eSource for Standardized Health Information Exchange in Clinical Research:  A Systematic Review of Progress in the Last Year

Garza, Maryam Y.; Myneni, Sahiti; Fenton, Susan H.; Zozus, Meredith N.

doi:10.47912/jscdm.66

Cited by 20 publications

(13 citation statements)

References 27 publications

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“…However, eSource can only be possible if the EHRs can support the collection of quality research data. There has been some eSource-related research progress in the field of clinical trials [ 2 – 4 ] and in relatively large projects, such as the OneSource project, EHR4CR project, European FP7 TRANSFoRm project, etc. [ 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the clinical application effect of eSource record tools for clinical research

Wang

Hao

Yan

et al. 2022

BMC Med Inform Decis Mak

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Background Electronic sources (eSources) can improve data quality and reduce clinical trial costs. Our team has developed an innovative eSource record (ESR) system in China. This study aims to evaluate the efficiency, quality, and system performance of the ESR system in data collection and data transcription. Methods The study used time efficiency and data transcription accuracy indicators to compare the eSource and non-eSource data collection workflows in a real-world study (RWS). The two processes are traditional data collection and manual transcription (the non-eSource method) and the ESR-based source data collection and electronic transmission (the eSource method). Through the system usability scale (SUS) and other characteristic evaluation scales (system security, system compatibility, record quality), the participants’ experience of using ESR was evaluated. Results In terms of the source data collection (the total time required for writing electronic medical records (EMRs)), the ESR system can reduce the time required by 39% on average compared to the EMR system. In terms of data transcription (electronic case report form (eCRF) filling and verification), the ESR can reduce the time required by 80% compared to the non-eSource method (difference: 223 ± 21 s). The ESR accuracy in filling the eCRF field is 96.92%. The SUS score of ESR is 66.9 ± 16.7, which is at the D level and thus very close to the acceptable margin, indicating that optimization work is needed. Conclusions This preliminary evaluation shows that in the clinical medical environment, the ESR-based eSource method can improve the efficiency of source data collection and reduce the workload required to complete data transcription.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the clinical application effect of eSource record tools for clinical research

Wang

Hao

Yan

et al. 2022

BMC Med Inform Decis Mak

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“…Some research progress has already been achieved in eSource technology [ 4 - 6 ] in relatively large projects [ 7 - 9 ]. The review by Garza et al [ 5 ] included 14 studies detailing recent advances in eSource technology in clinical research. In total, 57% (8/14) of studies described single-site, single-EHR system implementation; 67% (4/6) of multisite studies were part of the same pilot study (EHR4CR European Pilot), a collaborative initiative across multiple European countries.…”

Section: Introductionmentioning

confidence: 99%

Electronic Source Data Transcription for Electronic Case Report Forms in China: Validation of the Electronic Source Record Tool in a Real-world Ophthalmology Study

Wang¹,

Lai²,

Liu³

et al. 2022

JMIR Form Res

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Background As researchers are increasingly interested in real-world studies (RWSs), improving data collection efficiency and data quality has become an important challenge. An electronic source (eSource) generally includes direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. Although there are already large projects on eSource technology, there is a lack of experience in using eSource technology to implement RWSs. Our team designed and developed an eSource record (ESR) system in China. In a preliminary prospective study, we selected a cosmetic medical device project to evaluate ESR software’s effect on data collection and transcription. As the previous case verification was simple, we plan to choose more complicated ophthalmology projects to further evaluate the ESR. Objective We aimed to evaluate the data transcription efficiency and quality of ESR software in retrospective studies to verify the feasibility of using eSource as an alternative to traditional manual transcription of data in RWS projects. Methods The approved ophthalmic femtosecond laser project was used for ESR case validation. This study compared the efficiency and quality of data transcription between the eSource method using ESR software and the traditional clinical research model of manually transcribing the data. Usability refers to the quality of a user’s experience when interacting with products or systems including websites, software, devices, or applications. To evaluate the system availability of ESR, we used the System Usability Scale (SUS). The questionnaire consisted of the following 2 parts: participant information and SUS evaluation of the electronic medical record (EMR), electronic data capture (EDC), and ESR systems. By accessing log data from the EDC system previously used by the research project, all the time spent from the beginning to the end of the study could be counted. Results In terms of transcription time cost per field, the eSource method can reduce the time cost by 81.8% (11.2/13.7). Compared with traditional manual data transcription, the eSource method has higher data transcription quality (correct entry rate of 2356/2400, 98.17% vs 47,991/51,424, 93.32%). A total of 15 questionnaires were received with a response rate of 100%. In terms of usability, the average overall SUS scores of the EMR, EDC, and ESR systems were 50.3 (SD 21.9), 51.5 (SD 14.2), and 63.0 (SD 11.3; contract research organization experts: 69.5, SD 11.5; clinicians: 59.8, SD 10.2), respectively. The Cronbach α for the SUS items of the EMR, EDC, and ESR systems were 0.591 (95% CI −0.012 to 0.903), 0.588 (95% CI −0.288 to 0.951), and 0.785 (95% CI 0.576-0.916), respectively. Conclusions In real-world ophthalmology studies, the eSource approach based on the ESR system can replace the traditional clinical research model that relies on the manual transcription of data.

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“…As data (increasingly captured electronically) are used to support direct patient care, performance measurement, and research, the effects of data quality on decision-making need thorough exploration, as do the effects of system usability and data entry and cleaning methods on data quality and clinical workflow. Research opportunities exist (1) to understand the types of errors that perpetuate in MRA data collection, and (2) in the areas of data and process standardization [ 18 – 20 ] to aid in streamlining data collection for clinical research studies. Our ongoing work aims to address these issues as we develop solutions to streamline data collection processes and improve data quality in clinical research.…”

Section: Discussionmentioning

confidence: 99%

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

Garza

Williams

Myneni

et al. 2022

BMC Med Res Methodol

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Background Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time. Methods We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald’s method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields (“all-field” error rate) and populated fields (“populated-field” error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively. Results On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted. Conclusions Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study.

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eSource for Standardized Health Information Exchange in Clinical Research: A Systematic Review of Progress in the Last Year

Cited by 20 publications

References 27 publications

Evaluation of the clinical application effect of eSource record tools for clinical research

Evaluation of the clinical application effect of eSource record tools for clinical research

Electronic Source Data Transcription for Electronic Case Report Forms in China: Validation of the Electronic Source Record Tool in a Real-world Ophthalmology Study

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

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