How to Improve Clinical Data Trial Quality in Modern Trials

High-quality clinical data is the foundation of reliable clinical trials, enabling drug developers to make informed decisions and accelerate the development of investigational products. In practice, clinical trial data quality is not just about whether data is present or error-free. It is about whether the data is fit for purpose. This means that the data must be sufficient, credible, and usable for the scientific, operational, and regulatory decisions a study needs to support.

Effective Clinical Data Management (CDM) is essential to ensuring data collection, entry, validation, and reporting are performed effectively so teams can produce high-quality, credible, and statistically sound data of a clinical trial. Accurate and timely trial data can help study teams identify patterns, predict outcomes, and improve patient outcomes.

For Quality Control, the guidelines based on Good Clinical Practices (GCP), which are an international standard of ethical and scientific quality for designing, conducting, recording, and reporting clinical trials with human subjects are traditionally followed by drug developers. Within data management, reviewing and maintaining data accuracy and quality is a dynamic process during the conduct phase of the study. However, quality should not be treated as something checked only during cleaning.

It begins at study set-up, with decisions about what data is truly critical, how they will be captured, how they will be transformed, and how their meaning will remain clear across systems and outputs. Additional study documents like CRF Completion Guidelines (CCG) and/or Data Entry Guidelines (DEG) are created to ensure data are entered consistently by all site and study staff as expected. More broadly, data management plans, standard operating procedures (SOPs), and clear role definitions help make responsibilities, workflows, and escalation paths more consistent across the study.

How Clinical Data Quality Has Changed in Modern Trials

The shift from paper-based systems to Electronic Data Capture (EDC) reshaped clinical data quality priorities. The paper world had a clear understanding that the quality of the clinical data collected was often strongly linked to transcription accuracy. However, with EDC systems, manual transcription risks can be reduced, and the focus has shifted towards the quality of study design, collection workflows, edit checks, integrations, mappings, and downstream transformations. That does not mean quality concerns disappeared. Instead, the risk profile changed. In modern trials, data quality issues often arise from how data is defined, collected, integrated, mapped, reconciled, and interpreted across multiple systems and sources. However, it is the role of the data management teams to be involved in many efforts to prepare data for appropriate analysis and submissions. The growing complexity of data sources, including ePRO, eCOA, EMR, and wearables, demands a robust and integrated approach to data management

The quality of the efforts which result in developing data collection tools/eCRF and cleaning the data collected can directly impact the quality of the data collected. Thus, it is important for organisations to look into managing the quality of the workstreams the teams are involved in, especially as we are seeing increased streams of data being collected from various sources like eSource, ePRO/eCOA, EMR/EHR, wearable devices, mHealth, and AI based tools for adherence tracking, etc. Teams also need to assess whether data is complete enough, plausible enough, consistent enough, timely enough, and sufficiently well-defined for their intended use. In other words, quality is multidimensional, and it needs to be managed across the full data lifecycle rather than treated as a late-stage clean-up exercise.

Regular data trend analysis runs, using programs to identify outliers and deviations, and performing critical data review throughout the study conduct are few of the practices CDM can follow as part of validation activities to ensure data quality is maintained throughout the study conduct. Programming of data listings, real-time electronic checks within the system, and reconciliation review are also commonly used to monitor and maintain data quality during study conduct. Further using risk-based monitoring (RBM) approaches to focus resources on the most critical data, identifying errors and inconsistencies more effectively will enhance data quality throughout the CDM process. This lifecycle view is useful because quality work starts in planning, continues through database build and study operation, and still matters when data are transformed, reused, or interpreted later for analysis, submission, or secondary research.

Key Strategies for Improving Clinical Data Quality

Below are four practical strategies for improving clinical data quality in modern trials. Together, they work best when they are anchored to study objectives, critical data, and clear operational ownership.

1) Effective Design and Review of Data Collection Tool (DCT) Design Specifications

Clinical trials are nothing but an expensive method of ‘collecting data’. If the tool is not designed properly, teams often end up relying on avoidable fixes, rework, and extra review effort that affect cost and timelines. Specifications are reviewed normally, however how effectively are we looking at the appropriateness of the design from the site’s point of view for EDC and from the patient’s point of view for ePRO? The aim should be to collect the data that are genuinely needed for the protocol objectives and key endpoints, while reducing avoidable burden from non-critical fields and unclear design choices. For example, a patient suffering from a muscular dystrophy would be more interested in assessing how best they can do their daily chores or how well they can play with their grandchildren rather than measuring a six-step walking test to be reported every day.

Using validated user-friendly EDC systems that are compliant with regulatory requirements and aligned to industry best practices plays vital role in designing an efficient eCRF and setting up the required integration by clinical data managers to serve the quality requirements and fit for purpose data needs. Organisations should prioritise patient-centric design to improve the quality of responses and overall data integrity. This is also where teams should identify critical data early: the data points that matter most for subject safety, primary and secondary objectives, key protocol decisions, and credible interpretation later on.

2) Automation and Integration

Using clinical data management systems that are interoperable with other health systems (e.g. EHRs, laboratory information systems) enable seamless data exchange and reduce manual transcription errors. APIs can support automated data flows across disparate sources, reducing manual handling that may introduce errors. Reducing manual interventions in data collection can help, particularly where where solutions enable EHR/EMR integrations play an important role. Use of medical grade devices to collect data directly from patients when using wearables and the mHealth tool can help calibrated data flow into integrated EDC databases with minimal or no interventions. AI based tools can collect medication adherence data without human intervention. In addition, using integrated eCOAs, Central lab APIs, medical coding, imaging and safety data workflow with EDCs will help centralised data collection with minimal manual intervention in data transfer from varied sources and is currently a preferred set up many drug developers.

However, automation and integration do not guarantee quality on their own. Modern risks often sit in ETL logic, mapping decisions, source-system differences, inconsistent definitions, mixed-format data flows, and unclear provenance. Data can move seamlessly between systems and still be wrong, incomplete, delayed, or misinterpreted when transformation rules or metadata are weak. That is why integration quality should be assessed not only by whether data transfer works, but by whether the transferred data remain traceable, interpretable, and aligned with the intended analysis and review needs.

3) Data Standardisation

Implementing data standards early in the project lifecycle, such as CDISC-compliant eCRFs and standard mapping algorithms, can streamline the data management process and improve the consistency of data across multiple studies. Automation of steps converting the data collected to standards would enhance quality as well as efficiency. The process starts from developing CDISC compliant eCRFs to implementing standard mapping algorithms earlier in the project lifecycle than usual so that the SDTM requirements during the conduct of the study would be addressed seamlessly with improved quality. This helps to streamline the downstream; statistical programming requirements and make them more efficient, accurate and consistent across multiple data releases within the same study or across a program or portfolio of studies.

Standardisation is not only about target structures such as CDISC. It also depends on clear source definitions, consistent field meaning, metadata, controlled terminology, and documentation that explains where data came from and how they were transformed. Data dictionaries, mapping specifications, and provenance records help users understand what a variable actually represents, which becomes especially important when data are integrated from multiple systems or reused later.

4) Continuous Training Knowledge Sharing

We all know less human intervention can bring in more quality as it reduces the chance of errors; however, planning the automation and integration to support goals set is ultimately important. Generic and study level trainings have become just an onboarding routine. Developing comprehensive understanding with effective training is key to making teams deliver consistent, reliable work. Training should focus on aspects of effective study set up and conduct conceptualised from a blend of technical and clinical knowledge to ensure that teams are well-prepared to maintain high data quality standards. Refresher training should be conducted/provided whenever there is any amendment to the protocol, eCRF, or any relevant study documents impacting CDM.

Data management teams should be encouraged to develop skills in data analytics, enabling them to better identify trends and outliers in the data that could indicate quality issues. Organisations should also encourage knowledge sharing platforms within their infrastructure enabling teams to create various communities of learning. Training is most useful when it supports clearly defined processes, systems, standards, and responsibilities rather than acting as a substitute for them.

Quality Control in Clinical Data Management

Quality control in clinical data management is the operational process of checking whether study data are complete, plausible, consistent, and suitable for review and analysis. It sits close to data collection and study conduct, helping teams detect issues early enough for correction to remain practical. Quality control also continues throughout the study, supporting more reliable data as the trial progresses.

In day-to-day CDM work, quality control often includes edit checks within the system, data listings review, reconciliation review, query generation and follow-up, trend review, and focused review of critical data points. These activities help identify missing values, unexpected patterns, inconsistencies across sources, and issues that may affect subject safety, endpoint interpretation, or downstream analysis. They also help teams focus attention on the data that matter most.

Quality assurance plays a related role with a different emphasis. Quality control focuses on detecting issues during study conduct, while quality assurance focuses on establishing robust processes, standards, and controls early so that data collection, review, and handling are more consistent from the outset.

This distinction is useful because quality control works most effectively when it is supported by strong study design, clear definitions, sound mappings, and well-established governance. Well-written specifications, clear metadata, appropriate standards, and defined responsibilities across the study team all strengthen the value of quality control during the study.

Conclusion

Teams need to define quality in relation to intended use, focus effort on critical data, manage quality across the full data lifecycle, and control the risks introduced by integrations, mappings, and multi-source workflows. Technology can support that work, but it does not replace governance, metadata clarity, and sound operational design. When those elements are in place, clinical trial data are more likely to be reliable, interpretable, and ready for analysis, submission, and decision-making.

Quanticate’s clinical data management team are dedicated to ensuring high quality clinical data and have a wealth of experience in data capture, processing and collection tools. Our team offer flexible and customized solutions across various EDC platforms. If you would like more information on how we can assist your clinical trial, request a consultation below.