Medical Device for Patient Deterioration Risk Estimation
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Solution Overview
Problem
Current medical devices in hospitals lack an efficient and automated system for monitoring patient deterioration, particularly in in-patient environments, leading to potential delays in responding to critical clinical conditions such as cardiac arrhythmias and breathing disorders.
Innovation Solution
A medical device equipped with sensing electrodes, physiologic sensors, and a processor that acquires ECG and physiologic signals, performs risk assessments using machine learning models, and generates notifications for caregivers when clinical criteria are exceeded, allowing for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and assessment of patient conditions is used, then device complexity is reduced, but response time to critical conditions increases and reliability decreases
Solution Approach 1:
The system performs self-monitoring and self-assessment of patient conditions through automated sensors and processors that continuously evaluate clinical parameters without requiring manual intervention, enabling the system to detect deterioration trends autonomously
Solution Approach 2:
Manual clinical assessment is replaced with electronic sensors, processors, and algorithms that automatically monitor patient conditions, substitute human judgment with computational risk assessment models, and enable objective detection of deterioration
2Loss of time
If continuous automated monitoring is implemented, then response time to critical conditions improves, but device complexity and cost increase
Solution Approach 1:
The system performs preliminary risk assessment by continuously monitoring and evaluating patient conditions before critical events occur, using predictive algorithms to identify deterioration trends early and enable proactive intervention
Solution Approach 2:
The system implements feedback mechanisms where monitored patient data is continuously fed back to the processor, which updates risk assessments in real-time and triggers alerts when thresholds are exceeded, creating a closed-loop monitoring system
3Measurement precision
If multiple sensors and processors are used for comprehensive monitoring, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system uses a multi-functional integrated platform where a single processor handles multiple monitoring functions including ECG analysis, respiratory rate monitoring, oxygen saturation measurement, and risk assessment, reducing the need for separate dedicated devices
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device provides early warnings of patient deterioration, enabling rapid response teams to intervene promptly, thereby improving patient outcomes and reducing adverse events.
Implementation Method 1
The plurality of sensing electrodes is configured to couple externally to a skin of a patient and to acquire electrocardiogram (ECG) signals
Implementation Method 2
The one or more physiologic sensors are configured to couple externally to the patient and configured to acquire one or more physiologic signals
Data Source
AI summary
A medical device for assessing clinical patient deterioration in an in-patient hospital environment is provided. The medical device includes a processor and sensors that couple externally to a skin of the patient to acquire electrocardiogram (ECG) and other physiologic signals. The processor is configured to receive a medical history of the patient; generate physiologic data, including ECG data, over a period of time based on one or more physiologic signals; and execute a risk assessment process associated with a clinical condition of the patient. The risk assessment process analyzes the physiologic data and the medical history of the patient to generate a risk estimate of deterioration of the patient's clinical condition.