Asthma Trigger Identification via Pooled Patient Data Analysis
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Solution Overview
Problem
Current methods for predicting asthma trigger events are prone to high error rates, require an impractically long time horizon, and struggle to communicate complex statistical information to patients, leading to poor compliance and ineffective asthma management.
Innovation Solution
A respiratory ailment monitoring system that collects and analyzes data from inhalers, patient demographics, and environmental factors to identify trigger conditions by comparing a primary patient's data to a large population, using frequentist statistics and regression analysis to provide easy-to-understand risk notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis is performed on individual patient data to identify triggering events, then the analysis can be personalized to the patient, but the error rate becomes very high and the time horizon required becomes impractically long
Solution Approach 1:
The patent combines data from multiple patients into a pooled dataset to perform statistical analysis. By merging individual patient data with population data, the system achieves both personalized trigger identification and reduced error rates within practical timeframes. The pooled analysis allows borrowing strength across patients to identify triggers more reliably than individual analysis alone.
Solution Approach 2:
The system performs dual-function analysis: it identifies triggers specific to individual patients while simultaneously leveraging population-level patterns. This universal approach allows the same statistical framework to serve both personalized medicine goals and public health surveillance, reducing the time horizon required for accurate identification.
2Adaptability or versatility
If multiple hypothesis tests are conducted with correlated variables to identify triggers, then comprehensive coverage of potential triggers is achieved, but the false positive rate increases significantly
Solution Approach 1:
The patent introduces pooled population data as an intermediary reference framework. Individual patient analyses are conducted within this population context, allowing the system to distinguish true triggers from false positives by comparing against population-level expectations. This intermediary framework enables comprehensive variable coverage while controlling false positive rates through population-based validation.
3Measurement precision
If current statistical methods are used to identify trigger conditions, then analysis can be performed, but the results are difficult to communicate to patients and compliance remains poor
Solution Approach 1:
The system creates simplified representations (copies) of complex statistical results that are easier for patients to understand. Instead of presenting raw statistical outputs, the system generates patient-friendly summaries such as risk scores, trigger probability percentages, and actionable recommendations. These simplified copies maintain the statistical rigor of the analysis while improving patient comprehension and compliance.
Data Source
AI summary
A method for determining risk of triggering a respiratory ailment in comparison to a general patient population is disclosed. Data on activation of medicament devices to deliver respiration medicament to a population of patients is collected. The activation data and patient contextual parameter data related to each activation event is stored. A data analysis module determines the occurrence of rescue events based on the collected activation data, and determines a coefficient for a contextual parameter as a triggering event for a primary patient based on the correlation of the contextual parameter and the rescue event. The data analysis module provides a comparison of the coefficient for the at least one contextual parameter for the primary patient and the distributions of coefficients of the triggering event for the population of patients based on the correlation of the contextual parameter and the rescue events for the population of patients.