eSources consist of data that were initially documented in an electronic structure. Typically, an eSource encompasses the direct acquisition, compilation, and retention of electronic information (such as electronic health records [EHRs] or wearable devices), which serves to streamline clinical research [1]. eSources have the potential to enhance the accuracy of data, promote patient safety, and minimize the expenses associated with clinical trials. In a 2020 publication by TransCelerate, a study conducted on opinions outlined a road map for the future use of eSource technology [2]. The study also highlighted 5 significant areas of challenges, which include clinical trial design, protocol and data collection, automated data exchange, safety and privacy, new roles, regulatory coordination, and cooperation [2]. The process of accessing and correcting the source data in real time during data collection may experience delays owing to various obstacles. These obstacles include the limited interoperability between EHRs and electronic data capture (EDC) systems, the presence of unstructured data such as researcher notes or comments, and the requirement to manually transcribe and handle certain data that are specific to the research and not included in the EHR [2]. Limited development, implementation, and evaluation of electronic resource solutions specific to EHR have been observed, despite the presence of multiple US Food and Drug Administration guidelines [1,3] and guidelines from the European Medicines Agency [4,5]. Over the past decade, numerous eSource solutions have been created, assessed, and enhanced [6-14], aiming to achieve an optimal technology that can bypass EDC data input altogether. This advanced technology is designed to directly extract source data from the EHR and seamlessly transmit them to an electronic case report form. Significant advancements in eSource research have been observed in various substantial initiatives, including the OneSource project [15], Electronic Health Records for Clinical Research [16], European FP7 Translational Research and Patient Safety in Europe (TRANSFoRm) [17,18], Electronic Health Records to Electronic Data Capture [19-21], and the Vulcan Health Level Seven International Fast Healthcare Interoperability Resources Accelerator [22-24].

Real-world data (RWD) refer to information regarding the health status of patients and the provision of health care, which is routinely gathered from diverse sources [25]. A real-world study (RWS) entails the collection of RWD within an actual environment, analyzing them to obtain real-world evidence regarding the practical value and potential advantages or risks associated with medical products [26].

In 2019, the China National Medical Products Administration designated the Hainan Boao Lecheng Medical Tourism Pilot Zone as a testing ground for the RWS [27]. Subsequently, in December 2020, the Hainan Real World Research Institute initiated an eSource record (ESR) project [28] aimed at creating a comprehensive solution and tool for the collection, governance, and management of RWD within hospitals. In October 2021, the ESR project was officially released in China, and it was trialed in medical institutions in Boao Lecheng to support the implementation of the RWS [29]. For the design of the ESR system, refer to their recent proposal article [30]. In a real-world ophthalmology study, the researchers described the scenarios of ESR in hospitals, data standard transformation, and application effect assessment [28]. Retrospective and prospective studies can be supported by eSource or observational research data collection. The ESR has completed software deployment in nearly 10 medical institutions in Boao Lecheng. Researchers are working to pilot and promote the software in several large tertiary first-class hospitals in Beijing, China.

The background of this study concerns the use of eSource technology in clinical research. The purpose of this study was to examine the challenges and possible solutions regarding the implementation of eSource technology in RWSs by summarizing teams’ experiences and lessons learned from the ESR project in China. Readers interested in this study will include stakeholders who want to realize electronic transmissions from EHR to EDC systems, such as big data companies, medical system providers, and researchers in the field of clinical research. Similar to TransCelerate’s eSource initiative in the world, the researchers want to share the experience of ESR in China to promote global digital reform in the field of clinical research.

Qualitative research allows us to understand the participants’ experiences. After initially developing a simple prototype of the ESR software that could be demonstrated systematically, the researchers conducted in-depth interviews and interacted with different stakeholders to obtain relevant guidance and experience suggestions. The researchers selected 5 different roles for interview subjects: regulatory authorities, pharmaceutical company representatives, hospital information department employees, medical system providers, and clinicians. The sample size estimation was not applicable; the participants were stakeholders that the researchers could invite.

This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethics approval was obtained from the Peking University Institutional Review Board (number IRB00001052–21081).

Verbal informed consent was obtained from all the participants.

In-depth interviews with the interviewees were conducted using a flexible topic guide to ensure that the primary issues were covered across all interviews but enabled participants to introduce unanticipated issues. The interview topics were divided into 4 aspects: the view of eSource technology, the functional requirements of eSource technology in relevant fields, and the challenges and possible suggestions when applying ESR software to implement eSource technology.

Interview questions were designed according to the different roles of the interviewees (Textbox 1).

The researchers convened many web-based and offline meetings to summarize and sort out the challenges in and suggestions for advancing ESR software development by demonstrating ESR software prototypes. Many small expert interviews were conducted at academic conferences during the stage of designing and refining the theoretical framework of ESR software. During the formal development phase of the ESR software, there were discussions among various research teams, including big data companies, EDC system manufacturers, contract research organization teams, and medical system experts. These discussions took place in weekly small group sessions as well as in larger meetings. The interviews were recorded using a digital voice recorder. Audio recordings were transcribed and anonymized to protect confidentiality.

Interview transcripts were imported into NVivo (version 12; QSR International) qualitative data analysis software. The iterative process continued with data collection, coding, and analysis, followed by further data collection and analysis until saturation was reached, which occurred when the last few interviews fit the existing patterns and did not generate new ideas. Thematic analysis [31], using a data-driven inductive approach, was used to identify and analyze patterns and themes of particular salience for participants and across the data set using constant comparison techniques [32]. Two researchers (BW and FJ) designed a structured coding tree that began with inductive open coding. Once the core categories emerged, deductive selective coding was performed. Open coding was performed independently by the 2 researchers, and the derived core categories were compared in multiple rounds of discussions until all members agreed.

After screening all consultants, the researchers identified 25 representative main consultants. These experts provided important advice. For specific personnel information, refer to Table 1. All participants, except for the clinicians, responded to the topic of using eSource technology in clinical research. Clinicians only interact with the hospital information system for patients’ daily diagnosis and treatment during their work; thus, they lack knowledge about eSource technology.

Different stakeholders have described the important functional requirements for implementing eSource technology (Figure 1). For each requirement, the proposer and the recipient of the requirement are shown in the direction of the arrow. For example, the regulatory authorities require the hospital information department to “keep the source data well maintained and leave notes about any changes,” and they require pharmaceutical companies to “comply with data submission standards” when submitting data. The hospital information department requires pharmaceutical companies to “clearly list the source data used for research”; that is, when hospital companies conduct clinical research, they must notify the hospital information department of the data they need for research rather than provide the full amount of medical data to pharmaceutical companies. The purpose of this is to reduce the workload of the hospital information department when extracting data, while ensuring the security of medical data and reducing the risk of data leakage. The clinician is the recorder of the research source data. The clinician’s focus is on the usability of the system. Therefore, it is assumed that even if the software meets the needs of other stakeholders, it will be rejected if it does not conform to the habits of clinicians. When clinicians are faced with a heavy demand burden without the right software for them, it is impossible for clinicians to record high-quality source data for research.

We compiled challenges and possible solutions for designing ESR software from various stakeholders. These details can be found in Tables 2 to 5 and Multimedia Appendix 1. Importantly, among all stakeholder-proposed solutions, those proposed by clinicians and regulatory authorities were primarily determined by the technical staff involved in the discussions. On the other hand, the solutions proposed by hospital information departments, medical system providers, and pharmaceutical companies are primarily focused on their own interests. To increase the readability of the results, the researchers classified the key results in accordance with the paragraph titles in “Use of Electronic Health Record Data in Clinical Investigations,” a guide released by the United States Food and Drug Administration [3].

The challenges and recommendations provided by various stakeholders with regard to data standards, structured and unstructured data, and validation are presented in Textbox 2.

The eSource principles for EHRs consist of 2 aspects: data originator and data modifications. These principles of EHR are presented in Textbox 3.

This paper introduces the demand dependencies, existing challenges, and possible suggestions of different stakeholders when implementing eSource technology in China. Figure 1 shows that the number of requirements that the hospital information department needs is large, which makes it impossible to realize all the requirements in the existing EHR system. This explains why the researchers developed standalone ESR software that acts as a bridge between the EHR and EDC systems. The emergence of ESR is intended to divert the burden of the hospital information department from the enormous functional requirements of the outdated EHR system.

The Vulcan Real World Data project [33] is developing methods to use the Vulcan Health Level Seven International Fast Health Care Interoperability Resources standard to retrieve relevant data from RWD sources, for now primarily EHR systems, and ultimately will transform that data into a format suitable for submission to pharmaceutical regulatory agencies. The solution of this project is currently limited to the use of EHR for retrospective data analysis. The challenge faced by the project is the semantic and syntactic differences in the data transmission process from the field of health care data to the field of research and regulation. A qualitative study [34] described the challenges of implementing electronic trial data collection in primary health care using TRANSFoRm. In this qualitative study, different stakeholders were interviewed, including general practitioners, practice managers, information technology leaders, and research staff. It was found that the installation process of TRANSFoRm software was time-consuming and difficult because of the complexity of the primary health care information system [34]. These complications are primarily because of the differences between the permissions and information system skills of different stakeholders [34]. Researchers have found difficulties in the application value of quantified eSource technology (Table 5). Nordo et al [35] proposed a set of standardized measures to evaluate human security, data quality, operational efficiency, and the cost of eSource solutions. The researchers can see that many of the core evaluation indicators proposed in this study are consistent with the content of the interviews.

The preservation of patient anonymity and the impact on the design of eSource technology systems is a critical aspect that warrants careful consideration. Maintaining the confidentiality of patient’s medical information is of utmost importance in the implementation of eSource technology. Anonymity plays a crucial role in establishing trust between patients and health care providers, ensuring that their privacy is respected and protected [36,37]. The preservation of patient anonymity has significant implications for the design of eSource systems. It necessitates the development of robust data security measures to safeguard patient information from unauthorized access and breaches. The design should include encryption protocols, user authentication mechanisms, and access controls to ensure that only authorized individuals have appropriate access to patient data. These measures are essential for maintaining patient confidentiality and instilling confidence in the use of eSource technology for RWS.

In addition, it is essential to acknowledge the potential involvement of other stakeholders in data collection beyond the 5 groups studied in this research. The process of treating patients extends beyond their time in the hospital, and eSource technology may need to capture data from their experiences in community health care and social care settings. Stakeholders, such as community health care providers, social workers, and caregivers, play important roles in these contexts [36,38]. Recognizing their involvement and understanding their perspectives is crucial for comprehensively addressing the challenges and potential solutions associated with eSource technology implementation in RWSs.

There are certain limitations in this study. First, this study focuses on summarizing important challenges and suggestions. Second, there are still differences between the content and organization of the study results and regulatory guidelines. Finally, the stakeholders the researchers have included are still limited, and future studies need to focus on more significant stakeholders, such as patients, community health care providers, and social workers, among others.

This study introduces the requirement dependencies of different stakeholders and the challenges and recommendations for designing ESR software when implementing eSource technology in China. Experiences based on ESR projects will provide new insights into the disciplines that advance the eSource research field. Moving forward, the researchers acknowledge the need to expand the scope of this research to incorporate the patient perspective and involve a broader range of stakeholders. Future studies should engage patients directly to understand their experiences, concerns, and preferences regarding the implementation of eSource technology. By doing so, researchers can develop systems that prioritize patient privacy, uphold data security, and align with their needs and expectations. Moreover, involving additional stakeholders, including community health care providers and social workers, will provide valuable insights into the challenges and potential solutions across various health care settings.

It has been nearly 1 year since we conducted this qualitative study. As we continue to promote the trial use of ESR software in new research projects, the views delineated in this study may lag behind practical experience. Therefore, we have added recent progress results. We summarized the management process of applying ESR tools in clinical research projects (Figure 2). In this process, we differentiated the process based on the different stakeholders. The role of service providers corresponds to that of big data companies or medical system providers. At the same time, we summarized the standard operating procedures for the management responsibilities of different stakeholders (Multimedia Appendix 2). These new achievements will provide additional possibilities and implications for readers to understand the main content of the paper.