Automated Cohort Selection for Decentralized Clinical Trials
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Solution Overview
Problem
The manual and inefficient process of defining and managing cohorts in research studies, which often involves selecting participants from multiple sources and lacks access to underlying data for eligibility determinations, limits the precision and effectiveness of cohort selection.
Innovation Solution
A computer system utilizing machine learning techniques to enhance cohort definition and management by providing tools for querying databases, suggesting selection criteria, predicting participant eligibility, and generating customized communications to improve study outcomes, while also aiding in research question design and study parameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used to define cohorts by assessing data from disparate sources, then flexibility in cohort definition is maintained, but the process becomes difficult and time-consuming
Solution Approach 1:
The system enables automated cohort definition by having the computer system independently assess candidate data against selection criteria without requiring manual intervention. The system self-serves by automatically querying databases, evaluating eligibility, and generating cohort lists, thereby resolving the contradiction between operational flexibility and time efficiency.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computer-based systems. The computer system queries databases, evaluates candidate attributes, and determines cohort eligibility automatically, substituting human manual labor with computational processes that are both faster and maintain flexibility through programmable criteria.
2Loss of information
If traditional research management software is used, then basic cohort selection is possible, but access to underlying data for eligibility determinations is limited
Solution Approach 1:
The system segments the data access process by separately querying different databases (e.g., demographic data, health data, genetic data) and then integrating the results. This segmentation allows comprehensive data access while maintaining organized, precise eligibility determinations for each candidate based on relevant data segments.
Solution Approach 2:
The computer system performs multiple functions: querying diverse databases, evaluating eligibility criteria, generating cohort lists, and providing recommendations. This multi-functional approach ensures comprehensive data access and precise eligibility determination within a single integrated system.
3Adaptability or versatility
If decentralized clinical trials are implemented to improve recruitment, then participant diversity increases, but the complexity of managing dispersed cohorts increases
Solution Approach 1:
The computer system acts as an intermediary between dispersed candidates and the research study. It automatically queries candidate data from various sources, evaluates eligibility against study criteria, and manages cohort selection, thereby simplifying the management of decentralized trials while maintaining high adaptability in recruitment.
Solution Approach 2:
The system provides feedback by identifying candidates who meet selection criteria and highlighting those who lack requirements but could comply with further actions. This feedback mechanism guides researchers in managing dispersed cohorts efficiently by clearly indicating eligibility status and potential pathways to compliance.
4Measurement precision
If comprehensive data assessment is performed to ensure cohort eligibility, then selection precision improves, but the difficulty and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-querying databases for candidate data and pre-evaluating eligibility against selection criteria before final cohort selection. This preliminary assessment organizes and prepares data in advance, enabling precise cohort selection without increasing the complexity of the final decision process.
Data Source
AI summary
In some implementations, one or more computing devices receive data indicating selection criteria for a cohort through an interface. The one or more computing devices determine a first set of candidates classified as having attributes that satisfy the selection criteria. The one or more computing devices also determine a second set of candidates that satisfy a subset of the selection criteria and are determined to not satisfy a same one or more criteria of the selection criteria. The one or more computing devices provide output data through the interface that includes (i) data indicating the first set of candidates, (ii) data indicating the second set of candidates, and (iii) data indicating the one or more selection criteria not satisfied by the members of the second set of candidates.