Cincinnati Children’s Hospital Medical Center researchers have designed and tested a new, artificial intelligence-based computerized solution to effectively identify eligible subjects from electronic health records (EHRs) as a mission-critical tool to help busy clinical operations staff to evaluate and enroll study patients.

The Cincinnati Children’s Hospital Medical Center (CCHMC) published the results of the effort in JMR Medical Informatics

The Study

CCHMC designed a system and ran a study showing that compared to manually screening EHRs to identify study candidates, the system—called the Automated Clinical Trial Eligibility Screener®(ACTES)—reduced patient screening time by 34% and improved patient enrollment by 11.1%. The system also improved the number of patients screened by 14.7% and those approached by 11.1%

How it Works

Based on natural language processing capabilities, the new patient recruitment tool allows computers to understand and interpret human language as the system analyzes large amounts of linguistic data. Machine learning allows computerized systems to automatically learn and evolve from experience without specifically being programmed. This makes it possible for computer programs to process data, extract information and generate knowledge independently.

The system can extract structured information such as patient demographics and clinical assessments from EHRs. It also identifies unstructured information from clinical notes, for example, including patients’ critical conditions, symptoms, treatments and the like. The extracted information is then matched with eligibility requirements to determine a subject’s suitability for a special clinical trial.

The data within the system, as it accumulates, becomes “trainable.” Then the system’s machine learning component allows it to learn from historical enrollments to improve its future recommendations. CCHMC’s solution handles analyses by carefully designed AI algorithms, essentially procedures or formulas that computers use to solve problems by programming a set sequence of specified actions.

Lead Principal Investigator Comments

Yizhao Ni, Ph.D., Division of Biomedical Informatics, commented that busy emergency departments often serve as excellent locations for clinical trial coordinators to find people who may be good study candidates. Ni commented that ACTES is designed to streamline what often proves to be an inefficient clinical trial recruiting process that doesn’t always catch enough qualified candidates. Specifically, “because of the large volume of data documented in EHRs, the recruiting processes used now to find relevant information are very labor-intensive within the short time frame needed.” Ni continued “by leveraging natural language processing and machine learning technologies, ACTES was able to quickly analyze different types of data and automatically determine patients’ suitability for clinical trials.”

System Advanced to Live Clinical Setting

Previously the system was successfully pilot tested in a retrospective study published in 2015 by the Journal of the American Medical Informatics Association. The current study tested the solution prospectively and in real-time in a busy emergency department environment, where clinical research coordinators recruited patients for six different pediatric clinical trials involving different diseases.

Using the technology in a live clinical environment involved significant collaboration between data scientists, application developers, information service technicians, and the end-users, clinical staff.

“Thanks to the institution’s collaborative environment, we successfully incorporated different groups of experts in designing the integration process of this AI solution,” Ni said.

Outstanding Issues

The researchers listed as limitations the small number of clinical trials used in the study, all from a single clinical department. They also pointed to some lingering issues involving the system’s accuracy at interpreting data. These issues will be resolved in future studies through ongoing enhancements to the technologies and also by testing the system in a wider variety of clinical departments, according to the investigators.


Funding support for the study came in part from the National Institutes of Health (1R01LM012230, 1U01HG008666, 5U18DP006134), Agency for Healthcare Research and Quality (1R21HS024983), and from Cincinnati Children’s Hospital Medical Center. The production of ACTES is licensed through the medical center’s office for technology commercialization, Innovation Ventures.

Source: EurekAlert!

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