Remote ILD Monitoring With Real-Time Provider Alerts
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Solution Overview
Problem
Interstitial lung disease (ILD) is often misdiagnosed or diagnosed through exclusion, leading to delayed treatment initiation, increased resource utilization, and higher morbidity, particularly in non-small cell lung cancer (NSCLC) patients, due to the risk of pneumonitis, which is exacerbated by limited awareness and inability to timely manage the condition.
Innovation Solution
A remote monitoring system that collects patient data, including physiological and subjective data, using a rule-based and longitudinal algorithm to identify risk factors and alert healthcare providers to potential ILD through a cloud-based platform, enabling early detection and management of symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HCPs use traditional diagnostic methods for ILD, then diagnosis can be made, but it leads to misdiagnosis or delayed diagnosis through exclusion
Solution Approach 1:
The system performs preliminary actions by continuously monitoring patients at risk for ILD using algorithms that analyze symptoms, vital signs, and treatment data before ILD develops. This early detection enables proactive intervention rather than reactive diagnosis, resolving the contradiction between diagnosis accuracy and time delay.
Solution Approach 2:
The system implements feedback mechanisms where algorithm outputs are continuously fed back to HCPs through notifications and dashboards. This real-time feedback loop enables continuous monitoring and adjustment, improving both diagnosis accuracy and reducing time loss by keeping HCPs informed of changing patient status.
2Reliability
If HCPs monitor patients continuously for ILD, then early detection is enabled, but resource utilization increases
Solution Approach 1:
The system enables self-service monitoring where the algorithm automatically collects, analyzes, and monitors patient data without requiring constant HCP involvement. Patients use wearable devices and mobile apps to provide data, and the system autonomously detects anomalies, reducing the burden on HCPs while maintaining reliable early detection.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated electronic systems. Algorithms substitute for HCP review of continuous data streams, and electronic notifications replace manual chart reviews. This substitution maintains detection reliability while significantly reducing resource utilization.
3Reliability
If HCPs are aware of pneumonitis risk, then timely management is possible, but patient awareness remains limited
Solution Approach 1:
The system provides feedback to patients through mobile app notifications and educational materials, keeping them informed of their ILD risk status and symptoms to report. This patient feedback mechanism bridges the awareness gap while maintaining HCP capability for timely management through the same notification system.
Solution Approach 2:
The algorithm and notification system serve as intermediaries between patients and HCPs. They translate complex medical information into actionable alerts for patients while simultaneously notifying HCPs of concerning trends, enabling timely management without requiring direct patient-HCP communication for every detail.
4Productivity
If treatments with pneumonitis risk are used, then therapeutic benefit increases, but ILD risk increases
Solution Approach 1:
The system performs preliminary risk assessment and continuous monitoring during treatment, enabling early detection of ILD before it becomes severe. This allows HCPs to maintain beneficial treatments while intervening early to prevent harmful ILD progression, resolving the contradiction between therapeutic benefit and harm.
Solution Approach 2:
The system implements continuous feedback loops that monitor treatment response and ILD signs in real-time. When ILD symptoms emerge, the system notifies HCPs immediately, enabling dynamic adjustment of treatment to maintain benefits while preventing harm through timely intervention.
Data Source
AI summary
A patient at risk of lung disease due to a prescribed treatment may be remotely monitored. A cloud server may receive data collected from a patient and analyze the data to determine if the patient shows signs of onset or worsening interstitial lung disease. The cloud server may transmit the analyzed data to a healthcare provider in real-time or near real-time to provide timely symptom management to the patient.


