n the last 10 years, Clinical Data Management (CDM) has grown exponentially in Japan and throughout the world, in both scope and activities. Although we use the term CDM in job and project titles, today’s work is essentially the expansion of CDM: from recording, collecting, reporting, and analyzing clinical research data to broader Clinical Data Science (CDS).

DCTs require a unique approach to clinical trials, where the personnel and participants are not at a clinical trial site; rather they are part of the community. We looked at how a DCT can meet all regulatory requirements and monitor the trial appropriately. Just as in the United States, DCTs in Japan are part of academic/nonindustry collaborations. Thus, we provide lessons learned from a DCT collaboration example (international agreement) involving National Cancer Center Japan (NCC) partner sites in Japan and Thailand. This example illustrates the increasing complexity of data flow, oversight, and source diversity in modern clinical trials.

Related information about this example was shared in a session at the DIA Japan Annual Meeting 2025. This session was organized as a collaborative effort between the DIA Japan and Global DIA Clinical Data Management (CDM) communities, with members participating from both.

DCTs move away from the traditional study model, where a patient comes into a clinic and is followed in a prescribed manner with the type of data to be collected spelled out beforehand, to a model that focuses on the logistics of data collection and the integrity of the data flow.

The roles of data managers and data management have expanded substantially. While traditional CDM focused on the logistics of data collection and the integrity of the data flow, CDS expands this focus to include the meaning and value of the data, and the associated risks. The data manager is not only in charge of the data management function but is integral in making sense of the whole clinical data universe, which includes a growing variety of eSource types of data; e.g., electronic health records (EHRs), electronic medical devices, e-diaries, e-PROs, eCOAs, and Digital Health Technologies (DHTs).

DCT Benefits and Challenges for CDM/CDS

Decentralized clinical trial study elements such as telemedicine, wearables, and other remote data capture methods offer many benefits. These include the ability to query the data in real time, leading to less missing data and faster access to the trial data compared with traditional data capture. Through the inclusion of well-planned DCT elements, trials can now answer important scientific questions with high-quality data not possible even a few years ago, while simultaneously reducing trial burden for study participants and trialists.

Progress in designing and conducting DCTs can be attributed to a complex combination of factors, including the adoption of new technologies and progress in drug development science.

However, these changes pose challenges for clinical data scientists, whose primary mission is to ensure the quality and integrity of data. Clinical trial data goes through a complex process, including the collection, management, analysis, and reporting of findings. As a result, ensuring data quality and integrity cannot be the sole responsibility of a single group; rather, consideration of the overall data flow and the objectives of the trial, and cross-role collaborations, is necessary and extremely important.

New Landscape Needs New Knowledge and Capabilities

The complexities inherent in the adoption of DCTs have fundamentally transformed the landscape for CDM/CDS professionals, demanding a re-evaluation of established practices and the cultivation of new knowledge and capabilities.

Collective insight focuses on several critical areas that necessitate immediate attention and strategic thinking:

Diversity of Data Sources and the Assurance of Data Quality

The expansion of DCT tools has led directly to a greater diversity of data sources. It has become necessary to develop and establish robust methods for ensuring data quality and integrity that are specifically tailored to each scientific question and its associated data source and adhere to applicable regulatory requirements and scientific standards. Increasingly, this involves assuring reliability, traceability, completeness, and timeliness of data from nontraditional sources such as home visits, telemedicine applications, wearables, and wearable devices, and communicating the best way to collect this information.

Such complexity may become even more visible in DCT models involving multiple partner sites or international collaboration, but the underlying CDM/CDS challenges are broadly relevant to DCTs in general.

Regulatory Modernization: The Impact of ICH E6(R3) and ICH E8(R1)

The most significant recent regulatory change that affected the CDM/CDS role is the official adoption of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E6(R3) Good Clinical Practices and ICH E8(R1) General Considerations for Clinical Studies guidelines. ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1) represents modernization of GCP to address innovations such as DCTs, Digital Health Technologies (DHTs), and data-driven oversight models. E6(R3) emphasizes trial quality, participant protection, and data integrity within a risk-based framework.

Quality by Design (QbD) and Risk-Based Quality Management (RBQM)

QbD and RBQM illustrate long-term industry/regulatory initiatives to modernize the clinical trial ecosystem and go hand-in-hand with ICH E6 and E8. These modifications are important to CDM/CDS professionals across both pharmaceutical industry and academic research settings. Because QbD offers a systematic approach to development that begins with predefined objectives and emphasizes process understanding and control, its benefit is not limited to a single setting or role. For clinical data scientists specifically, QbD means extracting and using knowledge from diverse sources to assess quality attributes and improve data strategies from the initial discussions of the design.

Understanding Operational Challenges in Assessing Data Risks

To truly safeguard data integrity, it is essential to understand the operational challenges of DCT trials. This comprehensive understanding of challenges is fundamental to appreciating the inherent risks associated with the data generated in these novel, nontraditional trial settings. It may be expected, for example, that partner medical institutions will have a lower level of expertise regarding the collection of data for the trial in question. This may require a comprehensive medical/educational support process together with monitoring and data checks.

Learning from Practical Clinical Research Examples

Finally, we wished to examine how a complex DCT model involving partner-site collaboration was planned. We felt that there was significant value in sharing practical insights from DCT planning and preparation that could pose quality and data issues, and how such difficulties can be addressed. Specifically, we looked at a study agreement between sites in Japan and Thailand’s Department of Medical Services that highlighted invaluable lessons detailed below.

Through this planned partnership, we identified study-related tasks that can appropriately be delegated to a partner site, including examinations, drug dispensing, and electronic data capture, while the investigator responsible for the study retains oversight, including remote participant interviews, through DCT tools. Unlike a conventional international study, this model allows the investigator at NCC to remotely provide study-related medical care and oversight to participants located in another country through DCT tools, while collaborating with and being supervised by local physicians and partner institutions. This approach is expected to help address barriers such as limited access to investigators with specialized expertise, funding constraints, and restricted opportunities for eligible patients to participate in academic trials.

This framework illustrates how DCTs can create greater diversity in data sources and operational responsibilities, requiring careful coordination of IRB review, data sharing, and local medical licensure. It increases access to study participation for potential participants in countries where availability of qualified investigators in the relevant therapeutic area is limited.

Now What? Strategic Shift from CDM to CDS

The transformation from CDM to CDS is a foundational change underlying modern clinical data development activity. This transition requires that clinical data professionals move beyond thinking about and following checklist-driven processes to responsibilities and activities based upon more critical thinking and proactive risk management.

The core responsibility of CDM/CDS in ensuring data quality and integrity demands extending traditional data management/system knowledge and capabilities into greater proficiency in digital health technologies, including knowledge of inherent biases in each system, such as incomplete audit trails, pre-sorted adverse event terms based on a MedDRA pick list in an EDC system, incorrect summation of too many detailed data generated by wearables, and a keen awareness of evolving regulatory expectations.

Consequently, CDM/CDS, which has traditionally focused on streamlining and standardizing data management operations, must face even greater challenges in robust cross-functional collaboration with key stakeholders, such as experts in RBQM, Clinical Operations, IT, Biostatistics, Project Management, and the clinical sites themselves while moving towards the strategic oversight of complex data ecosystems that require new communication and education.

This expanded oversight includes the inclusion and description of CDM/CDS leadership roles on drug study teams. For example, sponsors must specify each activity that is permitted remotely in trial protocols and ensure that all personnel involved, including local healthcare providers, are qualified for and understand their contributions to the collection of quality clinical trial data. CDM/CDS professionals who have the primary responsibility for data integrity must contribute to protocol development from the start and remain engaged through all activities associated with trial conduct, analysis/reporting, and regulatory communication. The CDS, like every other key role in clinical trials, must have the education and training even about adjacent areas of the role, to ensure efficient operations and communication along the entire project.