Over the last decades, the number of clinical trials (CTs) conducted increased substantially [
1]. This development was paralleled by more demanding trial regulations including data monitoring, increasing complexity of study designs and lengthy data collection protocols [
2]. A similar trend towards larger amounts of more complex data to be handled in shorter time developed in clinical routine and led to the increasing use of electronic health records (EHR) [
3]. A large body of evidence suggests that EHR yield both process and structural benefits [
4]. The addition of mobile technology to EHR was shown to further improve data handling and increase time efficiency [
5]. Mobile technology is also well-accepted and preferred over classic data handling methods by users in clinical settings [
6]. Recent research suggests that improvements in EHR data handling technology can be key to meet current challenges in data handling efficiency in CTs and should thus be leveraged in electronic data capture (EDC) [
7,
8].
Various studies have addressed important concerns that might be associated with the replacement of paper case report forms and questionnaires (pCRF) by their electronic counterparts (eCRF). This led to high quality evidence that using EDC in CTs is a viable method in multiple aspects. It can be used in different settings including family practices, hospitals, research facilities, field studies and participants’ homes [
9‐
12]. pCRF and eCRF were also repeatedly shown to provide excellent internal consistency and construct equivalence, i.e. constructs can be measured equally across methods and entered values have equivalent meanings [
13‐
15]. eCRF are furthermore preferred over paper bound methods by participants and study personnel [
16,
17] and help improve data quality, particularly through a reduction of data omission errors [
15,
17,
18]. Multicenter studies also benefit from easy data sharing between study sites, which allows for syntactic and semantic interoperability [
19,
20]. Cost efficiency of eCRF use was evaluated in simulation studies and in the context of observational and interventional clinical trials and reported to be increased due to elimination of pCRF logistics (printing, delivery etc.), facilitation of data monitoring and time savings for study personnel [
18,
21‐
24]. None of the studies, however, took precise time records rendering causes of time savings unclear, importantly if and which part of the data collection procedure particularly benefitted from eCRF use. It is generally assumed that data transcription redundancy and patient self-report of study data play a major role, but a critical review of available literature reveals that this assumption lacks support by published data [
25]. Improvement in efficiency with eCRF may furthermore be affected by multiple factors that have not been studied so far such as length of CRFs, i.e. number of items in the CRF, complexity of items in CRFs and patients’ (e.g. age-related) ability to use eCRF. In summary, there are no studies that quantitatively assessed the time efficiency of pCRF and eCRF in a head-to-head comparison including the perspective of both involved parties, i.e. study personnel and patients, over multiple instruments in a CT [
26]. Availability of such quantitative evidence is particularly important to estimate costs in planning of clinical trials and to support the implementation of an EDC system, which is associated with substantial costs [
8,
27].
In this study, we evaluated how time efficiency and data quality are affected when source data is directly entered through eCRF into an EDC as compared to data capture with traditional paper pCRF including subsequent transcription to an eCRF. Secondary outcome parameter furthermore included predictors of efficient eCRF use.