Assessing an Electronic Health Record research platform for identification of clinical trial participants

Laaksonen, Niina; Varjonen, Juha-Matti; Blomster, Minna; Palomäki, Antti; Vasankari, Tuija; Airaksinen, Juhani; Huupponen, Risto; Scheinin, Mika; Blomster, Juuso

doi:10.1016/j.conctc.2020.100692

Cited by 6 publications

(11 citation statements)

References 8 publications

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“…There are local and national regulations on the quality and characteristics of EHR systems in general, but there is a lack of unified descriptions for EHR data, and the functionality requirements of EHR systems are not enforced in a systematic manner. 23 The integration of EHR research platforms in more orthopaedic-based practice seems likely in the near future.…”

Section: Discussionmentioning

confidence: 99%

“…Only one in three trials reaches its recruitment target in the predefined timeframe, whereas the remaining two-thirds of all trials either never reach the targeted sample size or the trial duration is extended because of slower than anticipated recruitment. 23 Due to recent advances in electronic health record (EHR) query technology, the use of EHR data for patient recruitment is currently seen as a promising tool to improve identification and recruitment of trial participants and reduce the burden of manual work, including manual chart reviews for screening patients. EHR research platforms appear to be a feasible tool with potential for identifying eligible patients for clinical trials, or enabling to limit the population of possibly eligible patients and decreasing the need for manual review work.…”

Section: Recommendationsmentioning

confidence: 99%

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Exploring Differences in Screening and Enrollment Metrics in Orthopaedic Clinical Trials

Stiegel,

Visperas,

Piuzzi

et al. 2023

J Knee Surg

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Background: The success of any clinical trial relies heavily on patient recruitment and retention. The purpose of this study was to review screening and enrollment metrics for orthopaedic clinical trials, comparing different patient populations to determine common challenges to recruitment and differences in rates of enrollment. Methods: Screening logs and study trackers were manually reviewed for 4 clinical trials at a single, academic institution and included RCTs and an observational study. Data extracted from these documents included the number of patients screened, number excluded and reasons for exclusion, number enrolled, number of withdrawn and reason. Results: Of the 4 trials reviewed, the POC diagnostic test had the highest number of patients excluded and the lowest patient refusal rate. Refusal rates were highest in the VTE prophylaxis study and enrollment rates were the lowest in the RCT of drug treatments and the highest rate in the observational study. Conclusion: The success of the trial relies on the ability to recruit patients and factors need to be considered when recruiting participants including sample size requirements and inclusion and exclusion criteria. These data provide some insights into the patient recruitment experience at our institution with different patient populations and study types, highlighting key points to be aware of when planning for an orthopaedic clinical trial. Keywords: enrollment, recruitment, RCT, outcomes, primary, revision

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Section: Discussionmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

Exploring Differences in Screening and Enrollment Metrics in Orthopaedic Clinical Trials

Stiegel,

Visperas,

Piuzzi

et al. 2023

J Knee Surg

View full text Add to dashboard Cite

show abstract

“…Clinical trials are of high importance for medical progress [ 1 ]. Well designed and well-executed clinical trial studies provide the foundational data for evidence-based medicine [ 2 ], which are the standard for evaluating the benefits and harms of medical interventions [ 3 ]. Numerous factors lead to the success of clinical trials, such as appropriate trial design(e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous factors lead to the success of clinical trials, such as appropriate trial design(e.g. randomization, blinding, and controls), thorough training of research staff, and recruitment of an adequate sample size by identifying and enrolling qualified participants in a timely manner [ 4 , 5 ] and maintaining good participation through study completion [ 2 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Utilization of EHRs for clinical trials: a systematic review

Kalankesh,

Monaghesh

2024

BMC Med Res Methodol

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Background and objective Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials. Materials and methods A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations. Results Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design. Conclusions According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.

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“…To make real hospital GECCO data available, university hospitals used Fast Healthcare Interoperability Resources (FHIR) repositories within their MII DIC. To support feasibility studies as part of the German Portal for Medical Research Data (Deutsches Forschungsdatenportal für Gesundheit [FDPG]) and to identify the size of decentral available data sets based on dedicated cohort characterizations (eg, described by Doods et al [ 10 ], Soto-Rey et al [ 11 ], and Laaksonen et al [ 12 ]), we developed a central feasibility portal, securely connected to all German university hospital GECCO FHIR data repositories. For timely design and development, owing to the pandemic, it was imperative to build on tools and experiences from previous projects and align the design for later strategic integration of this feasibility portal into FDPG of the MII [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

Gruendner¹,

Deppenwiese²,

Köhler³

et al. 2022

JMIR Med Inform

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Background An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing. Objective This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data. Methods We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service. Results We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative. Conclusions We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

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Assessing an Electronic Health Record research platform for identification of clinical trial participants

Cited by 6 publications

References 8 publications

Exploring Differences in Screening and Enrollment Metrics in Orthopaedic Clinical Trials

Exploring Differences in Screening and Enrollment Metrics in Orthopaedic Clinical Trials

Utilization of EHRs for clinical trials: a systematic review

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

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