Why Automation Reduces Risk and Reinforces Clinical Data Quality

Clinical Researcher—December 2025 (Volume 39, Issue 6)

DATA-TECH CONNECT

Manny Vazquez, CCDM

The people collecting and handling clinical data report that the tools and processes currently used in clinical trials are slowing them down. In fact, new industry research shows it might be interfering with the quality of data.

Two-thirds of data managers and clinical research associates (CRAs) think data quality in clinical trials is at risk if the inefficiencies in execution persist. This highlights a known challenge in the industry—there are too many manual steps and disconnected technologies that don’t work together to conduct tasks.

Every week, each data manager spends more than 12 hours per study checking, cleaning, and reconciling data. Virtually all (97%) undertake this job outside their core clinical systems or are using a mix of technologies, including Excel.

The findings show a significant opportunity to simplify and standardize clinical trials. Prioritizing system integration and automation can improve data quality and enable data managers and CRAs to focus on value-add activities.

Inefficiencies Slowing Clinical Data Management

When data managers and CRAs are required to pivot between systems, spreadsheets, and e-mails to make sure everything is correct, they are more likely to overlook something vital. The mistake can have negative downstream impacts, including reduced data quality and increasing costs.

The root causes of inefficiency have been identified as too many manual steps or data re-entry (68%), inefficient workflows (58%), and systems that don’t talk to one another (59%). Together, these factors illustrate that even though clinical technology is advancing, the lack of connectivity across tools and subsequent processes creates additional work for teams.

Manual reconciliation across lab results, patient-reported outcomes, and the ever-increasing list of external data sources has a direct effect on the quality and dependability of the data collected and on team productivity. Burnout ranked second highest for data managers, a significant risk especially as data volumes grow and workloads increase. Most CRAs (91%) say burnout or turnover is the top consequence of unaddressed inefficiencies, and it is likely a key factor in the current shortage of trained clinical research staff.

Automation is Key for Data Managers

When asked how their jobs will change over the next two years, 71% of data managers stated they expect to use more automated tools to clean and examine data, and in turn, reduce the time and effort needed. For example, data managers might use automation tools that detect discrepancies and identify missing information.

The majority of data managers see their roles evolving to use more automation for data cleaning. {Source: Veeva}

Interestingly, fewer data managers (59%) think artificial intelligence (AI) will help them make decisions, prioritize actions, and surface insights, showing that automation is prioritized before AI. Some sponsors are already moving to centralize clinical data review and reconciliation in an automated application connected with clinical operations. The shift can directly address the most time-consuming manual tasks for data managers.

Improving Documentation and Tracking is a Priority for CRAs

Frontline CRAs have separate challenges to manage. Since systems often do not connect with each other to share and analyze data, CRAs must check every data entry manually during a site visit, which takes approximately one fifth of their time.

CRAs spend 18% of their time on manual trackers and documentation, some of the processes most reported as frustrating by CRAs. {Source: Veeva}

A top 20 biopharma CRA says, “I use OneNote for documenting monitoring visits before transcribing [into the clinical trial management system]. It’s not that I like it, I don’t have any other tool to keep everything in one place. I have to capture data in a non-validated system.”

Nearly half of CRAs (44%) say their priority is to improve documentation and tracking. If systems were better integrated, CRAs could get real-time data changes for review and perform data checks remotely. The improvements in workflow would cut down the need for human review, especially onsite. Given the amount of time potentially wasted in monitoring visit reports and the costs it adds to clinical trials, this is a key area of opportunity to streamline the work of CRAs and accelerate trial timelines.

Better tracking is increasingly critical for global clinical trials. The majority of CRAs expect risk-based monitoring to become the standard over the next two years. This would allow central monitoring teams to manage day-to-day site oversight and better target onsite visits for CRAs based on risk assessments and mitigation strategies. To make this a reality, monitoring teams need better risk-based quality management tools that are connected across all clinical systems to get a complete picture of the state of a research site.

Focus on Connecting Systems for Improved Productivity

Data managers and CRAs say the path to higher efficiency lies in integrating the tools and processes used to execute trials. Most (81%) agree that bringing together clinical data and operations systems will make it easier to conduct clinical trials and handle increasing volumes of data more efficiently.

With a connected clinical infrastructure, data can freely flow between stakeholders. More connectivity allows data managers to eliminate the need to reconcile data sources, CRAs to see updates as they happen, and study leaders to make better decisions based on complete and concurrent data.

Efforts to modernize clinical systems are ongoing, with 75% of data managers saying their teams are already modernizing while 57% of CRAs say the same. Still, many people think that the current standard operating procedures don’t fully leverage the resources that are available or accurately reflect how work really gets done. This imbalance can delay or deter the launch of new systems and lead to poor performance because processes aren’t optimized to take advantage of new technologies.

Reducing Risk Through Modern Data Management

The risk of poor data quality extends well beyond a single monitoring visit or listing review—it can permeate every stage of a clinical trial and, ultimately, affect the success of a regulatory submission. The industry can and must make real progress toward efficiency by reducing manual work and using connected systems and automated processes.

The results of this research serve as a rallying call from data managers and CRAs to clinical leaders, showing that change is needed for more efficient and effective clinical trials.