Patient Recovery Tracking via Sensor-Based Predictive Modeling
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Solution Overview
Problem
Current medical recovery protocols for orthopedic procedures rely on standardized and subjective measures, leading to potential overestimation or underestimation of patient recovery, particularly in walking parameters and pain levels, resulting in inadequate post-surgical treatment.
Innovation Solution
A system that uses automated, objective measurements from physical sensors to track pre- and post-surgical walking parameters and pain levels, implementing a predictive model based on patient demographic data and comorbidities to accurately predict recovery trends, comparing actual post-procedural states to predicted trends for personalized treatment adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standardized recovery protocols are used to gauge patient recovery, then the recovery process can be monitored and appointments can be scheduled, but the accuracy of recovery assessment deteriorates due to overestimation or underestimation of patient-specific recovery paths
Solution Approach 1:
The system performs preliminary action by establishing a patient-specific predictive recovery model before the actual recovery process begins. The predictive model is trained on pre-surgical data and historical patient data to forecast expected recovery trajectories, allowing the system to proactively identify deviations from expected recovery paths and adjust treatment accordingly
Solution Approach 2:
The system applies parameter changes by transitioning from fixed standardized recovery parameters to dynamic, patient-specific parameters. The predictive model continuously adjusts recovery expectations based on individual patient characteristics, surgical details, and real-time sensor data, transforming static protocols into adaptive recovery guidelines
2Measurement precision
If self-reporting of walking parameters is used, then data collection is simple and low-cost, but measurement precision deteriorates due to subjective bias and patient reporting errors
Solution Approach 1:
The system replaces the mechanical/manual self-reporting method with automated sensor-based measurement. Physical sensors embedded in footwear or worn by the patient automatically capture walking parameters such as step count, walking distance, speed, and gait characteristics, eliminating subjective bias and reporting errors while providing objective, continuous monitoring
Solution Approach 2:
The system introduces an intermediary layer between the patient and the data collection process. Rather than directly relying on patient self-reporting, the system uses physical sensors as intermediaries to objectively measure walking parameters, which are then processed by the predictive model to assess recovery progress
3Reliability
If more post-surgical treatment is provided to ensure adequate recovery, then recovery outcomes may improve, but unnecessary treatment increases healthcare costs and patient burden
Solution Approach 1:
The system implements continuous feedback by comparing actual recovery progress against the patient-specific predictive model. When actual measurements deviate from expected trajectories, the system provides feedback to clinicians to adjust treatment intensity, ensuring that post-surgical care is optimized based on real-time recovery status rather than following fixed protocols
Solution Approach 2:
The system applies dynamics by making the treatment plan adaptive and flexible rather than static. The predictive model allows treatment intensity to dynamically adjust based on individual patient recovery rates, enabling the system to intensify treatment when needed and reduce it when recovery is proceeding as expected, thereby optimizing resource utilization
Data Source
AI summary
An apparatus and method are provided that tracks patient recovery following an orthopedic procedure. A statistical computing engine implements a predictive model of the patient's post-procedural state for the orthopedic procedure based on a database of patient demographic data, comorbidities, pre-procedural walking parameters, including steps taken, and the orthopedic procedure that the patient is undergoing. The pre-procedural walking parameters are populated from physical sensor data automatically collected from the patient. The predictive model uses machine learning and is trained using training data sets. The predictive model creates a temporal trendline of post-procedural walking parameters, including steps taken, and a temporal trendline of post-procedural pain level. A processor then compares the patient's actual post-procedural state to the predictive model of the patient's post-procedural state. The post-procedural walking parameters are also obtained from the physical sensor.