Respiratory Risk Analytics via Sensor-Enabled Inhaler Monitoring
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Solution Overview
Problem
Current methods for managing respiratory diseases, such as asthma and COPD, fail to effectively predict and prevent rescue medication usage due to reliance on aggregated and infrequently reported health outcomes, misclassification of air pollution exposure, and single pollutant modeling, which does not account for combined pollutant and weather influences on respiratory symptoms.
Innovation Solution
A respiratory disease analytics system that tracks rescue medication events from inhalers, using sensors to collect geographic, temporal, and environmental data, and applies machine-learned models to assess regional risks, integrating air pollutant, weather, demographic, and health parameters to provide personalized risk maps for patients and healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If aggregated and infrequently reported health outcomes are used, then data collection complexity is reduced, but measurement precision of respiratory disease events deteriorates
Solution Approach 1:
The patent replaces manual self-reporting mechanisms with automated sensor-based detection systems attached to inhalers. These sensors automatically detect and transmit medicament usage events, eliminating the need for patients to manually report health outcomes while significantly improving measurement precision through objective, real-time data collection.
Solution Approach 2:
The system enables self-service data collection where the inhaler device itself automatically records and transmits usage events without requiring patient intervention. The sensor attached to the inhaler autonomously monitors and reports medicament usage, reducing data collection complexity for both patients and healthcare providers while maintaining high measurement precision.
2Ease of operation
If residential address is used to define air pollution exposure location, then data collection simplicity is improved, but measurement precision of exposure classification deteriorates
Solution Approach 1:
The patent transitions from using a single static dimension (residential address) to incorporating multiple dynamic dimensions including GPS coordinates from the inhaler device, timestamp information, and real-time air quality data from multiple monitoring stations. This multi-dimensional approach precisely identifies the actual location where medicament usage occurs, accurately capturing transient air pollution exposures that differ from residential address-based assessments.
3Device complexity
If single pollutant modeling approaches are used, then model complexity is reduced, but reliability of respiratory disease risk assessment deteriorates
Solution Approach 1:
The patent merges multiple pollutant parameters (PM2.5, PM10, O3, NO2, SO2) with weather conditions (temperature, humidity, wind speed) and geographic information into a unified risk assessment model. This integrated approach captures the combined effects of multiple pollutants and environmental factors on respiratory disease, significantly improving the reliability of risk assessments compared to single pollutant models while managing complexity through systematic data integration.
4Productivity
If real-time regional risk assessment is implemented, then productivity of respiratory disease management is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal platform that performs multiple functions: collecting medicament usage events from multiple patients, integrating air quality and weather data, executing risk assessment algorithms, generating regional risk maps, and providing actionable recommendations. This multi-functional system improves respiratory disease management productivity by consolidating various tasks into a single integrated platform that serves multiple stakeholders including patients, healthcare providers, and public health officials.
Data Source
AI summary
A respiratory disease analytics system provides respiratory disease risk reports to a patient, provider, or third-party entity describing a patient's risk of experiencing a medication usage event given data in a geographic region. Regional data, including air pollutant conditions, weather conditions, demographic information, built environment factors, and regional health conditions for a geographic region are accessed from other sources and assigned based on event data recorded during a medicament usage event, as collected by sensors associated with the patient's medicament device/s. The regional data is assigned to medicament usage events occurring within a period of time. The assigned regional data is analyzed to determine an expected number of medication usage events for the geographic region occurring over the period of time.