Clinical Trial Supply Forecasting via RTSM API Integration
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Solution Overview
Problem
Current clinical trial supply forecasting systems are inadequate as they often fail to accurately match the resupply algorithm of Randomization and Trial Supply Management (RTSM) systems, leading to wasteful oversupply or stock-outs, and are not integrated with RTSM systems, requiring manual data mapping and lacking flexibility in handling unpredictable demand and real-time data updates.
Innovation Solution
A computer-implemented method and system that integrates with RTSM systems via an API for real-time data exchange, uses natural language processing to extract trial parameters from various file formats, and calculates a supply plan based on patient quantity, sites, and confidence values, enabling flexible forecasting and automatic notification of deviations from the supply plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data mapping and non-integrated forecasting systems are used, then system complexity is reduced, but forecasting accuracy deteriorates and time consumption increases
Solution Approach 1:
The patent introduces an API intermediary layer that connects the forecasting system with the RTSM system. This mediator enables automated data exchange and integration without requiring complex direct system coupling, thus improving forecasting accuracy through real-time data while maintaining manageable system complexity through standardized interface protocols.
Solution Approach 2:
The patent merges the forecasting system with the RTSM system through API integration, combining previously separate functions into a unified workflow. This integration allows automated data mapping and real-time supply plan adjustments, significantly improving forecasting accuracy by eliminating manual data transfer errors and delays.
2Productivity
If manual data mapping is used, then automation level is reduced, but system complexity is reduced, but productivity deteriorates
Solution Approach 1:
The system implements self-service automation where the forecasting system automatically maps and extracts data from RTSM system files through API calls. This eliminates manual data mapping operations, significantly improving productivity by enabling automated supply plan calculations while the system handles data transformation and integration tasks independently.
Solution Approach 2:
The patent replaces manual mechanical data mapping processes with automated electronic data extraction and transformation through API interfaces. This substitution of manual operations with automated computational processes dramatically improves productivity by eliminating repetitive manual tasks and reducing errors in data mapping.
3Loss of substance
If fixed supply plans are used, then adaptability is reduced, but system complexity is reduced, but loss of substance increases
Solution Approach 1:
The patent implements dynamic supply planning where the supply plan is automatically recalculated and adjusted based on real-time trial status data from the RTSM system. This dynamic adaptation allows the system to respond to changing trial conditions, optimizing drug allocation and reducing wastage by adjusting supply levels to actual demand rather than following fixed predetermined plans.
Solution Approach 2:
The system incorporates feedback loops where trial status data is continuously monitored and fed back into the forecasting model. This feedback mechanism enables automatic adjustments to the supply plan based on actual trial progression, patient enrollment rates, and site performance, thereby reducing drug wastage by aligning supply with actual demand while maintaining system flexibility.
4Loss of time
If delayed data updates are used, then automation level is reduced, but measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent implements continuous automated monitoring of trial status through scheduled API calls to the RTSM system. This continuous data retrieval ensures that the forecasting system always has up-to-date information about trial progression, patient enrollment, and site status, minimizing time delays while maintaining high measurement precision through frequent automated updates rather than manual periodic reviews.
Data Source
AI summary
Systems and methods for demand and supply forecasting for clinical trials are disclosed. An embodiment of a computer-implemented method may include receiving, by a server from a user, one or more electronic files that contain parameters of a clinical trial, the parameters comprising a quantity of patients, a plurality of sites, and one or more confidence values, calculating, by the server, according to the quantity of patients, a respective demand profile for each of the plurality of sites, calculating, by the server, according to the one or more confidence values, a buffer quantity for each of the plurality of sites, calculating, by the server, a supply plan for the clinical trial according to the demand profiles and the buffer quantities, and transmitting, by the server, the supply plan to the user.