2026年全球医药行业临床数据趋势报告

Table of Contents Outlook for 2026: New Forces Driving Impactful Innovation...................................................3 Trends reshaping clinical data.....................................................................................................4 1.Risk-based data management theory needs to become reality.........................................42.Automated orchestration will become core to the clinical data management role.........63.Smart automation and meaningful AI are ready to deliver ROI..........................................74.We’re moving from ‘all-encompassing’ MDR to leveraging core standards better..........85.Study design is incorporating patient optionality and site capability................................96.Sponsors and sites are working to remove transcription.................................................11 7.ICH E6(R3) demands innate traceability and drives proportionality................................128.Endpoint-driven design enables RBDM...............................................................................129.Deeper focus on eCOA data reliability and integrity..........................................................1310.Boosting data management resilience and future economics........................................14 Outlook for 2026:New Forces Driving Impactful Innovation In clinical data, there is often a lag between a new technology becoming available and itswidespread adoption and return on investment (ROI). For instance, it took about a decadebefore most of our industry regularly used electronic data capture (EDC) during clinicaltrials instead of paper. Instead of signifying caution about embracing innovation, these delays show that it takestime to develop new processes and embed change management, and to overcome riskaversion in our highly regulated industry. Today we believe there are six key trends tofollow closely, each of which already has real use cases. Along with four emerging trends,we anticipate they will reshape clinical data in years to come. A unifying theme across these ten trends is the notion of “simplify and standardize”.This applies focus to the increased complexity in the clinical landscape and drives ustoward impactful innovation that gives us more value with less risk. We are seeing greater connection between cross-functional teams, and many of thesetrends are themselves interconnected. For example, the shift to endpoint-driven design isfoundational to the rise of risk-based data management (RBDM). Others, such as the pivotto smart automation and ramp up of AI initiatives, as well as the changing perspectiveon metadata repository-driven (MDR) builds, reflect where companies want to focustheir energies. Many used AI pilots to test, fail, and learn and now believe that a mix ofrule-driven and AI-based automation will deliver the most significant cost and efficiencyimprovements. We hope this report helps your teams ideate, plan, and prioritize their clinical datainitiatives so that we can deliver better trials for all. Drew GartyChief Technology OfficerVeeva Clinical Data Six trends reshaping clinical data 1.Risk-based data management theory needs to become reality Regulators have long encouraged risk-based approachesto quality management (RBQM) and are now applyingthe same principles to data management and monitoring.Risk-based monitoring is advancing, but clinical datamanagement (RBDM) is lagging behind, with lessindustry and regulatory maturity despite the availabilityof guidance. Over half of data managers expect RBDM to impact theirrole in the near future[Figure 1], but most are yet to makethe leap of faith and move away from the security blanketof comprehensive review models. Given ever-expandingdata volumes, it is not sustainable for biopharmacompanies to scale data management linearly usingtraditional methodologies. Data management leadersare now looking at ways to institutionalize RBDM as anindustry. FIGURE 1 of data managers believeRBDM will be involvedin their role progressionover the next two years This will require data managers to become strategic partners to otherfunctions, such as RBQM, and utilize smart automation and AI to eliminatemanual effort and stale data (see ‘Automated orchestration will becomecore to clinical data management’and‘Smart automation and meaningfulAI are ready to deliver ROI’). These will be required if we are to movetowards RBDM. “For me the biggest challenge isthe resistance to change. We needa change in mindset from cleaningeverything to focusing on whatwe need to clean. My dream wouldbe to set up my study in EDC,feed that to CDB, and based onwhat we have done in the past,have AI categorize critical data andrecommend listings and checksand come up with a proposal for arisk-based plan” REAL USE CASES Some sponsors are experimenting with historical trend data for proactiveissue management. After defining thresholds and sharing data acrossdepartments, they assess how a trend