An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials

TL;DR

This paper presents an information extraction system that automates the prescreening of heart failure patients for clinical trials by converting unstructured electronic health record data into structured formats, significantly reducing screening time.

Contribution

The study introduces an open-source, rule-based information extraction approach that accurately identifies eligible patients from unstructured clinical notes, streamlining trial recruitment processes.

Findings

Achieved 0.95 recall and 0.86 precision in patient eligibility classification.

Reduced prescreening time from weeks to minutes.

Provided open-source tools for broader application in cardiovascular trials.

Abstract

To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators. However, a major obstacle is the vast amount of patient data available as unstructured free-form text in electronic health records. Here we propose an information extraction-based approach that first automatically converts unstructured text into a structured form. The structured data are then compared against a list of eligibility criteria using a rule-based system to determine which patients qualify for enrollment in a heart failure clinical trial. We show that we can achieve highly accurate results, with recall and precision values of 0.95 and 0.86, respectively. Our system allowed us to significantly reduce the time needed for prescreening patients from a few weeks to a few minutes. Our open-source information extraction modules are available for researchers and could be tested and validated in other cardiovascular trials. An approach such as the one we demonstrate here may decrease costs and expedite clinical trials, and could enhance the reproducibility of trials across institutions and populations.