Table 1 Summary of the basic characteristics of the studies included in the systematic review

Mohammad B Ateya 2016 [16]

USA

To quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous

Ariel Beresniak 2016 [17]

Switzerland

To evaluate the efficacy of EHR4CR (Electronic Health Records for Clinical Research) solutions in comparison to current practices, from the viewpoint of clinical trial sponsors.

Philipp Bruland 2016 [18]

Germany

To determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.

Jake Carrion 2018 [19]

USA

To examine the observed benefits and drawbacks of using Electronic Health Record (EHRs) to enhance various patient-centered aspects of the clinical trial process, specifically its potential to improve patient recruitment, patient retention, and data collection

GeorgesDe Moor 2015 [20]

Belgium

To describe the EHR4CR project which aims to demonstrate a scalable, widely acceptable and efficient approach to interoperability between Electronic Health Record (EHR) systems and clinical research systems.

Peter J. Embi 2005 [21]

Ohio

To determine if the use of EHR-based Clinical Trial Alert (CTAs) could enhance physicians’ participation in subject recruitment and increase physician-generated recruitment rates for an ongoing clinical trial.

Natalie C. Ernecof 2018 [22]

USA

To develop an EHR phenotype for identifying patients with late-stage dementia for a clinical trial of palliative care consultation.

Jae Hyun Kim 2021 [23]

USA

To evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using EHR data

Jeffrey Kirshner 2021 [24]

USA

To facilitate identification of clinical trial participation candidates, the researchers developed a machine learning tool that automates the determination of a patient's metastatic status, on the basis of unstructured EHR data.

Niina Laaksonen 2021 [25]

Finland

To evaluate the accuracy of a commercially available EHR Research Platform, “InSite”, in identifying potential trial participants from the EHR system of a large tertiary care hospital.

Mengyang Li 2021 [26]

China

To develop a patient-screening tool for clinical research using openEHR to address concept mismatch and enhance query performance.

Stéphane M. Meystre 2019 [27]

USA

To assess the feasibility of determining a patient's eligibility for a sample of breast cancer clinical trials by automatically mapping coded clinical trial eligibility criteria to the corresponding clinical information extracted from text in the EHR.

Riccardo Miotto 2015 [28]

USA

To develop a cost-effective, case-based reasoning framework for clinical research eligibility screening by reusing only the EHRs of a minimal number of enrolled participants to represent the target patient for each trial under consideration.

Sarah J Nelson 2021 [29]

USA

To develop a service line for extracting study population estimates from EHR systems to aid in selecting enrollment sites for multicenter clinical trials.

Yizhao Ni 2019 [30]

USA

To evaluate the impact of Automated Clinical Trial Eligibility Screener (ACTES) on the institutional workflow, both prospectively and comprehensively.

Emily C. O’Brien 2021 [31]

USA

To describe the current site-level processes for utilizing the EHR to identify and screen potential participants for an ongoing clinical trial.

James R. Rogers 2021 [32]

USA

To identify the extent of main clinical differences between clinical trial participants and nonparticipants using a combination of electronic health record and trial enrollment data.

Yingcheng Sun 2021 [33]

USA

To systematically estimate the representativeness of the population in clinical trials using EHR data during the early design stage.

Lindsay P. Zimmerman 2018 [34]

USA

To present a novel strategy for recruiting underrepresented, community-based participants for pragmatic research studies that leverage routinely collected EHR data.