Clinical Research Performance Monitoring System
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Solution Overview
Problem
The clinical research industry faces challenges in determining the most suitable and efficient investigators or research sites for clinical research studies due to incomplete and labor-intensive data collection from multiple repositories, which hinders the selection process.
Innovation Solution
A system utilizing machine learning models to collect and evaluate metrics for clinical research performance across various domains, producing performance scores and ranks for entities like investigators and research sites, and applying predictive analytics to match them with protocol eligibility criteria, thereby optimizing site selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from multiple data repositories manually, then information completeness may be improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical data collection processes with automated computer-based systems. The system automatically accesses multiple data repositories, retrieves relevant data, and processes it through algorithms to generate performance scores, eliminating the need for manual data gathering while maintaining information completeness.
Solution Approach 2:
The system enables self-service by automatically performing data collection, processing, and analysis without requiring manual intervention. The computer system independently queries multiple repositories, processes the data through predefined algorithms, and generates performance assessments autonomously.
2Loss of information
If multiple data repositories are accessed manually, then comprehensive information may be obtained, but labor intensity increases
Solution Approach 1:
The patent replaces manual labor-intensive data collection with automated computer-based retrieval systems. The system automatically connects to multiple data repositories, extracts relevant information, and processes it through computational algorithms, eliminating manual effort while maintaining comprehensive data collection.
Solution Approach 2:
The patent introduces an intermediary computer system that acts as a mediator between users and multiple data repositories. This intermediary automatically handles data retrieval, processing, and analysis, reducing the need for direct manual interaction with multiple sources while ensuring comprehensive information gathering.
3Adaptability or versatility
If performance assessment is done without standardized metrics, then flexibility may be maintained, but measurement precision decreases
Solution Approach 1:
The patent applies parameter changes by establishing standardized performance metrics and weighting factors that can be adjusted based on different study requirements. The system uses defined parameters for data quality, completeness, and timeliness, allowing flexible adaptation to different research contexts while maintaining precise measurement through consistent computational algorithms.
Data Source
AI summary
A computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger. Actions are performed based on the resulting performance scores, rank and/or match.