This post originally appeared on Fast Data Science’s blog on LinkedIn.
Clinical trials are at the heart of medical advancement but have considerable challenges, especially in risk management and cost estimation. Accurate forecasting of these factors is essential for a successful trial. Fast Data Science introduces AI-powered solutions that streamline and significantly enhance this process.
The financial stakes in clinical trials are high, with costs spanning several phases, locations, and participant demographics. Tr trials can avoid budget overspending, delays, and even early termination if these factors are managed effectively. That’s where AI can step in to help optimize and predict risk and cost.
Fast Data Science’s Clinical Trial Risk Tool enables trial managers to predict costs and risks precisely using artificial intelligence (AI) and natural language processing (NLP). The tool evaluates potential risks and offers accurate cost estimates by analyzing clinical documents, helping research teams manage financial resources and reduce trial disruptions.
Handling drug-related data efficiently is crucial in clinical trials. Fast Data Science’s Drug Name Recognizer plugin for Google Sheets automates the identification of drug names in large datasets. It tags each drug with its DrugBank ID and MeSH ID and links to the NHS, providing trial managers with valuable information at the click of a button.
This plugin is handy when managing trials involving complex drug regimens. By reducing manual data entry, the Drug Name Recognizer speeds up assessing drug interactions, monitoring adverse events, and cross-referencing with regulatory databases—essential steps in the risk analysis process.
For example, manually tracking information can introduce human errors, potentially increasing trial risks when working with hundreds of drug names across various locations. By integrating this plugin, teams can ensure that drug-related data is processed accurately, leading to more reliable risk and cost predictions.
By incorporating tools like the Clinical Trial Risk Tool and the Drug Name Recognizer plugin, Fast Data Science provides trial managers with a seamless, automated experience. Both tools support more efficient trial management, ensuring trials remain on track financially and operationally.
Whether in the early stages of budgeting or deep into participant management, these AI-powered solutions help reduce time, effort, and uncertainty in your clinical trials. As a result, you can allocate resources more effectively, manage risks with greater precision, and bring innovations to market faster.
Clinical trials demand careful planning and resource management. With Fast Data Science’s suite of AI-driven tools—such as the Clinical Trial Risk Tool and the Drug Name Recognizer plugin—you can streamline processes, reduce costs, and confidently manage risks. These innovations save time and ensure that trials are run efficiently, allowing research teams to focus on what truly matters: advancing healthcare.
Try today’s Clinical Trial Risk Tool and Drug Name Recognizer plugin to enhance your cost and risk estimation strategies.
#ClinicalTrials #CostEstimation #AI #HealthcareInnovation #DrugData
Introduction People have asked us often, how was the Clinical Trial Risk Tool trained? Does it just throw documents into ChatGPT? Or conversely, is it just an expert system, where we have painstakingly crafted keyword matching rules to look for important snippets of information in unstructured documents? Most of the tool is built using machine learning techniques. We either hand-annotated training data, or took training data from public sources. How We Trained the Models inside the Clinical Trial Risk Tool The different models inside the Clinical Trial Risk tool have been trained on real data, mostly taken from clinical trial repositories such as clinicaltrials.
Over the years, the overall cost of the drug development process has been exponentially increasing, prompting the adoption and use of adaptive clinical trial design software. Though there are practical difficulties and barriers in implementing clinical trial solutions, these problems are adequately addressed to overcome these issues as they arise. With advancements in software technologies, further improvements are being made to the software’s adaptive clinical trial design. Despite these progresses, just only a handful of well-established software with various types of clinical trial adaptations is currently available.
A clinical trial protocol is a document which serves as the step-by-step playbook for running the trial. The clinical trial protocol guides the study researchers to run the clinical trial effectively within a stipulated period. The prime focus of the clinical trial protocol is to ensure patients’ safety and data security. [1, 2] As the clinical trial protocol is an essential document for the seamless execution of the clinical trial, reviewing (peer-reviewing) the protocol is essential to ensure the scientific validity/viability/quality of the protocol.
