Adaptive Alert Classifier for Clinical Episode Filtering
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Solution Overview
Problem
Clinical staff are overwhelmed by repetitive alerts from a small percentage of patients, necessitating a method to automatically identify new or different clinical episodes to filter out non-significant alerts and prioritize attention on actual changes.
Innovation Solution
An adaptive alert classifier uses unsupervised training to identify characteristics of significant clinical episodes, grouping past episode vectors into clusters using algorithms like k-means, and issuing alerts for new episodes not assigned to these clusters or deemed clinically significant, with newer episodes weighted more heavily in re-computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alerts are generated for all clinical episodes, then comprehensive monitoring is achieved, but alert fatigue and resource overload occur
Solution Approach 1:
The patent segments alerts into two categories: standard alerts for episodes matching existing clusters, and potential alerts for episodes forming new clusters. This segmentation allows clinical staff to prioritize potential alerts while automatically handling standard alerts through the system, reducing overall workload while maintaining comprehensive monitoring.
Solution Approach 2:
The patent introduces an intermediary clustering algorithm that acts as a filter between episode detection and alert generation. This intermediary automatically processes and groups episodes, generating potential alerts only when new patterns are detected, thereby reducing the volume of alerts requiring human review while preserving important cases.
2Reliability
If all episodes are reviewed by clinical staff, then no significant episodes are missed, but time and resources are wasted on repetitive alerts
Solution Approach 1:
The patent performs preliminary clustering analysis automatically before presenting alerts to clinical staff. By pre-processing episodes through unsupervised learning algorithms, the system identifies and groups similar episodes, flagging only novel or significant cases for human review. This preliminary action ensures no significant episodes are missed while dramatically reducing the time required for manual review.
Solution Approach 2:
The system enables self-service monitoring by automatically detecting, clustering, and evaluating episodes using machine learning algorithms. The clustering model independently processes episodes, generating potential alerts only when novel patterns are detected, thereby eliminating the need for staff to review every episode while maintaining high detection accuracy.
3Quantity of substance
If clustering algorithms group all episodes equally, then historical data is utilized, but recent significant episodes may be obscured by older data
Solution Approach 1:
The patent applies local quality by assigning different weights to episodes based on their recency. More recent episodes receive higher weights in the clustering calculation, while older episodes receive lower weights. This weighted approach ensures that recent significant episodes are not obscured by historical data, while still utilizing the broader dataset for pattern recognition.
Solution Approach 2:
The patent implements dynamic weighting in the clustering algorithm, where the influence of each episode on cluster formation changes over time. Recent episodes have greater dynamic influence on cluster centroids and membership assignments, allowing the system to adapt to changing patient conditions and prioritize detection of recent significant episodes while maintaining historical context.
Data Source
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AI summary
Described herein are systems and methods for classifying clinical episodes in order to more accurately generate alerts for those episodes that warrant them. In some embodiments, alerts are only generated for those episodes that are new or different from previous episodes, where the previous episodes have been found to be not significant enough to warrant an alert.