Autonomous Prescription Status Updates via Trust Dynamics
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Solution Overview
Problem
Current prescription updating processes require manual medical assessments and approvals, which are time-consuming and inefficient, especially for medications with potential side effects, and do not leverage the patient's trust dynamics for autonomous adjustments.
Innovation Solution
A system utilizing artificial intelligence and machine learning to autonomously update prescription statuses based on trust disposition values, determined by analyzing electronic communications and commitments, to reflect the patient's responsiveness to the medication, thereby facilitating efficient and effective updates without direct human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual medical assessments are used to update prescriptions, then reliability of prescription updates is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system enables self-service by having the AI model autonomously evaluate patient trust dynamics and update prescriptions without requiring manual medical assessments. The machine learning model processes electronic communications and commitment data to automatically determine when prescription updates are needed, eliminating the time-consuming manual review process while maintaining reliable updates based on objective trust metric calculations.
Solution Approach 2:
The patent replaces the mechanical manual assessment system with an automated AI-based evaluation system. Instead of requiring healthcare professionals to manually review patient progress, the system uses machine learning algorithms to analyze electronic communications, track commitment fulfillment, and objectively determine prescription update timing, thereby reducing time consumption while maintaining update reliability through structured algorithmic decision-making.
2Reliability
If frequent manual medical assessments are conducted, then reliability of patient care is improved, but productivity and operational efficiency deteriorate
Solution Approach 1:
The system implements continuous feedback by monitoring patient trust dynamics through analysis of electronic communications and commitment data. The AI model continuously updates trust disposition values based on new information, enabling timely prescription adjustments without requiring frequent manual assessments. This feedback mechanism maintains reliable patient care by adapting to changing trust levels while improving productivity through automated real-time monitoring rather than periodic manual reviews.
Solution Approach 2:
The patent changes the parameter of evaluation frequency from frequent manual assessments to continuous automated monitoring. By transitioning from time-based scheduling to trust-dynamic-based triggering, the system maintains reliable patient care through continuous AI monitoring while significantly improving operational efficiency. The machine learning model processes trust parameter changes automatically, eliminating the need for frequent manual interventions and optimizing productivity.
3Productivity
If autonomous AI-based updates are implemented, then productivity and operational efficiency are improved, but device complexity and system complexity increase
Solution Approach 1:
The system achieves multi-functionality by having a single AI-based prescription management system handle multiple tasks: analyzing electronic communications, evaluating trust dynamics, tracking commitments, and automatically updating prescriptions. This universal approach consolidates what would otherwise require multiple separate manual processes into one integrated system, improving productivity while managing complexity through functional consolidation rather than adding separate complex subsystems.
Solution Approach 2:
The patent introduces an intermediary AI model that mediates between raw electronic communication data and prescription update decisions. This intermediary layer processes and interprets complex trust dynamics, translating them into actionable prescription recommendations. By using the AI model as an intermediary, the system manages the complexity of processing unstructured communications while maintaining high productivity through automated decision-making.
4Adaptability or versatility
If trust dynamics are measured and used for prescription updates, then adaptability of patient care is improved, but measurement precision and data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing the trust measurement on specific, actionable aspects of patient behavior rather than attempting to measure all possible trust indicators. The machine learning model analyzes particular patterns in electronic communications and commitment fulfillment that are most relevant to prescription effectiveness. This selective measurement approach provides sufficient precision for adaptive prescription updates without requiring overly complex or imprecise measurements of all trust dimensions.
Data Source
AI summary
Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.