Trially AI

About Trially AI

This AI-powered platform automates patient identification and recruitment for clinical trials by integrating with electronic health records. It helps research sites accelerate enrollment, reduce screen failures, and improve operational efficiency, ultimately speeding up the development of new therapies.

```xml <problem> Clinical trial enrollment is frequently delayed due to the time-consuming and complex process of identifying eligible patients. Research sites often struggle to efficiently access and interpret patient data within electronic health records (EHRs), leading to extensive manual chart reviews and increased screen failure rates. This inefficiency wastes resources for sponsors and burdens research sites, ultimately slowing down the development of new therapies. </problem> <solution> Trially AI offers an AI-powered platform that streamlines patient identification and recruitment for clinical trials. The platform integrates with existing EHR, CRM, and CTMS systems, unlocking both structured and unstructured patient data to accelerate the enrollment of qualified patients. By automating the extraction and interpretation of complex medical information, Trially reduces the need for manual chart reviews and minimizes screen failures. The AI matching technology analyzes patient records against trial protocols, providing research sites and sponsors with real-time visibility into potential candidates and enabling them to proactively select best-fit trials. </solution> <features> - AI-powered matching technology that analyzes structured and unstructured EHR data, including demographics, diagnoses, medications, imaging, labs, pathology, and physician notes - Protocol analysis that translates complex inclusion/exclusion criteria into filter-friendly study criteria cards within minutes - Real-time patient matching, providing a list of high-quality patient candidates with clear reasons for qualification and visit schedules - Feasibility Explorer that enables sites to proactively select trials aligned with their patient population through clinical data insights and population analytics - Integration with existing EHR, CRM, and CTMS systems via compliant, site-based integrations - Support for sites with or without EHRs, allowing data upload from patient lists - Compliant with HIPAA, SOC 2, FDA 21 CFR Part 11, and ISO 27001 regulations </features> <target_audience> Trially AI serves research sites, hospitals, sponsors, and contract research organizations (CROs) seeking to improve patient enrollment rates, reduce screen failures, and enhance operational efficiency in clinical trials. </target_audience> ```

What does Trially AI do?

This AI-powered platform automates patient identification and recruitment for clinical trials by integrating with electronic health records. It helps research sites accelerate enrollment, reduce screen failures, and improve operational efficiency, ultimately speeding up the development of new therapies.

Where is Trially AI located?

Trially AI is based in Kansas City, United States.

When was Trially AI founded?

Trially AI was founded in 2023.

Who founded Trially AI?

Trially AI was founded by Kyle McAllister.

  • Kyle McAllister - CEO
Location
Kansas City, United States
Founded
2023
Employees
13 employees
Investors
Alpaca VCAtria Ventures

Trially AI

7
Relative Traction Score based on online presence metrics compared to companies in the same age group.

Executive Summary

This AI-powered platform automates patient identification and recruitment for clinical trials by integrating with electronic health records. It helps research sites accelerate enrollment, reduce screen failures, and improve operational efficiency, ultimately speeding up the development of new therapies.

trially.ai700+
Founded 2023 – Kansas City, United States

Funding

Backed by

Alpaca VCAtria Ventures

Team (10+)

Kyle McAllister

CEO

Ramon Prieto

CTO

Company Description

Problem

Clinical trial enrollment is frequently delayed due to the time-consuming and complex process of identifying eligible patients. Research sites often struggle to efficiently access and interpret patient data within electronic health records (EHRs), leading to extensive manual chart reviews and increased screen failure rates. This inefficiency wastes resources for sponsors and burdens research sites, ultimately slowing down the development of new therapies.

Solution

Trially AI offers an AI-powered platform that streamlines patient identification and recruitment for clinical trials. The platform integrates with existing EHR, CRM, and CTMS systems, unlocking both structured and unstructured patient data to accelerate the enrollment of qualified patients. By automating the extraction and interpretation of complex medical information, Trially reduces the need for manual chart reviews and minimizes screen failures. The AI matching technology analyzes patient records against trial protocols, providing research sites and sponsors with real-time visibility into potential candidates and enabling them to proactively select best-fit trials.

Features

AI-powered matching technology that analyzes structured and unstructured EHR data, including demographics, diagnoses, medications, imaging, labs, pathology, and physician notes

Protocol analysis that translates complex inclusion/exclusion criteria into filter-friendly study criteria cards within minutes

Real-time patient matching, providing a list of high-quality patient candidates with clear reasons for qualification and visit schedules

Feasibility Explorer that enables sites to proactively select trials aligned with their patient population through clinical data insights and population analytics

Integration with existing EHR, CRM, and CTMS systems via compliant, site-based integrations

Support for sites with or without EHRs, allowing data upload from patient lists

Compliant with HIPAA, SOC 2, FDA 21 CFR Part 11, and ISO 27001 regulations

Target Audience

Trially AI serves research sites, hospitals, sponsors, and contract research organizations (CROs) seeking to improve patient enrollment rates, reduce screen failures, and enhance operational efficiency in clinical trials.

Sources:

1
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