Alarm Rate Prediction Model Adaptation Across Healthcare Environments
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Solution Overview
Problem
Current systems for predicting physiological alarm frequency in healthcare environments rely on extensive physiological data collection, which is expensive and time-consuming, and fail to account for unique factors between different healthcare environments, leading to erroneous results and alarm fatigue among clinicians.
Innovation Solution
A method and system that generate an alarm rate model based on patient monitoring data from one healthcare environment, allowing for the calculation of an alarm rate transform to predict alarm rates in different environments, enabling accurate estimation of alarm rates at various configurations without requiring extensive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alarm rate models are developed using extensive physiological data collection from multiple patients, then prediction accuracy improves, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary data collection and model development in a first healthcare environment before deployment. By pre-collecting extensive physiological data and pre-developing the alarm rate model in the source environment, the system avoids the need for extensive data collection in the target environment, thus reducing time consumption while maintaining prediction accuracy
Solution Approach 2:
The system creates a copy of the alarm rate model developed in the first healthcare environment and applies it to the second healthcare environment. Instead of collecting extensive data in the target environment, the system copies the pre-developed model and adapts it using alarm rate transforms, significantly reducing data collection time and cost while preserving prediction accuracy
2Measurement precision
If alarm rate models are developed using extensive physiological data collection from multiple patients, then prediction accuracy improves, but cost increases
Solution Approach 1:
The system performs the costly model development process in advance in a first healthcare environment where extensive data can be collected once. This preliminary action consolidates the expensive data collection and model training into a single phase, avoiding repeated costly data collection efforts in multiple target environments
Solution Approach 2:
The system copies the developed alarm rate model to multiple target environments, eliminating the need to repeat expensive data collection and model development in each new environment. The copying approach allows the model to be reused across multiple settings, significantly reducing overall development costs while maintaining prediction accuracy
3Reliability
If alarm configurations are increased to monitor more parameters, then detection capability improves, but alarm frequency increases causing alarm fatigue
Solution Approach 1:
The system uses alarm rate transforms that are calculated based on feedback from the second healthcare environment's initial alarm rate data. This feedback mechanism allows the system to adjust the model predictions to match the actual alarm characteristics of the target environment, enabling optimization of alarm configurations that maintain detection capability while reducing unnecessary alarms
Solution Approach 2:
The system applies alarm rate transforms that modify the alarm rate model parameters to account for differences between healthcare environments. By changing the model parameters through the transform calculation, the system adapts the alarm configurations to achieve optimal detection capability while minimizing alarm fatigue in the specific target environment
4Measurement precision
If alarm rate models are environment-specific, then prediction accuracy improves, but adaptability to different environments decreases
Solution Approach 1:
The system creates a universal alarm rate model framework that can be applied across multiple healthcare environments. The base model is developed in a first environment but designed to be adaptable to second environments through the alarm rate transform mechanism, allowing the same model structure to serve multiple functions across different settings
Solution Approach 2:
The alarm rate transform serves as an intermediary that bridges the alarm rate model and different healthcare environments. The transform calculation acts as a mediator that adapts the model predictions to match the specific characteristics of each target environment, enabling the model to maintain high prediction accuracy across diverse settings without requiring environment-specific model development
Data Source
AI summary
A method of predicting physiological alarm frequency by patient monitors includes collecting patient monitoring data of multiple patients in at least a first healthcare environment, and then determining an alarm rate model based on the patient monitoring data, wherein the alarm rate model describes alarm rates at a range of alarm configurations in the first healthcare environment. Initial alarm rate data is collected for a second healthcare environment, wherein the initial alarm rate data includes at least one alarm rate at at least one known alarm configuration within the range of alarm configurations. An alarm rate transform is then calculated for the second healthcare environment comparing the initial alarm rate data for the second healthcare environment to the alarm rate model. Alarm rates are predicted for the second healthcare environment at a different alarm configuration than the at least one known alarm configuration of the initial alarm rate data by applying the alarm rate transform to the alarm rate model.