Table 2 Application cases of the EHR in Clinical Trials processes and outcomes reported in the included studies

Mohammad B Ateya 2016 [16]

EHRs as a potential source for assessing patients' eligibility for enrollment in clinical trials.

Careful design of EHR systems that include data elements representing the content categories will facilitate integration with clinical trial management systems, and improve patient care and clinical research.

Ariel Beresniak 2016 [17]

Reuse of EHR data for executing clinical trials.

Improving clinical trial design and execution using the EHR4CR platform would provide significant benefits for pharmaceutical industry, which serves as the primary sponsor of clinical trials in Europe, and other regions.

Philipp Bruland 2016 [18]

Utilizing EHR data for secondary purposes beyond direct patient care.

Common data elements relevant to clinical trials were identified and their availability within hospital information systems were assessed.

Jake Carrion 2018 [19]

Improvement of patient recruitment, patient retention, and data collection through using the EHR.

Leveraging EHR tools can enhance the way clinical trials are planned and executed.

GeorgesDe Moor 2015 [20]

Streamlining the optimization of clinical trial protocols and facilitating patient identification and recruitment.

EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

Peter J. Embi 2005 [21]

Solution to the common problem of inadequate trial recruitment.

Use of an EHR-based CTA led to significant increases in physicians’ participation in and recruitment rates to an ongoing clinical trial.

Natalie C. Ernecof 2018 [22]

A novel method using an EHR phenotype plus brief medical record review is effective to identify hospitalized patients with late-stage dementia.

In health care systems with similar clinical data warehouses, this method may be applied to serious illness populations to improve enrollment in clinical trials of palliative care or to facilitate access to palliative care services.

Jae Hyun Kim 2021 [23]

Recruitment and observable clinical outcomes of COVID-19 clinical trials.

This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.

This method promises to improve feasibility and efficiency for COVID-19 clinical trial recruitment.

Jeffrey Kirshner 2021 [24]

This tool infers from unstructured EHR data with high accuracy and high confidence in more than 75% of cases, without requiring additional manual review.

This tool could mitigate a key barrier for patient ascertainment and clinical trial participation in community clinics.

Niina Laaksonen 2021 [25]

Identify potential trial participants from the EHR system of a large tertiary care hospital.

The EHR query resulted in a larger patient count than the manual query. Searching for patients with the EHR Research Platform can help identify potential trial participants from a hospital's EHR system by limiting the number of records to be manually reviewed.

Mengyang Li 2021 [26]

Utilizing EHR for designing a patient screening tool for clinical research that provides high-level expressions and improves query performance.

It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers.

Stéphane M. Meystre 2019 [27]

Deploying NLP to extract clinical trial eligibility criteria from EHR clinical notes.

By leveraging machine learning-based NLP, this system can automatically discover eligible patients for clinical trials with good accuracy and reduce the workload of human screening for clinical trials.

Riccardo Miotto 2015 [28]

Identifying eligible patients for clinical trials by processing data from EHR.

Potential candidates for clinical trials can be identified efficiently through applying case-based reasoning model on EHR data.

Sarah J Nelson 2021 [29]

Producing reliable counts of potentially eligible study participants.

The RIC EHR cohort evaluation process is efficient and useful for potential sites for multicenter trials.

Yizhao Ni 2019 [30]

Enhancing the efficient identification of patients through pre-screening based on EHRs, streamlining patient recruitment workflow and increasing enrollment in clinical trials.

By leveraging NLP and machine learning technologies, the Automated Clinical Trial Eligibility Screener (ACTES) demonstrated good capacity for improving efficiency of patient identification.

The quantitative assessments demonstrated the potential of ACTES in streamlining recruitment workflow and improving patient enrollment.

The post evaluation surveys suggested that the system was a good computerized solution with satisfactory usability.

Emily C. O’Brien 2021 [31]

EHR screening was commonly used for recruitment in a cardiovascular outcomes trial.

The EHR is effective in finding potential trial participants.

James R. Rogers 2021 [32]

Using a combination of EHR and trial enrollment data.

Combining trial enrollment data with EHR data may be useful for better understanding of the generalizability of trial results.

Yingcheng Sun 2021 [33]

Using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

The EHRs data are useful for estimating the population representativeness of clinical trial study.

Lindsay P. Zimmerman 2018 [34]

Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. Feasibility of eligibility verification and automated follow-up.

Improvement of recruitment efficiency and engagement traditionally underrepresented individuals in research.