Predictive Computing Platform for Clinical Trial Subject Matching
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Solution Overview
Problem
Clinical trials face challenges in identifying and recruiting suitable subjects for participation due to limited resources and inefficient enrollment processes, leading to delayed or unsuccessful execution of programs.
Innovation Solution
A predictive computing platform that analyzes healthcare data to identify suitable subjects for clinical trials by matching patient health attributes with trial criteria, using a digital referral network to securely share screening data between providers and investigators, thereby automating the subject selection and referral process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mass media direct to patient campaigns and principal investigator recruitment techniques are used, then patient enrollment can be achieved, but execution of clinical trials is delayed due to challenges in identifying and recruiting suitable subjects
Solution Approach 1:
The system performs preliminary actions by pre-identifying and pre-screening potential subjects using healthcare data analysis before clinical trials begin. The predictive platform continuously analyzes patient data to create ready-to-enroll subject pools, so when trials start, suitable subjects are already identified and screened, eliminating the time-consuming recruitment phase.
Solution Approach 2:
The patent replaces traditional mechanical recruitment methods (mass media campaigns, manual investigator outreach) with an automated electronic system. The predictive computing platform uses algorithms to automatically analyze healthcare data, identify suitable subjects, and facilitate enrollment, substituting manual processes with automated electronic screening and matching.
2Productivity
If principal investigators and geographic site locations use limited resources to develop and engage existing subject networks, then some enrollment can be achieved, but enrollment targets are missed due to insufficient resources
Solution Approach 1:
The predictive platform serves multiple functions: it identifies subjects for various trial types, screens candidates against different inclusion/exclusion criteria, and can be applied across multiple geographic locations simultaneously. This universal system replaces the need for each investigator to separately develop subject networks, providing a shared resource that achieves enrollment targets with minimal additional resources.
Solution Approach 2:
The system enables self-service by automatically performing subject identification and screening without requiring investigators to manually search for or contact potential subjects. The platform autonomously analyzes healthcare data, applies screening criteria, and presents eligible candidates, allowing investigators to enroll subjects without dedicating significant resources to recruitment activities.
3Productivity
If manual subject identification and screening processes are used, then enrollment can occur, but the process is inefficient and requires significant human intervention
Solution Approach 1:
The patent replaces manual screening processes with automated electronic analysis. The predictive platform uses computing algorithms to automatically review healthcare data, apply inclusion/exclusion criteria, and identify suitable subjects, substituting manual investigator screening with automated electronic processes that require minimal human intervention.
Solution Approach 2:
The system introduces an intermediary predictive platform between the healthcare data and the investigators. This intermediary automatically processes the data matching and screening functions, acting as a mediator that connects patient data with trial requirements without requiring direct manual intervention from investigators in the screening process.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that identifies information about a trial program. The information is related to healthcare data for a subset of providers. The system identifies a provider based on analysis of the information and the healthcare data and provides trial program criteria for analysis at a provider system. The provider system has access to healthcare data for subjects that interact with the provider. The computing system generates data indicating a result of screening each subject by analyzing the trial program criteria against healthcare data for each subject and receives data for a selection of a subject from the provider system. The selection is determined using screening data for the subject. A referral network of the system provides the screening data for access and analysis at an investigator system.