Drug Delivery System Therapy Continuity Across Device Swaps
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Solution Overview
Problem
Current manual systems for managing and delivering drug therapy, such as those used for treating hyperglycemia, are burdensome and prone to errors due to the complexity of physiological control and limited resources, failing to adequately account for patient-specific responses and nonlinear drug characteristics.
Innovation Solution
An electronic drug delivery system that includes an infusion pump and sensor, which estimates patient-specific control variables based on therapy and response, updates therapy accordingly, and transfers records to a remote system for continuous patient care, enabling seamless continuation of therapy across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control process is used for adjusting infusion rates, then clinicians can directly monitor and adjust therapy, but the process becomes burdensome and prone to medication errors
Solution Approach 1:
The system enables self-service by automatically adjusting infusion rates based on patient response data without requiring manual clinician intervention. The closed-loop control system monitors patient parameters and autonomously modifies therapy settings, freeing care providers from burdensome manual adjustment tasks while maintaining reliability through automated error prevention
Solution Approach 2:
The system implements feedback by continuously monitoring patient response to therapy and using this information to automatically adjust infusion rates. The feedback loop captures patient-specific responses and feeds them back to the control algorithm, which then modifies therapy settings to optimize outcomes while reducing manual intervention and associated errors
2Adaptability or versatility
If static adjustment protocols are used, then simple protocols can be followed, but they fail to account for patient-to-patient differences and nonlinear drug responses
Solution Approach 1:
The system applies dynamics by transitioning from static adjustment protocols to dynamic, adaptive control that responds to real-time patient data. The control algorithm continuously adjusts infusion rates based on changing patient conditions, drug accumulation, and nonlinear pharmacodynamic responses, enabling the system to adapt to each patient's unique physiological characteristics and treatment trajectory
Solution Approach 2:
The system implements parameter changes by modifying infusion rates dynamically based on patient-specific control variables such as drug concentration, patient response metrics, and physiological parameters. The control algorithm adjusts these parameters in real-time to account for inter-patient variability and nonlinear drug responses, moving beyond fixed static protocols
3Reliability
If frequent blood glucose measurements and manual adjustments are performed, then intensive insulin therapy can be delivered, but the activity becomes burdensome and time-consuming
Solution Approach 1:
The system applies self-service by automatically performing the tasks of frequent glucose monitoring interpretation and insulin infusion adjustment without requiring manual clinician action. The system autonomously processes glucose measurements and modifies insulin delivery based on predefined control algorithms, maintaining reliable glycemic control while eliminating the burdensome manual workflow
Solution Approach 2:
The system ensures continuity of useful action by maintaining constant monitoring and continuous automatic adjustment of insulin infusion rates. Rather than discrete manual interventions, the system provides uninterrupted closed-loop control that continuously optimizes blood glucose levels, improving both reliability and productivity through seamless automated therapy delivery
Data Source
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AI summary
In example methods and systems described, a medical device can store information locally and in a separate database on a server, for example. If the device fails, or a patient is moved to a second device, information may be transferred to the second device such that the second device can resume a complex therapy at a point where the initial medical device left off. The data necessary to restart the complex therapy system may include certain underlying patient-specific parameters according to a model capturing the patient's physiological response to the medication in question. As a result, it is not necessary for the second device to restart the complex therapy or regress to an initial set of baseline assumptions.