records, maintaining information completeness while improving productivity
Solution Approach 2:
The system transforms raw ECG data into characterized records with computed quality parameters (amplitude ranges, frequency characteristics, artifact scores). This parameter transformation enables automated filtering and prioritization, allowing clinicians to review only records exceeding quality thresholds or requiring attention, thus improving efficiency without losing critical information
3Productivity
If automated pre-analysis is implemented, then data review efficiency improves, but system complexity increases
Solution Approach 1:
The pre_analysis function is segmented into distinct operational modules: quality metric computation (amplitude, frequency, artifacts), record characterization, and prioritization logic. This segmentation allows the complex automated analysis to be broken down into manageable functions that can be implemented incrementally, reducing perceived system complexity while maintaining high productivity improvements
Solution Approach 2:
The system implements self-service through automated quality assessment and self-prioritization of ECG records. The automated analysis characterizes records based on built-in quality metrics and automatically determines review priority without requiring clinician intervention for each decision, thereby achieving high productivity with minimal operational complexity from the user perspective
Data Source
AI summary
In embodiments, a pre-analyzing computer receives a data record that is created by a wearable medical system (“WMS”) which may implement a wearable cardioverter defibrillator (“WCD”). The WMS has created such data records from patient data captured when the WMS has detected that the patient was having episodes of potential interest for review by clinicians. Before this review, however, the pre-analyzing computer may parse the contents of a received data record and accordingly give it a score. The score may reflect the clinician's expected preference to review this data record before or after the others. For instance, a low score may be given to data records whose contents are likely not interpretable reliably due to noise or likely of low interest after all. The pre-analyzing computer may then perform a characterizing action with reference to the data record, for facilitating the clinician to find it by its score.