Clinical Decision System for Cannabis Therapy
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Solution Overview
Problem
The limited clinical research and lack of effective methods for selecting appropriate cannabis and cannabinoid administration methods, strains, dosages, and consumption techniques due to production restrictions, social stigma, and educational limitations hinder personalized treatment for medical and wellness applications.
Innovation Solution
A wellness system that integrates monitoring inference engines, clinical decision support engines, and data filtering and synthesis engines to process user-generated sensor data, CB regimen data, and over-the-counter medication data, using machine learning to provide personalized insights and recommendations for cannabis treatment regimens, reducing the trial-and-error process and associated anxiety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple personal tracking devices are used to monitor patient data, then measurement precision and data completeness improve, but device complexity and data integration difficulty increase
Solution Approach 1:
The patent combines data from multiple personal tracking devices (wearables, mobile phones, tablets) into a unified cloud-based platform. The system merges sensor data, EHR data, and patient-generated data into a single integrated view, allowing clinicians to access consolidated patient information without managing multiple separate devices or data sources.
Solution Approach 2:
The patent introduces a cloud-based data integration platform as an intermediary between multiple tracking devices and clinicians. This intermediary layer standardizes data formats, handles data synchronization, and provides a unified interface, eliminating the need for clinicians to directly manage complex multi-device configurations.
2Reliability
If comprehensive sensor data and EHR data are integrated, then clinical decision support quality improves, but data processing time and computational requirements increase
Solution Approach 1:
The patent pre-processes and structures data from multiple sources before clinical use. Data from wearables, EHR systems, and patient journals are continuously ingested, cleaned, and organized in the cloud platform in advance, so that when clinicians need information, it is already prepared and accessible without requiring real-time processing during clinical decisions.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated machine learning algorithms and AI-driven analytics. The system automatically processes sensor data, identifies patterns, generates insights, and provides personalized recommendations, eliminating the need for clinicians to manually analyze large volumes of raw data.
3Reliability
If personalized treatment recommendations are provided, then patient outcomes improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent enables the system to automatically generate personalized treatment recommendations without requiring complex manual configuration. The machine learning algorithms self-adjust based on patient responses and outcome data, continuously improving recommendations while requiring minimal clinical intervention for system management or parameter tuning.
Solution Approach 2:
The patent implements closed-loop feedback where patient outcomes and treatment responses are continuously monitored and fed back into the system. This feedback drives automatic refinement of treatment recommendations, allowing the system to adapt and improve over time without increasing implementation complexity for clinicians.
Data Source
AI summary
An automatic system for making clinical decisions, especially with relation to cannabis use, that incorporates data from multiple personal tracking devices such as fitness trackers, analyzes that data using machine learning techniques to determine which data points are relevant to account for changes over time, and offers predictions, insights, and/or suggestions to support clinical decision making.