Appl Clin Inform 2021; 12(01): 017-026
DOI: 10.1055/s-0040-1721010
Research Article

Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

Georg Melzer
1  Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
Tim Maiwald
2  Institute for Electronics Engineering, Department Electrical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
Hans-Ulrich Prokosch
1  Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
Thomas Ganslandt
1  Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
3  Heinrich-Lanz-Center for Digital Health, Department of Biomedical Informatics, Mannheim University Medicine, Ruprecht-Karls-University Heidelberg, Mannheim, Germany
› Author Affiliations
Funding This study was funded in part by the European Commission within the EU/IMI project Electronic Health Records for Clinical Research (EHR4CR), grant no.: 115189.


Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored.

Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset.

Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial.

Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.


The present work was performed in fulfillment of the requirements for obtaining the degree “Dr. med.” from the Friedrich-Alexander University Erlangen-Nürnberg (FAU).

Protection of Human and Animal Subjects

The project was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by the ethics board of the Medical Faculty of the University of Erlangen Nuremberg (247_14Bc).

Supplementary Material

Publication History

Received: 18 May 2020

Accepted: 04 October 2020

Publication Date:
13 January 2021 (online)

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