Continuous Analyte Monitoring With ML Alerts for Readmission Reduction
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Solution Overview
Problem
Current monitoring techniques for chronic or acute illnesses are often invasive, reactive, and infrequent, leading to missed early warning signs and gaps in patient data, which can result in delayed or missed diagnoses and increased patient readmission rates.
Innovation Solution
A continuous analyte monitoring system that utilizes machine learning to predict patient outcomes based on real-time analyte data, such as lactate and glucose levels, and provides alerts and recommendations to healthcare providers to facilitate timely interventions and reduce readmission events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous analyte monitoring is implemented, then early detection of patient deterioration is improved, but device complexity and resource intensity increase
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with machine learning algorithms that analyze continuous analyte data. The ML model predicts patient outcomes by processing sensor data, eliminating the need for complex mechanical intervention systems while improving early detection capability.
Solution Approach 2:
The system implements self-monitoring through continuous analyte sensors that automatically track patient conditions without requiring manual intervention. The machine learning model autonomously analyzes data and generates predictions, reducing the need for continuous human resource involvement while maintaining high reliability.
2Measurement precision
If continuous monitoring is used, then measurement precision and early warning detection are improved, but loss of time and data processing complexity increase
Solution Approach 1:
The machine learning model is pre-trained on extensive patient data before deployment. This preliminary training enables the model to rapidly process continuous analyte data in real-time without requiring complex analysis during patient monitoring, thus maintaining high measurement precision while minimizing data processing time.
Solution Approach 2:
The system implements continuous feedback loops where analyte data is constantly monitored and fed into the ML model for real-time prediction updates. This feedback mechanism allows the system to maintain high measurement precision through continuous analysis while optimizing processing speed through iterative model refinement.
3Device complexity
If reactive monitoring with gaps is used, then device complexity is reduced, but loss of information about patient condition increases
Solution Approach 1:
The patent implements continuous analyte monitoring that eliminates gaps in data collection. The system continuously measures analyte levels and feeds data to the machine learning model, ensuring uninterrupted information flow about patient condition while maintaining manageable system complexity through automated processing.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments to an improved alert and recommendation system for reducing patient readmission via the detection and treatment of patient conditions based on continuous analyte data. The disclosed techniques utilize analyte data, such as lactate, glucose, and creatinine, provided from a continuous analyte sensor to predict patient outcomes and generate recommendations for reducing patient readmission in a hospital and home setting. The disclosed system allows for early and non-invasive prediction of patient outcomes and the subsequent generation of recommended actions to facilitate patient intervention with the goal of reducing readmission of the patient.