Patient Model Mapping to Clinical Trials via Synthetic Patient Proxies
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Solution Overview
Problem
Physicians face difficulty in identifying relevant clinical trials for specific patient conditions due to varying inclusion and exclusion criteria, and differing metrics across trials, leading to a lag in incorporating clinical trial results into routine patient care.
Innovation Solution
A system that maps a patient to relevant clinical trials by generating synthetic patients based on trial participant characteristics, comparing the patient's model to these synthetic patients using similarity criteria, and ranking trials based on matching quality weights to facilitate faster incorporation of trial results into clinical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually review multiple clinical trials to identify relevant ones, then they can find applicable trials, but the process is time-consuming and delays incorporation of trial results into patient care
Solution Approach 1:
The system creates synthetic patient representations that copy the essential characteristics and data structures of real patients from clinical trials. These synthetic patients serve as proxies to automatically match against actual patient data, enabling rapid identification of relevant trials without manual review while maintaining accurate matching based on inclusion/exclusion criteria
Solution Approach 2:
The system transforms clinical trial data into standardized parameter representations and compares them against patient parameters using automated algorithms. By changing the evaluation from manual qualitative assessment to automated quantitative parameter comparison, the system dramatically reduces time while maintaining or improving identification accuracy
2Adaptability or versatility
If clinical trials use different inclusion and exclusion criteria and metrics, then they can address specific research questions, but physicians struggle to generalize results across trials
Solution Approach 1:
The system creates a universal framework that can handle multiple different trial criteria and metrics through a common synthetic patient representation. This universal approach allows the same system to evaluate diverse trial types (different diseases, treatments, criteria) without requiring separate analysis methods, enabling physicians to generalize results across heterogeneous trials
Solution Approach 2:
The system standardizes diverse trial criteria into comparable parameter representations. By transforming different inclusion/exclusion criteria and metrics into a unified parameter space, the system enables automated comparison and generalization across trials that would otherwise be incompatible, reducing the complexity of cross-trial analysis
3Reliability
If physicians review all available clinical trials, then they ensure comprehensive coverage, but the large number of trials with varying criteria makes identification difficult
Solution Approach 1:
The system uses synthetic patients as copies or proxies for real patients in trial databases. These synthetic representations enable automated bulk comparison against patient data, ensuring comprehensive screening of all relevant trials while making the process operationally simple through automated matching algorithms
Solution Approach 2:
The system performs self-service by automatically executing the trial identification process without requiring physician intervention in the search and filtering steps. The automated system independently handles data comparison, matching, and result generation, maintaining reliability through systematic comprehensive review while dramatically improving ease of operation
Data Source
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AI summary
Systems and methods for mapping a patient to one or more clinical trials are provided. Patient data of a patient is received and encoded into a patient model of the patient. Synthetic patients are generated for each clinical trial in a set of clinical trials based on characteristics of participants in that clinical trial. The patient model of the patient is compared to each of the synthetic patients to identify synthetic patients matching the patient model. The patient is mapped to one or more clinical trials in the set of clinical trials based on the matching synthetic patients.