Data Sources and Data Acquisition Methods

Operational data associated with a single, Phase II, multicenter, investigator-initiated clinical trial conducted in Japan was used for this analysis. Table 1 shows the source clinical trial of our eSource DDC research. The target therapeutic area was cancer, and the participating sites were either university hospitals or cancer-specialized hospitals, which had more than 100 beds and multiple departments. In general, clinical trials in which eSource DDC can be more easily implemented are characterized as Phase I trials in healthy adults, where there is less need to input information into medical records or share information with other departments. Additionally, Phase II or later trials are considered suitable when the participating sites are small clinics or hospitals with a single department, making it easier to share information within the hospital. Current ICH-E6 states that the protocol and other referenced documents should include “The identification of any data to be recorded directly on the CRFs (i.e., no prior written or electronic record of data), and to be considered to be source data” [1]. In the investigator-initiated clinical trials covered herein, each site created a source data identification list (SDIL) to identify the source data to be entered into the EDC for each eCRF item. The data used included raw data output from EDC, database structure specifications (DSS), SDIL, audit trails output from EDC, and monitoring reports. The information necessary to perform the analysis was extracted from these data. As described above, because this trial was conducted by entering source data directly into the EDC, the audit trail of the EDC includes the audit trail of the source data. This study was reviewed and approved by the Ethics Committee of Tohoku University Graduate School of Medicine.

Table 1 Overview of the clinical trial that implemented eSource DDCVarying Collection Items to be Source Data at Each Site

The SDIL defines the fields where the eCRF is the source data (DDC) and those with other source data (non-DDC). In non-DDC, work hours are required to create the source documents and transcribe them to the EDC. Additionally, SDV is important for checking the consistency of these data. Therefore, all fields defined in the DSS were classified as DDC, Non-DDC and DDC/Non-DDC based on the SIDL definition by site. Audit trails were used to identify the distribution of the person who had initially entered each eCRF item into EDC.

Time from Data Occurrence to Data Entry and Finalization

The date of data occurrence was identified for each field. For example, for the field related to weight measurement, the date of data occurrence was set to the date of weight measurement. The number of days from initial data entry to data finalization was calculated from the audit trails. Data finalization was defined as the date on which the freeze flag was applied to the final data. If the data had been modified after the freeze flag was applied, it was defined as the date on which the last freeze flag was applied The use of the freeze flag varies according to the operational policy of each clinical trial; however, in this trial, the freeze flag was applied by clinical research associates (CRAs) after the completion of data review by both the CRAs and data managers (DMs).

Number of Visits to the Site and Time Spent at the Site by CRAs

The number of times the CRAs visited each site and the time at which the work began and ended were extracted from the monitoring reports. Visits were counted if the visit purpose included the SDV of subject data. Because the EDC data included subjects with screening failures, the number of site visits and the work time at sites by CRAs were calculated per site per subject’s visit.

Simulation on the Impact of Change in the Clinical Trial Scale and the Percentages of DDC Fields on Site Work Hours

Kellar et al. demonstrated that challenges in implementing eSource DDC include the time required to initiate the trial and the associated costs. Additionally, site training and site resistance are also recognized as potential obstacles [4]. It is crucial to anticipate changes in site effort in advance. However, the data collected in this trial did not allow us to examine the hours required at clinical sites. Therefore, we conducted a simulation to assess the impact of changes in the clinical trial scale and the proportion of eSource DDC fields on site work hours, based on the study by Eisenstein et al. [7].

In the simulation, we assumed various combinations for the total number of data fields, the percentage of those that can be DDC, and the number of subjects. In addition to these conditions, we set the data entry speed and the additional time needed to set up the eSource DDC, compared with the time necessary for traditional study (i.e., entering source data into the medical record or paper worksheet and transcribing the data to the EDC), as fixed values under hypothetical conditions. Regarding the data entry speed, we assumed that 134 fields could be entered per hour, using the same values as in the study by Eisenstein et al. For a simplified simulation, the time required for data correction by issuing a query is not considered in this simulation. When considering the additional time required for implementing eSource DDC, it was assumed that two physicians and two CRCs would be assigned to the project. A total of 44 h was allocated for training, creating source data identification lists, conducting site user acceptance testing, and confirming the worksheet items to be implemented in the EDC (Table 2). These are hypothetical figures and may vary based on factors like the number of personnel assigned and the site’s prior experience with eSource DDC. Under these assumptions, X is the number of subjects, whereas Y is the number of hours (h) at the site. The hours Y for eSource DDC was calculated as follows: \(\ \ \right)}}} + \frac}}) \times number\;of\;subjects\} + 44\), while that for the traditional method was calculated as \((\frac}} + \frac}})*number\;of\;subjects\). The first half of the brackets represent the time required to enter the source data outside the EDC and assume that 134 fields can be entered per hour. The second half of the brackets represents the time to enter data into the EDC.

Table 2 Additional resources associated with eSource DDC implementation compared with the traditional method

The simulation was conducted in two steps. The first step involved fitting the number of data fields and the percentage of DDC-capable fields to match those in the actual clinical trial. These values were determined based on the results of “valuing collection items to be source data at each site” as outlined in this study. In the second step, we established eight different patterns for Simulation Sites 1–9 by varying the combinations of the number of data fields and the percentages of DDC fields. Cutoff values were determined for the number of subjects. That is, the threshold at which the time spent on the trial with eSource DDC was less than that with the traditional method was examined.