#FutureReadyHealthcare
Executive Summary
For life sciences companies, Clinical Data Management (CDM) plays a pivotal role in evaluating the safety and efficacy of new treatments. High-quality data management ensures that clinical trial data is properly prepared for analysis, leading to valid and reliable results. Efficient CDM is essential for managing the large volume of data generated from trials conducted across numerous sites globally and from multiple sources, especially in the wake of the rise in decentralized clinical trials.
To gain deeper insights into this evolving landscape and offer actionable recommendations, we surveyed 100 CDM leaders to understand their organizations' priorities, the current state of technology adoption, the role of outsourcing in CDM, and the roadmap for adopting Generative AI (GenAI) and machine learning (ML). The survey also highlights the leaders' commitment to investing time and resources in upskilling their CDM workforce and implementing best practices to significantly enhance the CDM function.
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Redefining Clinical Data Management: Synergy of Technology, Talent, and Outsourcing
Key findings from the study are presented below:
Priority Areas for CDM
53% of respondents identified accelerating SDTM mapping and domain creation as their top priority, while 48% emphasized the importance of reducing database go-live time. Additionally, 42% highlighted the need to expedite and improve eCRF creation. These findings reflect the strong focus of industry leaders on streamlining data collection and management processes, ensuring data quality and integrity, and leveraging technology to accelerate clinical trial timelines.
Industry Initiatives
Over 50% of respondents reported they have fully implemented the ICH M11 initiative for Structured Protocol Authoring, underscoring the importance of a standardized approach to writing clinical trial protocols. This standardization is critical for ensuring consistency, efficiency, and data integrity across trials. In addition to maintaining high data quality, data management leaders are also focused on advancing patient-centric clinical trials. Notably, 60% of respondents indicated they have launched initiatives and are exploring proof-of-concept projects aimed at reducing the burden on research sites and trial participants, reflecting the growing emphasis on enhancing trial experiences.
Adoption of GenAI and ML
An impressive 75% of respondents identified achieving greater efficiency as the primary driver for integrating Generative AI (GenAI) and Machine Learning (ML) into Clinical Data Management (CDM) operations. Additionally, over 50% are already using these technologies to enhance data quality and improve clinical study outcomes. To further accelerate GenAI adoption within CDM, respondents have pinpointed key use cases that are currently in the planning or execution phases. More than 75% plan to leverage GenAI and ML to quickly generate accurate Tables, Listings, and Figures (TLFs) and reduce database go-live time. Furthermore, 60% aim to deploy GenAI to expedite data standardization, while 46% view faster protocol generation as a critical use case. For these initiatives, over 30% of respondents are targeting an implementation timeline of 6 to 12 months.
Investment in Technology Initiatives
56% of overall spending was allocated to technology, but adoption levels vary significantly across functions. Over 35% of respondents indicated that in areas like Protocol Development and Clinical Data Capture, technology use is mostly limited to workflow management activities. Additionally, 54% reported automation in Tables, Listings, and Figures (TLF) generation, but without the integration of AI/ML technologies. More than 50% of respondents stated that 50-75% of SDTM domain creation activities are already automated, highlighting it as a prime candidate for further technological innovation. Similarly, 41% noted that ADaM generation and TLF output activities are also automated to the same extent, making them key areas for potential advancements in automation and AI/ML integration.
Outsourcing Trends
Apart from protocol development, over 25% of respondents reported that 50-75% of all other clinical functions are outsourced. The primary driver for outsourcing is better management of labor costs, followed closely by the adoption of AI/ ML models, particularly for Study Data Tabulation Model (SDTM) and TLF generation activities— identified as key factors by more than 60% of respondents. CDM leaders expect outsourcing vendors to deliver not only agility, innovation, flexibility, and alignment with their long-term vision but also proactive risk management. In addition to these, vendors are expected to ensure data accuracy, maintain integrity, and guarantee compliance with regulatory standards, particularly when integrating advanced technologies.
Workforce development
51% of the upskilling budget is dedicated to technology, encompassing training on existing systems, data sources, and AI/ML integration to manage the growing volume of clinical trial data and produce high-quality study reports. These investments align with the organization’s long-term vision of accelerating digital transformation in clinical trials, with a strong focus on automation and AI. The remaining budget is allocated to process improvements and industry initiatives, reflecting the need to equip R&D teams to effectively implement technology-driven solutions and enhance the overall efficiency of data management in clinical trials. This balanced approach ensures both technological advancements and operational readiness, driving the future of clinical trials.
Methodology
The study gathers insights based on a survey conducted with 100 senior executives who are responsible for a range of activities within the Clinical Data Management function in a life sciences and medical devices organization. Over 50% of the respondents oversaw operations across the following areas; Clinical Data Capture and Management Systems, Clinical data standards and submission, Safety Data Management, Reporting and analytics, Clinical Protocol Development, and Clinical Research applications. By region, 60% of the respondents focused solely on the US, 25% on EU 5, and 15% on the rest of the EU. To ensure we gather perspectives of delegates across seniority levels, the respondents were balanced across the hierarchy with CXO/EVP/President, SVP, and VP, each representing 20%, followed by Managing Director, Executive Director, Senior Director, Director delegates who formed the remaining 40% of the respondents’ pool. To enhance the comprehensiveness of the findings, we included the life sciences (75%) and medical devices industries (25%) respectively.
Key Priorities for Maximizing Efficiency and Ensuring Data Quality
Clinical trial data management is evolving rapidly. Key trends include comprehensive data collection, the adoption of AI/ML for data integrity, and streamlined reporting. With all these advancements, top priorities for Clinical Data Management (CDM) leaders are data quality, technology adoption, global trial management, regulatory compliance, operational efficiency, and patient engagement. Collaboration with research sites and patients and the adoption of industry-wide data standards are also becoming more important.
As shown in Figure 1, the industry continues to focus on accelerating SDTM mapping, domain creation, and reducing database go-live times to speed up the process of bringing innovative therapies to patients faster. The survey delves into aspects of CDM function that, according to the respondents, has the potential to improve and streamline data management and accelerate the trial process. To learn more about this, we asked respondents to highlight their most important CDM priorities for the next 1 to 3 years.
The most critical priority for clinical trial data management is SDTM mapping, especially among operational leaders. This reflects a growing emphasis on streamlining data standardization. Reducing the time it takes to go from protocol to database is crucial for all companies, especially for the US and mid-sized firms. Accelerating eCRF processes is a significant operational focus, particularly in the US, indicating a push to optimize core data management processes. ADaM creation and TLF generation are more important for larger firms, aligning with the need for detailed data analysis in large-scale trials. While CSR creation is a lower priority overall, it gains prominence in larger companies, reflecting a focus on efficiently closing out trials at scale.
To meet the above set of priorities, most organizations are using several solutions to manage clinical data requirements. We asked the senior leaders what technology-related features and functionalities would help them streamline clinical trial data collection and tackle the clinical data management demands and what industry standards and industry-approved best practices are they implementing to ensure they are effectively leveraging technology and resources to prepare themselves as the clinical trials landscape continues to evolve at a rapid pace.
Predictably, organizations are prioritizing the integration of data from new sources like wearables, reflecting a shift towards more comprehensive and real-time data collection. There is a significant focus on utilizing AI and ML for data integrity analysis, especially among senior leaders, indicating a trend toward advanced technologies for better data management in clinical trials. Accelerating TLF generation is crucial for streamlining reporting processes and reducing validation overheads. While protocol authoring through AI/NLP shows promise, it remains a lower priority, suggesting early stages of adoption. Additionally, there is a lower emphasis on decentralized trial tools, indicating that while important, they may not be the immediate focus within the next 12 months.
90% of CXO level executives and 73% of respondents in the $1bn-5bn revenue categories respectively said integrating data from new data capture sources is the most important feature. Interestingly, 70% of respondents from companies with revenue of over $20bn mentioned Protocol Authoring/ Digitization using AI/ML or NLP models is the most important feature.
Real-Time Data, AI, and Decentralized Technologies continue to play a critical role in improving the efficiency of the CDM function
Driving Patient-Centricity through Industry Initiatives
The clinical R&D functions adhere to several key industry standards to ensure data quality, integrity, and compliance as they play an important role in helping these organizations adopt advanced technologies, enable collaborative data sharing, and implement the required data security measures. However, the state of adoption of these standards and the best practices organizations are taking to implement them vary significantly based on their current state of operations and the extent of decentralized clinical trials being implemented.
ICH M11 for Structured Protocol Authoring is the most mature initiative, reflecting its established importance in the industry. Most other initiatives are still in the early stages, with a high percentage of respondents stating organizations are either exploring or engaged in POCs. The initiatives focused on EMR data capture, GenAI/ML and Automation, and reducing burdens on clinical sites/patients are particularly noteworthy for their strong levels of exploration and POC engagement, indicating these are emerging areas of focus that could see more widespread adoption in the future.
Initiatives like HL7 Vulcan for EMR Data Capture and DDF are primarily in the Limited Implementation/POC stage, which reflects ongoing efforts to achieve full-scale adoption. The use of GenAI and ML, is in the Limited Implementation/POC stage, with no organizations fully adopting these technologies, owing to the potential challenges they would face in scaling these initiatives in a sustainable and regulatory-compliant manner at a global scale without investing heavily in terms of funds and resources.
59%
are exploring initiatives to reduce burdens on clinical sites/patients
This data showcases the industry’s drive toward digital transformation and process optimization, with a strong focus on data quality, security, and leveraging advanced technologies for better clinical trial data management outcomes. As seen in Figure 4, ensuring data quality is the highest priority across the board, reflecting the measures companies take to create structured processes to maintain the integrity and accuracy of clinical trial data, which is critical for successful trial outcomes.
Meanwhile, the widespread implementation of advanced technologies (such as AI, ML, and automation) demonstrates the long-term vision life sciences and medical devices organizations have, to transform the CDM function, especially to optimize data management, speed up workflows, and reduce the error rate in study reports. With the increasing volume and sensitivity of clinical data, data security is understandably a significant focus. Ensuring the protection of patient data and maintaining compliance with global privacy regulations (e.g., GDPR, HIPAA) is a fundamental requirement. The lower emphasis on the adoption of EDC systems and collaborative platforms, which are fundamental components of modern CDM practices, could indicate that most organizations have already deployed these solutions to facilitate standardized and efficient clinical data management processes.
Optimizing the Synergy Between Technology and Workforce
Investing in both technology and the workforce is crucial, as they are interdependent and equally important to harness the full potential and ensure the successful implementation of digital transformation strategies. Technology investments mainly propel innovation, operational efficiency, and data management, whereas workforce investments ensure the organization is well-equipped with the skillset and culture to drive sustainable change.
56% of investment is directed toward technology, indicating a strong focus on adopting digital tools, automation, AI, and Machine Learning to achieve CDM priorities. The acute focus CXO and SVP level respondents have, to accelerate SDTM mapping and domain creation followed closely by eCRF creation and ADaM creation, is a clear reflection of the need for cutting-edge technologies to achieve efficiency, accuracy, and scalability in CDM processes. While technology takes the larger share, the considerable investment (44%) in workforce capabilities reflects the need for specialized training in AI for clinical data management, and regulatory knowledge. Human oversight is essential for tasks like validation, ensuring compliance, and making strategic decisions, particularly in complex scenarios where AI may fall short.
Companies with $20 billion+ revenue allocate 55% to building skilled teams for complex trials and advanced tech.
More than half of the upskilling budget is being allocated to technology, highlighting the importance of technological advancements in CDM. This could include training on Electronic Data Capture (EDC) systems, AI/ML integration, database management tools, and emerging software to manage large-scale clinical trial data. Excluding large pharma companies who have a higher focus on processes (42%), this trend is the same across all other categories. This focus is likely driven by the ongoing digital transformation in clinical trials, where automation and AI play significant roles in improving efficiency and accuracy. As the integration of diverse data sources increases and adoption of emerging technologies picks up pace, there will be notable investments in processes as well to streamline workflows, ensure data quality, and enhance cross-functional collaboration that is essential to improve the overall efficiency required to manage clinical data. While technology and processes take precedence, organizations still recognize the need to align with industry initiatives. This could involve training on regulatory standards, best practices, and participation in collaborative industry efforts to harmonize and standardize clinical data practices globally. Although the focus on technology is paramount, nearly half of the investment is still channeled toward improving processes (28%) and adhering to industry standards (21%). This balanced approach reflects the need to ensure both technical and operational excellence. The data indicates that organizations are likely investing in areas such as data security, regulatory compliance, and AI/ML integration, which are becoming critical as the industry continues to evolve and face new challenges in clinical trials.
According to the respondents, familiarity with CDISC standards like SDTM and ADaM is one of the most important skills employees in the CDM function should have to ensure they are equipped to conduct operations while ensuring regulatory compliance and interoperability in global trials. This is closely followed by knowledge of statistical methods and proficiency in programming languages, as technical knowledge and strong analytical skills are the cornerstone for any sustainable innovation to improve the efficiency of the CDM function.
Respondents in $1bn-5bn (78%) and $5bn-20bn (88%) categories indicated that familiarity with standards is the most important skill they want the employees to have, this was followed by programming language expertise (75% for $1-5bn revenue companies), followed by knowledge of statistical methods (80% for $5-20bn employees). Meanwhile, expertise in EDC systems, knowledge of statistical analysis, and integrating data from various sources were the three most important skills reported in the >$20bn companies.
Digital Maturity: Where the Industry Stands Today
To understand the landscape of technology adoption, we assessed the maturity level of technology across functions and some of the biggest challenges companies are facing with the current technology infrastructure.
Despite the promising trends we have witnessed in the adoption of GenAI and ML over the course of 3 years, currently, AI/ML technology adoption is minimal across all CDM functions, with no function exceeding 10% of its application. This indicates that while organizations are embracing automation, AI/ML is still in the experimental stage for most functions. The most advanced use of automation (without AI/ML) is found in TLF creation (54%), suggesting that organizations see significant benefits in automating this highly repetitive task.
For protocol development, there is a higher reliance on basic tools like MS Office, reflecting the slower need to adopt advanced technologies in this critical phase, possibly due to the complexity and regulatory scrutiny involved. Meanwhile, workflow management systems are widely used across functions (e.g., 40% in clinical data capture, 49% in CSR authoring), indicating that organizations are transitioning from basic tools to more structured approaches, but many have not yet embraced full automation or AI/ML.
<10%
AI/ML adoption remains low across all CDM functions
Automation is making significant inroads in key data-heavy processes within clinical trials, particularly in SDTM domain creation and ADaM generation/TLF outputs. SDTM domain creation shows a strong focus on automation, with 52% of organizations automating 50-75% of this process, indicating its priority for streamlining. ADaM generation and TLF outputs follow closely, with 44% automating 25-50% and 41% automating 50-75%, reflecting their emphasis on improving data handling efficiency. While clinical data capture (EDC) shows moderate progress, with 60% of organizations automating 25-50%, there is still ample room for growth. Conversely, protocol development and CSR authoring lag behind, with 72% of respondents reporting less than 25% automation in protocol development, signaling that this remains largely manual. CSR authoring shows slightly more progress, with 41% automating 25-50%, but only 13% have achieved 50-75% automation, highlighting the need for further technological integration in these areas.
Half the respondents said that rapidly creating high-quality SDTM (Standardization and Harmonization) data from raw study outputs was the biggest challenge. This indicates transforming raw study outputs into standardized SDTM datasets, is a critical area where automation, AI/ML, could add considerable value, and improve overall efficiency. A notable portion of respondents still struggle in transforming data into actionable insights citing the need for more advanced analytical tools or AI/ML models that also provide better dashboards and granular reports to improve the accuracy of data analysis. There is a clear demand for streamlining both the reporting and start-up phases of clinical trials. Automating report generation processes could reduce the time spent preparing documentation for regulatory bodies, speeding up submissions. Similarly, delays in study initiation, particularly in site selection, activation, and patient recruitment, can be mitigated by AI-driven tools for feasibility and trial design, significantly reducing the time to first-patient enrollment.
Unlocking Data Efficiency through GenAI and ML
In Clinical operations, GenAI can rapidly process and analyze large volumes of data to uncover insights, optimize trial protocols by refining eligibility criteria, and enhance patient adherence through personalized communication. Additionally, it automates the creation of trial documents, while continuously monitoring trial data to identify risks early and enable timely interventions. This survey also delved into understanding the leaders’ perspectives about the potential benefits and challenges of adopting GenAI/ML in CDM and what use cases they are considering to help improve the overall CDM function.
GenAI and ML has created a lot of buzz in the industry regarding the transformational impact it can bring to the CDM function and operations. However, across the board, companies are extremely cautious in adopting these disruptive technologies for strategic and operational purposes. Hence, 61% of the respondents said they are using Generative AI (GenAI) to a limited extent, primarily piloting technologies for evaluation. Regionally, 60% and 63% of respondents from US and Europe respectively agreed that they are in the evaluation stage of testing various pilot assignments to assess the best use cases that are sustainable and compliant with industry norms.
AI/ML implementation in CDM is mainly focused on optimizing data processing tasks such as generating TLFs, reducing database go-live timelines, and accelerating data standardization, with less emphasis on protocol authoring and CSR generation at present. These priorities highlight the industry’s immediate focus on leveraging AI/ML to speed up data-driven processes and improve overall trial efficiency.
Adoption of GenAI in clinical trials for faster generation of the protocol is an important use case in two extreme revenue buckets; $500-1bn revenue companies (75%) and >$20bn (67%) respectively. Reducing the time to study database go-live is of higher importance for companies in the $1-5 billion revenue range (79%). Contrasting the global average, large organizations (>$20 billion) prioritize CSR summaries (83%). Regionally, reducing the go-live timelines (75%) and swift generation of accurate TLFs (72%) are almost equally important for respondents from the US, whereas in Europe, at 89% response rate, TLFs use case take over the need to reduce the go-live timelines (78%) which is the second most important application.
GenAI adoption for protocol generation is particularly significant among companies with annual revenues exceeding $20 billion.
Companies are largely looking at a timeline of 6 to 12 months to actively adopt GenAI and ML for CDM operations. It is interesting to note that while 79% of respondents said that rapidly generating accurate TLFs was the most pressing use case, the adoption timelines of 6-12 months for this is still mostly on par with other use cases and not earlier than that. When we look at a timeline of 6 months to 3 years, at 79%, accelerating the generation of high-quality SDTM and ADaM standards data will be the most widely adopted GenAI use case, this is followed by reducing the time to go-live (68%) and swift generation of TLFs (67%).
A staggering 83% of respondents from large companies said they will use GenAI over the next 3 years for TLF generation, and suggesting summaries for CSR authoring. Meanwhile, 75% of respondents from $500mn-1bn companies stated they will leverage GenAI in the next 1-3 years to accelerate the generation of high-quality SDTM and ADaM standard data. Regionally, 42% of respondents from the US stated they will use GenAI in the next 6-12 months to accelerate the generation of high quality SDTM and ADaM standard and 42% of respondents from Europe are looking at this implementation timeline for faster generation of protocols.
GenAI and ML in Clinical Trials: Navigating Challenges, Reaping Benefits
The majority of respondents believe in GenAI’s transformative potential and that it can help them deliver superior outcomes. The different scales of adoption across different tiers of companies also validate that organizations are already on the GenAI adoption journey. Across all use cases, the goals organizations are trying to achieve are standard. As we can see in the below chart, efficiency, accuracy and the pace of operations continue to be the core factors influencing the decision to implement GenAI.
Achieving greater efficiency in CDM operations is the most important factor driving the adoption of AI/ML across the board. This perception is much stronger at the CXO level and $1-5bn dollar revenue companies respectively (85%) each. This was followed by AL/ML’s ability to improve study accuracy, with 80% of CXOs stating this as a key benefit. Regarding the role of AI and ML in maintaining the quality of standards, 70% of respondents in the $1-5bn revenue category said the impact is much stronger. By region, in the US, the respondents’ opinion is equally divided as high and medium 48% each, whereas in Europe 58% of them unanimously agree that AL and ML can drive higher quality standards.
However, like with all technology initiatives, this one is not without its set of challenges. To understand the hurdles organizations currently face, we asked them to highlight the most pressing issues that either delay implementation or stop them from adopting GenAI for a specific use case.
Core drivers for GenAI adoption
Efficiency
Accuracy
Operational speed
Most respondents said the lack of defined regulations for AI and ML adoption is the most significant barrier, indicating that the industry direly needs regulatory frameworks to ensure the compliant use of AI technologies. While companies continue to invest heavily in technology and have already identified use cases for the next 3 to 5 years, it is challenging to scale these initiatives without adequate regulatory support. Closely aligning with the first challenge is the Undefined Risks to Research Subjects AI and ML presents (57%). Irrespective of the stream of operations with the Clinical and R&D function, patient centricity and patient safety are the key pillars and hence safeguarding their interest and privacy will continue to be important across the board despite the efficiencies AI brings to the table. Over half of respondents (52%) said that AI could lead to a loss of SME resources which could result in a decrease in specialized knowledge in the long-term, which is vital for the nuanced decision-making required in clinical trials.
Further, with 45% of respondents stating they see the loss of accountability and transparency as a risk, it reflects the need for implementing explainable AI for the GenAI use cases in CDM. It also presents significant opportunities and first-mover advantage to technology players who are developing AI/ML solutions to solve CDM challenges, as vendors who can build solutions that give a comprehensive audit trail of activities have higher collaboration chances, especially with companies that generate revenues of $1bn and above, as the volume and variety of their clinical trials are significantly higher.
Leveraging Outsourcing for Operational Excellence
SDTM/ADaM creation and TLF generation show a high degree of outsourcing, with 51% and 45%, respectively, outsourcing 25-50% of these activities. This outsourcing likely stems from the specialized knowledge required to meet regulatory standards (SDTM/ADaM) and the need for efficient report generation (TLF). Clinical data capture (CDC) and database development are more frequently outsourced, with 51% of organizations outsourcing between 25-50% and 24% outsourcing even more (50-75%). These are also the same set of activities where companies are applying automation at a relatively higher level than other activities which could indicate that most of the outsourcing activities are related to automation of operations in these 2 streams. Protocol development sees low levels of outsourcing, with 51% of organizations outsourcing less than 25% of the activity. This suggests that organizations prefer to handle critical trial design in-house, likely due to its impact on study objectives, regulatory compliance, and trial success. While 47% of organizations outsource less than 25% of CSR authoring, 27% outsource between 50-75%. This indicates that while organizations may retain some level of control over the writing of Clinical Study Reports, external vendors play an important role in ensuring timely and high-quality submissions.
For most of the activities, the respondents’ opinions across segments aligned with the global average. However, for certain activities it was more pronounced; 50% of CXO level delegates and large companies said they are outsourcing 50-75% of TLF generation activities. 75% of SVP-level respondents and 58% of European respondents stated that 25-50% of these activities are outsourced. 70% of respondents in the $1-5bn category said they are outsourcing CSR authoring activities.
Drivers for outsourcing in each of the below activities
Better management of labor costs
74% of respondents rated cost management as Very or Extremely Important for SDTM and ADaM creation outsourcing decisions. This could indicate that tasks requiring standardization are outsourced to specialized vendors with economies of scale to manage costs efficiently. TLF generation (53%) and Clinical Data Capture system development (50%) are the next important areas where respondents said they can manage costs better by outsourcing these activities. By revenue and by region, over 70% of respondents in the $1bn-20bn categories cited labor cost management as an extremely important reason to outsource SDTM and ADaM activities. However, 80% of respondents in the >20bn revenue companies said outsourcing CSR authoring activities was for the benefit of cost management.
Automation-Driven Outsourcing is high in EDC and Database Development
Better analysis and insights
On the lines of better management of labor costs, at 72%, better insights and analysis also rank highest for SDTM and ADaM creation. This was followed by Clinical Data Capture activities (61%). This indicates that the standardization and transformation of clinical data for regulatory submissions requires strong analytical capabilities, that most sponsors might not have in-house given the limited knowledge they have based on the number of trials they handle, especially smaller companies. In TLF Generation, 50% of respondents rated better analysis and insights as Very or Extremely Important. This reflects the high importance of deriving accurate and comprehensive insights from clinical trial data to support the creation of tables, figures, and listings. Respondents in the $5-20bn companies stated better analysis and insights influenced their decision to outsource Clinical Data Capture activities (80%) and SDTM activities. However, for $20bn and above companies, for this parameter also CSR authoring was an important activity.
Application of AI/ML models
In SDTM and ADaM creation, the application of AI/ ML models is a major outsourcing driver, with a significant 70% of respondents rating it as Very or Extremely Important. Automation in generating tables, figures, and listings is crucial for improving the efficiency of clinical trial reporting and analysis, and organizations are outsourcing this to leverage advanced AI/ML tools that reduce manual work and improve data insights.
For companies in the $500mn-1bn category, this driver was important across SDTM and TLF activities respectively. Meanwhile, respondents in the $5- 20bn category stated that adoption of AI/ML was the most important driver to outsource Clinical Data Capture and SDTM activities.
Centralized Metadata Governance
63% of respondents rated this driver as either Very Important or Extremely Important for SDTM activities suggesting that organizations prioritize governance in managing Clinical Data Capture systems, making it a key outsourcing consideration. 51% of respondents rate metadata governance as Very Important and Extremely Important for outsourcing SDTM and ADaM creation. The standardization requirements for regulatory submissions emphasize the need for centralized governance, making it a strong outsourcing driver, as it helps ensure accurate and compliant data. 70% of respondents in $500mn-1bn category companies said it was an important factor for SDTM activities, while 80% of them said it was moderate and very important for TLF generation. In the $1bn-5bn revenue range, 95% of respondents said centralized metadata governance was a moderate to very important factor in outsourcing SDTM activities, while in $5-20bn category it was 83%. In this segment 78% of respondents stated this was an important driver for outsourcing CSR authoring activities. Over 80% of respondents in the >$20bn mentioned it was an outsourcing driver for CDC and SDTM activities respectively.
Improved monitoring and reporting mechanism
78% of respondents rate improved monitoring and reporting as Very or Extremely Important for outsourcing SDTM and ADaM Creation. This suggests that the rigorous standards required for these domains demand accurate and continuous reporting, making it a highly significant driver for outsourcing decisions in this area. A large proportion (60%) of respondents rated this driver as Very or Extremely Important, emphasizing that Clinical Data Capture relies heavily on improved monitoring and reporting mechanisms. This indicates the critical need for better oversight and quality control in managing data integrity, making it a strong outsourcing driver in this function. With 59% of respondents rating improved monitoring and reporting as Very or Extremely Important, TLF Generation shows a high reliance on these mechanisms for outsourcing. Meanwhile, over 70% of respondents in $500mn-$5bn category stated outsourcing SDTM and TLF activities benefited from improved monitoring.
Reduced timelines
71% of respondents believe that reducing timelines is Very or Extremely Important when outsourcing SDTM and ADaM Creation. The high importance placed on this driver reflects the time-sensitive nature of converting raw clinical data into standardized formats. Shorter timelines can accelerate downstream processes such as statistical analysis and reporting, making outsourcing attractive in this function. 69% of respondents rate Reduced Timelines as Very or Extremely Important in outsourcing TLF Generation. Over 70% of respondents in the $500mn-$1bn category and 68% of respondents in the $1bn- 5bn said reduced timelines drove the outsourcing decision for SDTM and TLF generation activities. For $5-20bn respondents CDC activities and SDTM activities ranked higher at 70% and 80% respectively. Interestingly for large companies this was an important factor across all functions (+60% responses).
Expertise in specific Therapeutic Area (TA)
SDTM and ADaM Creation, Clinical Data Capture and TLF generation show a high reliance on therapeutic area expertise, with 66, 57% and 56% of respondents, respectively, rating this as Very or Extremely Important. These functions require specialized knowledge to ensure the accuracy and relevance of the data being captured and transformed. Since these processes involve transforming raw clinical data into structured formats, therapeutic area expertise ensures data accuracy and relevance, especially for complex or specialized studies. Outsourcing partners with strong TA expertise can handle the nuances of different clinical trial designs and regulatory requirements. With data integrity and capture processes varying across therapeutic areas, having domain-specific expertise is crucial for ensuring accuracy and compliance. This highlights the importance of having outsourcing partners with TA-specific insights to ensure precise and relevant outputs. Over 75% of respondents across small and large companies said expertise in specific TA was an important factor they considered while outsourcing CDC and TLF generation activities.
75%+
respondents prioritize therapeutic area expertise when outsourcing CDC and TLF generation
Conclusion
Next steps for Clinical Data Management leaders: Drawing on our research and domain expertise, we have identified three key actions that we believe organizations can take to leverage the full potential of technology and workforce and strengthen the CDM function.
Prioritize functions that can accelerate technology adoption
Improvements in SDTM and ADaM creation, along with the automation of TLF generation, have a significant impact on the efficiency and quality of clinical trial data management. Streamlining these processes not only enables greater automation but also accelerates database lock and regulatory submissions, ultimately speeding up the development and approval of new therapies. As such, it’s no surprise that our survey revealed these activities as top priorities among respondents. From being key focus areas for the year to prime candidates for outsourcing and adoption of Generative AI use cases, SDTM, ADaM, and TLF automation have garnered the highest interest across the board, reflecting their critical role in optimizing clinical trial operations and expediting time to market for life-saving treatments.
GenAI use cases should strengthen existing processes
Seventy-five percent of respondents from smaller companies indicated that they are prioritizing the use of GenAI to accelerate protocol generation, while an impressive 83% of respondents from large organizations plan to leverage GenAI for generating CSR summaries. This highlights that, while the industry broadly focuses on reducing database go-live time and accurately accelerating TLF generation, priorities vary significantly between company segments.
To ensure sustainable GenAI adoption and achieve meaningful cost and resource efficiencies, organizations must strategically identify the most relevant use cases. Companies should explore innovative approaches for developing and deploying these solutions, whether through in-house development, strategic partnerships with vendors, or outsourcing. The decision must consider investment requirements, potential challenges in prioritizing use cases, and the complexities of scaling GenAI adoption, particularly in navigating regulatory approvals. Strategic alignment will be key to maximizing the impact of GenAI on clinical trial data management.
Channelize outsourcing strategies
Outsourcing levels and the factors driving them vary across different activities within the CDM function. For SDTM and ADaM creation, respondents cited better labor cost management and enhanced analysis and insights as key reasons for outsourcing. In contrast, accelerating timelines and accessing therapeutic area expertise were identified as the primary drivers for outsourcing TLF generation.
Organizations should assess their current outsourcing levels in the CDM function and weigh the benefits of leveraging specialized external resources while maintaining strategic in-house control. By tapping into external expertise for specific activities, companies can optimize efficiency and quality, while retaining oversight on critical functions to ensure alignment with broader organizational goals.
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