Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department

By Yizhao Ni, Stephanie Spellman Kennebeck, Judith W. Dexheimer, Constance M. McAneney, Huaxiu Tang, Todd Lingren, Qi Li, Haijun Zhai, Imre Solti. 2014

103 citations recorded by the source corpus.

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches. Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Goran Nenadic
6. Irena Spasic
7. Yuan Luo

In Health Information Management

1. Yuan Luo
2. Xiaoyu Li
3. Yu Deng
4. Zexian Zeng
5. Tristan Naumann

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[54] used TF-IDF similarity scores calculated from the feature vectors to identify a cohort of patients for”
• Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review (2017, background): “Current NLP systems have proven to be useful for certain activities and have, for example, reduced the time required for screening candidates for clinical trial eligibility [1] and identified potential adverse drug reactions [2].”
• Clinical Text Data in Machine Learning: Systematic Review (2020, methodology): “For instance, historical trial patient enrollment decisions were used to demonstrate the potential of NLP to increase trial screening efficiency by 450% and reduce workload associated with patient cohort identification by 90% [137].”