Transform Trial Feasibility with AI

Manually interpreting complex eligibility criteria is a thing of the past. With BEKhealth’s trial feasibility AI, you can instantly convert vague inclusion criteria into precise patient-matching algorithms—ensuring faster, smarter trial feasibility assessments.

In this demo, we show how our advanced AI seamlessly translates eligibility text into actionable queries, identifying a cohort of 45,000 matched patients in seconds. This means less guesswork, fewer delays, and more successful trials.

Feasibility Reports and Insights

BEKplatform facilitates powerful decision making from easily digestible reports, allowing researchers to access real-time patient data and use it to analyze their patient populations, quickly determine trial feasibility, automate processes and identify untapped trial opportunities.

Turn unstructured data into searchable information

BEKplatform accesses the saved files, reports, and even hand-written notes associated with patient charts, then divides each note/record into sentences and transforms the underlying text.

The platform then analyzes and transforms text using deep learning neural net based on BERT to identify medical entities and associated attributes for key domains (ex: demographics, diagnosis, medications, etc.).

The text is then mapped to a searchable ontology made of more than 24 million search terms, synonyms, and lexemes. Combined with structured medical data, BEKplatfom generates a synthesized, longitudinal patient graph.

To support it's industry-leading 93% accuracy in interpreting EMR records, BEKhealth employs a human-in-the-loop feedback mechanism to hone the models’ outputs, ensuring a high level of reliability and relevance to clinical contexts.

Imagine if AI powered every step of your recruitment workflow...

BEKplatform identifies the clinically-qualified patients. Delfa enrolls them over voice, chat, and text. It’s an AI-driven experience from data to doorstep—saving time, reducing friction, and helping sites hit their goals.