Dynamic Surgical Simulation via Patient Cohort Risk Injection
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Solution Overview
Problem
Current medical training simulation systems lack the ability to dynamically adapt to individual patient-specific risks, leading to inadequate preparation for unexpected complications during surgeries, which contributes to medical errors and increased healthcare costs.
Innovation Solution
A computer-implemented method that identifies a patient cohort based on medical information records, determines relevant risk factors, and dynamically injects these risk scenarios into surgical simulations, such as virtual, augmented, or physical training scenarios, using 3D printing to create customized simulation models that reflect the patient's specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical training simulation systems use standardized procedures without patient-specific adaptation, then the training is easier to manage and less complex, but the preparedness for actual patient-specific complications is reduced
Solution Approach 1:
The system performs preliminary analysis of patient-specific risk factors and complications before the surgical procedure. By pre-identifying potential complications based on patient data and cohort analysis, the simulation can be tailored in advance to address specific risks, improving preparedness without adding complexity during the actual procedure.
Solution Approach 2:
The system dynamically adjusts simulation parameters based on patient-specific data, including demographic factors, medical history, and procedure-specific risks. This allows the simulation to adapt to individual patient characteristics while maintaining a standardized framework, resolving the contradiction between customization and complexity.
2Reliability
If medical training simulations include comprehensive patient-specific risk scenarios, then the training effectiveness improves, but the time required for simulation setup and execution increases
Solution Approach 1:
Patient-specific risk factors and cohort analyses are performed in advance before the simulation begins. This preliminary preparation allows the system to pre-load relevant complication scenarios and customize the training content without requiring extensive setup time during the actual simulation execution.
Solution Approach 2:
The system automatically analyzes patient data, identifies risk factors, and generates customized simulation scenarios without requiring manual configuration. This automation reduces the time investment needed for simulation setup while maintaining comprehensive patient-specific training content.
3Measurement precision
If cohort analysis is performed to identify patient-specific risks, then the accuracy of risk prediction improves, but the data processing requirements and system complexity increase
Solution Approach 1:
The cohort analysis process is divided into distinct modules: data collection from electronic health records, demographic matching to identify similar patients, risk factor extraction, and scenario generation. This segmentation allows each component to be optimized independently, improving prediction accuracy while managing system complexity through modular design.
Data Source
AI summary
Disclosed embodiments provide a computer-implemented technique for selection and/or modification of surgical simulation scenarios based on patient similarity cohort identification. Prior to undergoing surgery, medical information of a patient, including genomic, physiological, and/or environmental data, is used to identify a patient cohort. The patient cohort represents a statistically significant sample size of similar patients, and complications that may have arose during similar surgical procedures to the procedure planned for the patient. Relevant scenarios are identified based on the patient cohort. These scenarios are then input to a surgical simulation system. The surgical simulation system may be implemented by virtual reality, augmented reality, and/or physical workpieces used for surgical practice and training. This can increase the probability of a successful outcome for the patient, and furthermore save costs by reducing the risk of malpractice lawsuits, thereby potentially lowering overall healthcare costs.