This post originally appeared on Fast Data Science’s blog on LinkedIn.
Clinical trial protocols are often long, detailed documents—sometimes 200 pages—filled with vital information about sample size, treatment methods, and statistical plans. These protocols ensure the effective conduct of trials, but their complexity increases the time needed for manual reviews and the risk of human error.
This is where Natural Language Processing (NLP) steps in. NLP enables machines to “read” unstructured data, such as clinical trial protocols, and extract key insights. By turning complex text into structured data, NLP makes it possible to perform detailed risk assessments quickly and accurately.
In the pharmaceutical industry, NLP is revolutionising processes, from pre-clinical research to protocol reviews and aftermarket monitoring. One such innovative application is the Clinical Trial Risk Tool, a powerful solution designed by Fast Data Science to simplify risk assessment and cost prediction.
When assessing clinical trial risks, the Clinical Trial Risk Tool uses a machine learning model to categorise protocols as High, Medium, or Low risk of ending uninformatively. But what does this mean?
Here’s how the tool works:
High Risk: Protocols with major design flaws, such as missing statistical analysis plans (SAPs) or insufficient sample sizes, increasing the likelihood of an inconclusive trial.
Medium Risk: Protocols that meet some key requirements but may have areas of concern, such as complex trial sites or unconventional endpoints.
Low Risk: Well-structured protocols that are adequately powered, have completed SAPs, and align with best practices.
The tool’s risk scoring formula (0–100) evaluates key features like:
Number of participants and study arms
Countries involved in the trial
Use of randomisation and simulations for sample size calculation
By automating this process, the tool enables more consistent, objective risk assessments, reducing human subjectivity.
The Clinical Trial Risk Tool initially supported trials for Tuberculosis (TB) and HIV, but it has since expanded to cover other critical disease areas, such as:
Oncology: Cancer trials often have complex multi-site designs and multiple endpoints, making risk assessment crucial.
COVID-19: Given the urgency of developing vaccines and treatments, this tool has been used to assess trials under accelerated timelines.
Neglected Tropical Diseases (NTDs): These trials often occur in low- and middle-income countries (LMICs), where limited resources make efficient protocol design essential.
For example, a pharmaceutical company conducting an oncology trial used the Clinical Trial Risk Tool to flag that its protocol lacked a clear effect estimate and had an overly ambitious sample size. This allowed the team to revise its design and prevent delays.
Whether you’re a pharmaceutical company, a funding organisation, or a regulatory body, the Clinical Trial Risk Tool offers several key benefits:
Improved Accuracy: The tool uses NLP to analyse clinical trial protocols, ensuring no crucial details are overlooked and providing data-driven insights to reduce the risk of human error.
Faster Protocol Reviews: The drag-and-drop PDF upload feature enables non-technical users to upload documents and receive a risk score in minutes, reducing review times by up to 50%.
Better Cost Prediction: The tool identifies cost-driving factors such as sample size, study visits, and trial site complexity, helping stakeholders optimise trial budgets.
Consistency: With its structured scoring system, the tool provides consistent evaluations across multiple trials, ensuring fairness and transparency.
The Clinical Trial Risk Tool is not just a time-saver—it’s a game-changer for clinical trial planning and optimisation. By harnessing the power of AI and NLP, you can improve protocol design, prevent costly delays, and ensure your trials deliver informative results.
Ready to see how it works?
🔗 Try the Clinical Trial Risk Tool today: Click here to get started
Want a guided walkthrough?
🎥 Watch this short demo: How to Use the Clinical Trial Risk Tool
Don’t let uninformative trials slow down your research—leverage AI-driven insights to make smarter, faster decisions.
#ClinicalTrials #NLP #AIInPharma #ClinicalResearch #RiskAssessment
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Designing a high-quality clinical trial protocol is critical for the success of any study. A protocol is the blueprint that outlines every aspect of a trial. In an ideal world, a flawless protocol would require no revisions and include only essential elements. In reality, however, the average protocol undergoes 2–3 amendments and often contains excessive data collection and overly complex entry criteria.
Clinical trials have long been the foundation of medical breakthroughs, but traditional methods often stumble over slow timelines, high costs, and difficulties in finding the right participants. Artificial intelligence (AI) — a technology ready to transform this landscape by making trials faster, more affordable, and smarter. The accelerating adoption of AI in clinical trials signals a major shift in healthcare research. It is already making significant strides in transforming clinical trials.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan In clinical trials, a staggering 80% encounter delays during the startup phase and 37% struggle to meet enrollment targets. Read more Key clinical trial statistics. These figures highlight a critical, yet often underemphasized, aspect of clinical trials—the feasibility process. The feasibility process is essential for assessing the practicality of a clinical trial’s design, ensuring the study is prepared to tackle the challenges that may arise.
