Overview

AIP accelerates clinical trial operations by empowering trial managers to rapidly identify optimal sites for enhanced recruitment, quality and efficiency. Leverage AI to seamlessly consolidate operational, planning, and third-party sources to create comprehensive site profiles. This allows users to rapidly identify high performing sites and investigators tailored to trial requirements

Features

• Unified Site & Investigator Repository: Leverage AIP's capabilities to unify site and investigator data from various sources (e.g., CTMS, IRT, EDC, CRO data feeds, CT.gov, etc.) into a singular, comprehensive view of the clinical landscape.
• AI-Driven Site Identification: Trial managers can easily assess the quality of sites and investigators for their trials based on AI-generated performance metrics around speed (e.g. activation times), enrollment (e.g. screen fails), and quality (e.g. protocol deviations)
• Dynamic Performance Evaluation: AIP generates real-time reporting at site, network, trial, and portfolio levels, improving the ability to make strategic decisions around objectives such as site network partnerships, country prioritization and cross-study planning.

Extensions & customizations

• Study Financial Planning: Leverage AIP’s Study Financial Planning application to optimize costs of clinical trials during site identification. Enrich site profiles with internal and CRO budget data and generate insights around cost implications of enrolling patients from different countries and sites, facilitating budget negotiations.
• Site Feasibility: Accelerate site selection timelines through AIP’s Site Feasibility solution, reducing repetitive data entry tasks in feasibility questionnaires, enhancing collaboration between CROs, sites and sponsors, and ensuring high quality site selection.
• Study Resource Planning: Optimize employee allocation within and across studies through AIP’s dynamic Study Resource Planning application. Use AI to run wholistic resource forecasting scenarios across portfolios, programs and studies, generate optimal resource allocation schedules, and rapidly adapt to real-time changes within studies.