Srotas Health Ltd develops Srotas Connect, an AI-driven platform that utilizes behavioral science to enhance patient identification and recruitment for clinical trials. By processing large health datasets in real-time, the platform significantly accelerates candidate selection and improves retention rates, addressing inefficiencies in traditional trial methodologies.
Funding
Funding not disclosed
Founders
Product
Problem
Oncology clinical trials often face delays due to inefficient patient identification and recruitment processes. Clinicians struggle to manage unstructured patient data and lack real-time insights, hindering their ability to enroll suitable candidates and ensure optimal patient care. Traditional methods are time-consuming and can lead to high retention costs.
Solution
Srotas Health offers Srotas Connect, an AI-driven platform designed to accelerate patient identification, streamline recruitment, and improve retention rates for oncology clinical trials. The platform processes large volumes of health data in real-time, leveraging behavioral science and generative AI to identify ideal candidates and personalize the trial approach. Srotas Connect analyzes patient preferences, automates communication, and predicts patient engagement, enabling clinical researchers to conduct more effective and efficient trials. The platform also provides clinicians with comprehensive insights from complex data, facilitating better medical decision-making and improved patient outcomes.
Target Audience
The primary target audience includes oncology researchers, clinicians, pharmaceutical companies, and contract research organizations (CROs) involved in clinical trials.
Features
- AI-based patient identification that processes terabytes of health data to identify suitable candidates
- Behavioral analysis for recruitment, including online consent forms, appointment bookings, and tele-consultations
- Generative AI to automate email communications and virtual consultations
- On-trial prognosis using real-time data and biomarker analysis to predict patient engagement and response to treatment
- LLM-based medical search to extract actionable insights from unstructured clinical data
- Cohorting and feasibility analysis for improved trial planning
- Real-time notifications for simplified patient matching
- DICOM analysis capabilities