Smartphone Sensor-Based Surgical Recovery Prediction
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Solution Overview
Problem
Current methods for assessing the efficacy of surgical interventions rely heavily on subjective Patient Reported Outcome Measures (PROMs), which are prone to biases and lack standardization, making it difficult to accurately predict surgical outcomes and recovery patterns.
Innovation Solution
A system that utilizes objective data from smartphones, including accelerometers and gyroscopes, to track patient activity, processing this data with machine learning algorithms to generate granular, real-time analytics and predict surgical efficacy and recovery patterns, providing a standardized and objective measure of patient function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective PROM surveys are used to assess surgical outcomes, then patient self-reported data can be collected, but the measurement precision and objectivity deteriorate due to inherent subjectivity and biases
Solution Approach 1:
The patent replaces the mechanical survey-based assessment system with an automated sensor-based monitoring system. Smartphones with accelerometers, gyroscopes, and other sensors continuously collect objective physical activity data, replacing subjective patient self-reports with automated mechanical measurements of movement, steps, and physical behavior.
Solution Approach 2:
The patent introduces machine learning algorithms and processing systems as intermediaries between raw sensor data and clinical outcome assessment. These intermediaries transform continuous sensor signals into standardized mobility metrics and recovery patterns, objectively mediating between physical activity measurements and surgical outcome evaluation.
2Measurement precision
If granular real-time activity data is collected using sensors, then measurement precision and objectivity improve, but device complexity and data processing requirements increase
Solution Approach 1:
The patent leverages the universal multi-functionality of smartphones, which already contain accelerometers, gyroscopes, GPS, and other sensors for various consumer applications. By repurposing these existing multi-functional devices for clinical monitoring, the system achieves granular measurement precision without adding separate dedicated medical devices, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The patent implements self-service through automated machine learning algorithms that continuously process sensor data in real-time without requiring manual intervention. The system automatically calibrates baseline mobility patterns, detects deviations, and generates clinical assessments autonomously, reducing the operational complexity despite high measurement granularity.
3Reliability
If continuous monitoring is implemented to detect early improvements or worsening, then reliability of outcome assessment improves, but use of energy and data processing load increase
Solution Approach 1:
The patent implements periodic action by analyzing sensor data at strategically determined intervals rather than continuously processing every data point. The machine learning system identifies key temporal patterns and assesses mobility changes at optimal moments, maintaining high reliability for detecting early improvements or worsening while reducing cumulative energy consumption compared to truly continuous analysis.
Data Source
AI summary
Generally described, one or more aspects of the present application relate to enabling determination of a predicted surgical efficacy of a proposed surgical intervention and a predicted pattern of post-operative recovery for the patient. More specifically, the present disclosure provides a system that can analyze the patient data of a plurality of patients data, generate analytics data indicating the post-operative recovery of the plurality of patients, and store the analytics data in association with the biological traits of the patients and the types of surgical interventions that the patients had. Subsequently, the system can analyze the patient traits and activity data of a patient, and output, based on the previously generated analytics data, a prediction of how efficacious a given surgical intervention might be for that patient and/or how the patient might recover from the given surgical intervention.