Conversational AI Trial Dosing With Real-Time Sensor Integration
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Solution Overview
Problem
Conventional clinical trial management systems face challenges such as transcription errors, version-control issues, latency, and lack of integration with real-time physiologic signals, leading to inefficient dosing decisions and non-compliance with regulatory requirements, especially in decentralized and hybrid trials.
Innovation Solution
A conversational AI system that integrates with physiological sensors and laboratory feeds to provide real-time, adaptive dose escalation and titration, using algorithms like Bayesian continual reassessment, while ensuring secure, auditable electronic records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual workflows are used for dosing decisions, then system complexity is low, but error rate increases and time latency increases
Solution Approach 1:
The system enables self-service through automated AI-driven dosing decisions that process participant data, apply protocol rules, and generate dosing recommendations without requiring manual intervention from investigators or study staff, thereby reducing human error while maintaining appropriate system complexity
Solution Approach 2:
The patent replaces manual mechanical workflows (spreadsheets, email, handwritten orders) with an automated digital system that uses AI models, electronic data capture, and integrated algorithms to make dosing decisions, eliminating transcription errors and version-control problems while managing complexity through structured software architecture
2Adaptability or versatility
If simple rule engines are used for dose automation, then system complexity is low, but integration with real-time data sources is insufficient
Solution Approach 1:
The system achieves universality by designing a multi-functional platform that integrates multiple data sources (physiological sensors, laboratory feeds, wearable devices), supports various dosing strategies (adaptive designs, continual reassessment, model-informed approaches), and serves different user roles (investigators, study staff, regulators) through a single unified architecture
Solution Approach 2:
The patent introduces an intermediary layer in the form of an AI model and data integration framework that sits between diverse data sources and the dosing decision logic, allowing real-time integration of multiple data types without directly increasing the complexity of individual components through standardized interfaces and abstracted data handling
3Ease of operation
If decentralized trial workflows are implemented, then participant convenience improves, but data integrity and regulatory compliance become more difficult to maintain
Solution Approach 1:
The system implements continuous feedback mechanisms that monitor data quality, track protocol compliance, and provide real-time validation checks on participant-reported data and sensor readings, ensuring data integrity is maintained throughout the decentralized trial workflow while preserving participant convenience
Solution Approach 2:
The patent applies preliminary action through pre-configured protocol rules, pre-established data validation criteria, and pre-defined audit trails that are set up before the trial begins, enabling automatic enforcement of data integrity standards and regulatory compliance requirements throughout the decentralized execution without requiring ongoing complex verification
4Productivity
If manual data aggregation and qualitative risk assessment are performed, then system complexity is low, but cognitive burden on clinicians increases
Solution Approach 1:
The system enables self-service by having the AI model automatically aggregate participant data from multiple sources, perform quantitative risk assessments, and generate dosing recommendations without requiring clinicians to manually collect and interpret data, thereby improving productivity while containing complexity within the automated system
Solution Approach 2:
The patent replaces the manual mechanical process of data aggregation and qualitative assessment with an automated digital system that uses AI algorithms to process data, calculate risks, and generate recommendations, significantly improving clinician productivity while managing complexity through structured computational approaches
Data Source
AI summary
A conversational AI platform for managing remote and hybrid clinical trials for investigational medical interventions. Embodiments of the present disclosure comprise a conversational AI model and an algorithmic logic engine configured to define and implement protocol parameters for dosing schemas, visit schedules, safety thresholds, and eligibility criteria for a clinical trial. An AI agent is configured to deliver mapped voice prompts to participant devices, captures audio responses, transcribe and extract symptom, adherence, or adverse-event data, and pair response data with physiological-sensor or laboratory input data. The logic engine continuously evaluates the combined data to adaptively select dose-escalation or titration instructions and issue follow-up queries while enforcing safety thresholds. All prompts, audio, transcriptions, decisions, and metadata may be immutably timestamped in an electronic record repository accessible via role-based, encrypted connections. Embodiments of the present disclosure provide for real-time, audit-ready trial communications, automated personalized dosing, and enhanced participant safety monitoring.