Predictive Respiratory Risk Modeling via Environmental Trigger Analytics
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Solution Overview
Problem
Patients with respiratory diseases such as asthma and COPD face challenges in managing symptoms due to multiple environmental triggers and factors, making it complex to monitor and respond to these triggers effectively for symptom management.
Innovation Solution
An analytics system that includes medicament device sensors, client devices, application servers, and database servers to monitor real-time medicament usage, perform analytics, and provide notifications, allowing patients and healthcare providers to track and respond to environmental triggers and factors affecting respiratory health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients monitor multiple environmental triggers and factors manually, then symptom management completeness improves, but patient burden and complexity increase
Solution Approach 1:
The patent introduces an intermediary system comprising sensors, processors, and communication devices that automatically collect, analyze, and interpret environmental trigger data. This intermediary handles the complexity of monitoring multiple factors (air quality, weather, land use, etc.), freeing patients from manual monitoring while ensuring comprehensive symptom management through automated alert generation and provider notification.
2Measurement precision
If comprehensive environmental data collection is implemented, then predictive accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the data processing system into specialized components: environmental sensors for data collection, processors for analyzing specific trigger types, and communication devices for selective notification. This segmentation allows comprehensive data collection across multiple environmental factors while distributing processing complexity across modular system components, maintaining predictive accuracy without overwhelming single-point complexity.
3Speed
If real-time monitoring and notification systems are deployed, then response time to triggers improves, but energy consumption and device resource usage increase
Solution Approach 1:
The patent implements periodic monitoring and notification cycles where the system continuously collects environmental data but only activates full notification sequences when predictive algorithms detect actual trigger conditions. This periodic action pattern maintains rapid response capability when needed while reducing energy consumption during normal operation by keeping the system in a low-power standby state between trigger events.
Data Source
AI summary
An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.