How One Organization Changed The Way Patients are Identified for Clinical Trials with AI

Introduction

One clinical trial organization was using manual chart review and was looking to reduce the time it takes to find eligible patients.

The Problem

In order to identify eligible patients for clinical trials, research coordinators typically use information from structured data such as ICD codes. Clinicians then combine that knowledge with the information gathered from reviewing a patient’s chart. This manual process takes hundreds of hours and is riddled with inefficiencies and errors. 

Consequently, 40% of clinical trial sites under-enroll compared to plan, and 10% of sites fail to enroll even a single patient

The Goal

This customer wanted to explore if Mendel could replace manual chart review and deliver on two goals:

1. reduce the amount of time it takes to detect eligible patients
2. identify more patients overall  

The Test:

The organization had already manually reviewed 5000 charts in two clinical sites for an oncology trial and identified 26 eligible patients in 1 week.

They tested this against Mendel’s advanced query builder and clinically smart search. Mendel helped the organization index the clinical data of 17,354 patients in the same two clinical research sites in 3 days. 

The Results:

• Leveraging Mendel’s search alone significantly reduced effort to identify eligible patients
• 90% of eligible patients confirmed via chart review ranked within top 250 patients from search results
• Reduced number of patients that need to be reviewed by 95% vs using structured data alone (e.g., ICD codes)

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