Unified Clinical Alert Generation from Multiple Predictive Models
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Solution Overview
Problem
Existing clinical decision support systems face challenges in integrating multiple predictive models, leading to conflicting information and alarm fatigue, which hinders effective clinical decision-making.
Innovation Solution
A framework that combines outputs from multiple predictive models using rules to generate a single alert, suppressing conflicting triggers and prioritizing based on severity levels or weights, thereby generating a unified alert for clinical action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple predictive models are used to predict different facets of health conditions, then clinical utility and information availability are improved, but alarm fatigue and information overload increase
Solution Approach 1:
The patent combines multiple predictive models into a unified alert generation system. Instead of presenting separate alerts from each model, the system integrates their outputs and generates a single consolidated alert when any model predicts a high-risk state, thereby maintaining clinical utility while reducing alarm fatigue.
Solution Approach 2:
The alert generation system is designed to work with multiple different predictive models simultaneously, creating a universal platform that can handle various health condition predictions (sepsis, AKI, ARDS, etc.) through a single interface, reducing the need for multiple separate alert systems.
2Loss of information
If multiple predictive models with separate alerts are implemented, then more information about patient state is available, but device complexity and information overload increase
Solution Approach 1:
The system merges multiple model outputs into a single alert decision, maintaining all underlying information from each model while presenting a unified alert to clinicians, thereby reducing information overload without losing predictive capabilities.
Solution Approach 2:
The alert generation system acts as an intermediary layer between multiple predictive models and the clinician. It processes and integrates outputs from various models, applying configurable rules to determine when to generate alerts, thereby simplifying the interface while preserving comprehensive information.
3Reliability
If multiple predictive models are integrated into clinical workflow, then comprehensive risk assessment is improved, but ease of operation and clinical decision-making are worsened due to conflicting information
Solution Approach 1:
The system combines multiple risk assessments into a single unified alert decision, allowing clinicians to make decisions based on one clear alert rather than reconciling conflicting information from multiple models, thereby improving ease of operation while maintaining comprehensive risk assessment.
Solution Approach 2:
The system allows flexible configuration of alert thresholds and rules that can be adjusted based on clinical needs and patient populations. By making parameters configurable, the system adapts to different clinical contexts while maintaining a unified alert interface, improving both reliability and ease of operation.
Data Source
AI summary
Provided is an apparatus and method for combining the outputs of separate predictive models or algorithms to generate a single alert. The apparatus includes a framework which uses rules to determine a single alert to be generated across all the predictive models or algorithms based on evaluating each model separately at each timepoint based on their model scores. Using the proposed rules, the results of each of the models are combined to generate a single alert at each timepoint or window. In case of conflicting results from the models, the generation of the alert is suppressed.