AI helps find clinical trial candidates at Cincinnati Children’s
Cincinnati Children’s Hospital Medical Center has developed artificial intelligence that leverages electronic health records to identify qualified subjects for clinical trials.
Cincinnati Children’s Hospital Medical Center has developed artificial intelligence that leverages electronic health records to identify qualified subjects for clinical trials.
Recruiting the right patients for clinical trials is often a difficult, time-consuming process. Typically, researchers manually screen EHRs to find appropriate study candidates—it can be an inefficient clinical trial recruitment process that yields poor results in terms of identifying patients who meet the eligibility criteria.
To address the problem, Cincinnati Children’s Automated Clinical Trial Eligibility Screener (ACTES) is designed to streamline the process.
“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,” says Yizhao Ni, assistant professor in the UC Department of Pediatrics and Department of Biomedical Informatics. “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.”
In addition to extracting structured information such as patient demographics and clinical assessments from EHRs, ACTES also identifies unstructured data from clinical notes regarding patients’ clinical conditions, symptoms and treatments.
Also See: Northwell Health using AI and NLP to find clinical trial candidates
In tests, ACTES—which is licensed through the medical center's office for technology commercialization, Innovation Ventures—reduced patient screening time by 34 percent and improved patient enrollment by 11.1 percent. Further, the system improved the number of patients screened by 14.7 percent and those approached by 11.1 percent.
Results of the testing were published in the journal JMIR Medical Informatics.
“The ACTES was fully integrated into the clinical research coordinators’ (CRC) workflow in the pediatric emergency department (ED) at Cincinnati Children’s Hospital Medical Center,” states the study. “The system continuously analyzed EHR information for current ED patients and recommended potential candidates for clinical trials. Relevant patient eligibility information was presented in real time on a dashboard available to CRCs to facilitate their recruitment.”
According to the study’s authors, the ED at Cincinnati Children’s Hospital Medical Center provided researchers with a busy clinical environment to test ACTES, where clinical research coordinators were able to recruit patients for six different pediatric clinical trials involving different diseases.
“Thanks to the institution's collaborative environment, we successfully incorporated different groups of experts in designing the integration process of this AI solution,” noted Ni, which included data scientists, app developers, information service technicians and clinical staff.
Recruiting the right patients for clinical trials is often a difficult, time-consuming process. Typically, researchers manually screen EHRs to find appropriate study candidates—it can be an inefficient clinical trial recruitment process that yields poor results in terms of identifying patients who meet the eligibility criteria.
To address the problem, Cincinnati Children’s Automated Clinical Trial Eligibility Screener (ACTES) is designed to streamline the process.
“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,” says Yizhao Ni, assistant professor in the UC Department of Pediatrics and Department of Biomedical Informatics. “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.”
In addition to extracting structured information such as patient demographics and clinical assessments from EHRs, ACTES also identifies unstructured data from clinical notes regarding patients’ clinical conditions, symptoms and treatments.
Also See: Northwell Health using AI and NLP to find clinical trial candidates
In tests, ACTES—which is licensed through the medical center's office for technology commercialization, Innovation Ventures—reduced patient screening time by 34 percent and improved patient enrollment by 11.1 percent. Further, the system improved the number of patients screened by 14.7 percent and those approached by 11.1 percent.
Results of the testing were published in the journal JMIR Medical Informatics.
“The ACTES was fully integrated into the clinical research coordinators’ (CRC) workflow in the pediatric emergency department (ED) at Cincinnati Children’s Hospital Medical Center,” states the study. “The system continuously analyzed EHR information for current ED patients and recommended potential candidates for clinical trials. Relevant patient eligibility information was presented in real time on a dashboard available to CRCs to facilitate their recruitment.”
According to the study’s authors, the ED at Cincinnati Children’s Hospital Medical Center provided researchers with a busy clinical environment to test ACTES, where clinical research coordinators were able to recruit patients for six different pediatric clinical trials involving different diseases.
“Thanks to the institution's collaborative environment, we successfully incorporated different groups of experts in designing the integration process of this AI solution,” noted Ni, which included data scientists, app developers, information service technicians and clinical staff.
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