Dynamic Treatment Schedule Adjustment via Real-Time Patient Monitoring
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Solution Overview
Problem
Current medical treatments for conditions requiring specialized nutritional intake or diet lack customizable control mechanisms and cognitive guidance, failing to fully address individual eating patterns and nutritional intake symptoms effectively.
Innovation Solution
A system that generates an interactive and customized diagnosis object (ICDO) to create a cognitive and customizable treatment plan, using an internet of things network to monitor patient status in real-time and adjust the treatment schedule by exchanging elements according to learned rules, providing immediate modifications to the eating plan to compensate for detrimental food intake events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and dynamic adjustment of treatment schedules are implemented, then treatment effectiveness and patient adherence are improved, but system complexity and computational requirements increase
Solution Approach 1:
The treatment schedule is transformed from a static plan to a dynamic, self-adjusting system that automatically modifies treatment elements based on real-time patient status monitoring. The system exchanges treatment elements according to learned rules when deviations are detected, enabling adaptive treatment optimization without requiring manual intervention.
Solution Approach 2:
The system implements continuous feedback loops where patient status is monitored in real-time, compared against the treatment schedule, and deviations trigger automatic adjustments. This closed-loop feedback mechanism ensures the treatment plan responds dynamically to patient conditions, improving effectiveness through continuous optimization.
2Speed
If automated real-time adjustments are made without doctor intervention, then treatment responsiveness is improved, but loss of doctor-patient control and customization may occur
Solution Approach 1:
The system performs self-adjustment by automatically detecting deviations from the treatment schedule and exchanging treatment elements according to pre-learned rules. This self-service capability enables rapid response to patient status changes without requiring real-time doctor intervention, while the learned rules ensure adjustments remain within medically appropriate parameters.
Data Source
AI summary
Approaches presented herein enable treating a medical condition via individual customized cognitive guidance. More specifically, a current interactive and customized diagnosis object (ICDO) comprising a treatment schedule for a patient is generated by combining a set of previous ICDOs from the patient, wherein each ICDO has a structure which defines elements that are used to diagnose a symptom and match the patient with a treatment. A status of the patient is monitored in real time via an internet of things network to detect an actual event of the patient differing from the treatment schedule. In real time, the treatment schedule is modified responsive to the differing event by adjusting the current ICDO by exchanging one element of the treatment plan for a new element according to a learned set of rules to compensate for the differing event. This modified treatment schedule is provided to the patient in real time.