Digital Therapeutic Decision Engine for Heart Failure Management
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Solution Overview
Problem
Despite advancements in heart failure therapies, the prevalence of heart failure continues to rise, and clinical decision-making for managing these therapies has become increasingly complex and time-consuming, leading to clinical inertia and unchanged or increasing mortality and hospitalization rates.
Innovation Solution
A system and method for continuously evaluating and updating heart failure therapeutic regimens using remote monitoring data and patient data through a digital therapeutic decision engine, which acquires physiological sensor data, processes it, and adjusts medication dosages or initiates new medications based on patient stability, reducing the need for clinician intervention and improving treatment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new pharmacological therapeutic agents are developed and approved for use, then therapeutic options for heart failure patients are improved, but clinical decision making becomes increasingly challenging and time consuming
Solution Approach 1:
The system enables automated clinical decision-making through a digital therapeutic decision engine that autonomously processes patient data, evaluates therapeutic regimens, and generates recommendations without requiring extensive clinician time for manual analysis of multiple pharmacological options
Solution Approach 2:
The patent replaces the manual mechanical process of clinician review and decision-making with an automated computational system that uses algorithms to evaluate therapeutic agents, patient stability, and dosage adjustments, thereby reducing time loss while maintaining adaptability to new pharmacological options
2Stability of the object's composition
If clinicians fail to initiate or intensify evidence-based therapy due to clinical inertia, then current treatment processes are maintained, but potential improvement in clinical outcomes is reduced
Solution Approach 1:
The system continuously monitors patient stability through physiological sensor data and provides real-time feedback to clinicians and patients about therapeutic regimen effectiveness, automatically triggering alerts and recommendations when adjustments are needed, thereby overcoming clinical inertia while maintaining process stability
Solution Approach 2:
The digital decision engine proactively evaluates patient data and prepares therapeutic regimen adjustments in advance, presenting optimized treatment options to clinicians before clinical inertia can set in, thereby improving clinical outcomes while maintaining systematic stability
3Productivity
If therapeutic regimens are manually evaluated and updated, then clinician control is maintained, but treatment efficiency and speed of optimization are reduced
Solution Approach 1:
The system implements a dynamic hybrid model where the level of automation adjusts based on patient stability and clinical context, allowing fully automated decisions for stable patients while maintaining clinician control for complex cases, thereby increasing productivity without excessive complexity
Solution Approach 2:
The decision-making process is segmented into distinct automated components (data acquisition, stability evaluation, regimen recommendation) that can operate independently at different automation levels, enabling efficient processing while maintaining appropriate clinician involvement based on specific clinical needs
Data Source
AI summary
Disclosed herein are systems and methods for management and administration of therapeutic treatment regimens for heart failure patients. In some examples, this includes monitoring a patient’s heart rate and electrocardiogram using a wearable heart rate sensor to provide input into a decision tree model to provide an effective treatment regimen. Furthermore, regular blood pressure measurements may be input into the model. The system may additionally iterate through various heart failure therapeutics based on a patient’s response to prior administered therapeutics.