Sensor-Based Patient Positioning Assessment for Outcome Prediction
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Solution Overview
Problem
Healthcare providers lack effective systems for predicting and improving the quality of actions performed on patients, as they often receive insufficient oversight and feedback on their actions, leading to potential errors and injuries.
Innovation Solution
A system utilizing machine learning models to analyze sensor data from various sources to evaluate action performance quality, predict outcomes, and initiate interventions when criteria are not met, including generating quality scores and risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models and sensor data analysis systems are implemented to evaluate and predict action quality, then measurement precision and reliability of action assessment are improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process sensor data and generate quality scores. These models act as mediators between raw sensor inputs and actionable insights, enabling precise action quality assessment without requiring complex direct measurement systems. The ML models translate multi-source sensor data into interpretable quality metrics that guide provider feedback and intervention decisions.
Solution Approach 2:
The patent replaces traditional mechanical or manual observation methods with sensor-based detection systems and machine learning analysis. Instead of relying on human observers to assess action quality, the system uses accelerometers, gyroscopes, and other sensors combined with ML algorithms to automatically evaluate provider actions, reducing subjectivity and increasing measurement precision while managing system complexity through automated processing.
2Reliability
If continuous monitoring and feedback systems are implemented for healthcare providers, then action quality and patient safety are improved, but loss of time for data processing and intervention coordination increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive datasets before deployment. The models are pre-configured with knowledge of safe and effective provider actions, allowing them to rapidly assess new data without requiring time-consuming analysis during critical moments. This preliminary preparation enables real-time quality assessment and immediate feedback to providers, maintaining patient safety while minimizing processing delays.
Solution Approach 2:
The patent establishes continuous feedback loops where quality scores and risk assessments are immediately communicated to healthcare providers. The system provides real-time or near-real-time feedback on action quality, enabling providers to adjust their techniques promptly. This feedback mechanism improves patient safety by ensuring problematic actions are corrected quickly, while the automated nature of the feedback system minimizes time loss compared to manual review processes.
3Measurement precision
If comprehensive sensor data collection from multiple sources is implemented, then measurement precision and completeness of action analysis are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs universal machine learning models that can process multiple types of sensor data from diverse sources including accelerometers, gyroscopes, cameras, and wearable devices. The ML architecture is designed to handle various data formats and sources through unified processing pipelines, enabling comprehensive action analysis without requiring separate specialized systems for each sensor type. This multi-functionality approach improves measurement completeness while managing system complexity through standardized processing methods.
Data Source
AI summary
Techniques for improved machine learning are provided. Sensor data collected by a set of sensors is accessed, the sensor data indicating positioning of a patient in a physical environment. A set of patient characteristics for the patient is determined. An outcome score for the positioning of the patient is generated, using a trained machine learning model, based on the sensor data and the set of patient characteristics. In response to determining that the outcome score does not satisfy one or more criteria, one or more interventions are initiated for the patient.