{"id":6721,"date":"2026-02-24T08:43:33","date_gmt":"2026-02-24T08:43:33","guid":{"rendered":"https:\/\/www.almacgroup.com\/clinical-technologies\/?p=6721"},"modified":"2026-02-24T09:25:21","modified_gmt":"2026-02-24T09:25:21","slug":"data-quality-and-monitoring-in-clinical-trials-reducing-errors-and-ensuring-compliance","status":"publish","type":"post","link":"https:\/\/www.almacgroup.com\/clinical-technologies\/blogs\/data-quality-and-monitoring-in-clinical-trials-reducing-errors-and-ensuring-compliance\/","title":{"rendered":"Data Quality and Monitoring in Clinical Trials: Reducing Errors and Ensuring Compliance\u00a0"},"content":{"rendered":"\t<div class=\"\">\n\t\t<div class=\"o-wrapper\">\n\t\t\t<div class=\"o-grid\">\n\t\t\t\t<div class=\"o-grid__item u-width-full\">\n\t\t\t\t\t<div class=\"s-cms\">\n\t\t\t\t\t\t<p><strong> Rich Wzorek<\/strong>, <em>Director | New Products &amp; Services\u00a0| Almac Clinical Technologies<\/em><\/p>\n<p><strong>Mark Dickinson<\/strong>, <em>Technical Manager| New Products &amp; Services\u00a0| Almac Clinical Technologies<\/em><\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\n\t\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Capturing usable source data is one of the most critical aspects of a clinical trial.&nbsp;With the global reach of many&nbsp;clinical trials,&nbsp;clinical sites&nbsp;can face unique challenges in ensuring the data is&nbsp;accurate, consistent, and&nbsp;fit&nbsp;for its intended&nbsp;use.&nbsp;Leveraging the right&nbsp;technology helps&nbsp;site users&nbsp;capture,&nbsp;validate, and aggregate&nbsp;trial data&nbsp;more effectively, offering clear advantages over manual processes.&nbsp;&nbsp;<\/p>\n\n\n\n<p>This article illustrates how tailored reporting solutions address the unique requirements of different data sets, ensuring both regulatory compliance and operational efficiency.&nbsp;<\/p>\n\n\n\n<p><strong>Technology&#8217;s Role<\/strong>&nbsp;<\/p>\n\n\n\n<p>Given the sheer volume of transactions&nbsp;that&nbsp;create&nbsp;meaningful data throughout a clinical&nbsp;trial\u2019s&nbsp;duration, manually&nbsp;validating,&nbsp;analysing,&nbsp;and aggregating this data would&nbsp;require&nbsp;large-scale&nbsp;execution of manual procedures.&nbsp;Ensuring compliance with&nbsp;these&nbsp;high-risk&nbsp;manual processes&nbsp;requires&nbsp;documented&nbsp;training and&nbsp;ongoing&nbsp;monitoring.&nbsp;&nbsp;<\/p>\n\n\n\n<p>By entering&nbsp;transactions&nbsp;into&nbsp;validated&nbsp;systems that not only&nbsp;integrate but&nbsp;also&nbsp;interact with each other,&nbsp;the need for&nbsp;widespread&nbsp;manual&nbsp;processes&nbsp;is significantly reduced.&nbsp;Manual&nbsp;processes have their place but should be&nbsp;the exception,&nbsp;not the norm.&nbsp;<\/p>\n\n\n\n<p>The data&nbsp;required&nbsp;for a clinical trial is very often&nbsp;just&nbsp;a small subset of what the site requires to provide the participant with the standard of care they deserve. This often has site users&nbsp;frequently&nbsp;switching&nbsp;between&nbsp;multiple&nbsp;systems,&nbsp;many of&nbsp;which&nbsp;are often not connected and require a certain level of manual transcription between systems.&nbsp;&nbsp;This can lead to data quality issues at worst,&nbsp;and&nbsp;a high&nbsp;volume of&nbsp;queries and discrepancies at best.&nbsp;<\/p>\n\n\n\n<p>For example, if&nbsp;a site user&nbsp;transcribes&nbsp;lab values incorrectly,&nbsp;it could&nbsp;lead to&nbsp;erroneous&nbsp;dosing calculations and potentially even&nbsp;provide the incorrect dosage to&nbsp;a patient.&nbsp;In contrast, using technology to&nbsp;copy lab values from one system to another&nbsp;greatly reduces&nbsp;the likelihood of human error.&nbsp;<\/p>\n\n\n\n<p><strong>Data Ownership<\/strong>&nbsp;<\/p>\n\n\n\n<p>Given&nbsp;the need for multiple systems&nbsp;and&nbsp;different types&nbsp;of data,&nbsp;it is important to&nbsp;analyse&nbsp;not only the&nbsp;data&nbsp;itself but also how other systems&nbsp;utilise&nbsp;the information.&nbsp;Take, for example,&nbsp;a&nbsp;subject being screened at a site.&nbsp;In isolation,&nbsp;it&#8217;s&nbsp;a&nbsp;seemingly&nbsp;straightforward&nbsp;activity to record, but&nbsp;this single event&nbsp;can have a myriad of downstream impacts&nbsp;across the clinical trial. Here are some considerations:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Who&nbsp;is responsible for&nbsp;data stewardship to ensure that the data is attributable,&nbsp;timely, and&nbsp;accurate?&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which system &#8220;owns&#8221; that&nbsp;screening&nbsp;data&nbsp;and&nbsp;can be considered the &#8220;source of truth&#8221;?&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How&nbsp;and where in the workflow&nbsp;will the data be&nbsp;validated?&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How should that&nbsp;newly screened subject&nbsp;reflect in&nbsp;dashboards, reports, and&nbsp;webpages&nbsp;used&nbsp;by site users?&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which other systems care about the subject being screened?&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>The answers to these questions can help guide how data should be stored and how other systems can&nbsp;expect to&nbsp;receive that data.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p>When designing a&nbsp;system, proper&nbsp;data validation&nbsp;requirements&nbsp;are essential&nbsp;for&nbsp;maintaining&nbsp;consistency&nbsp;and quality.&nbsp;&nbsp;These&nbsp;validations also&nbsp;enable&nbsp;sites&nbsp;to conduct&nbsp;more&nbsp;effective analysis of datasets&nbsp;which&nbsp;can&nbsp;encompass multiple trials or&nbsp;even&nbsp;entire&nbsp;clinical trial&nbsp;portfolios.&nbsp;Furthermore, restricting non-source systems from&nbsp;modifying&nbsp;source&nbsp;data helps&nbsp;maintain&nbsp;data integrity and simplifies the process of reconciling data during key milestones such as interim analysis and database&nbsp;lock.&nbsp;<\/p>\n\n\n\n<p><strong>Reporting<\/strong>&nbsp;<\/p>\n\n\n\n<p>To achieve&nbsp;optimal&nbsp;outcomes,&nbsp;it\u2019s&nbsp;crucial to design solutions that address specific reporting needs. Purpose-built tools streamline workflows and deliver precise answers,&nbsp;minimising&nbsp;manual&nbsp;intervention&nbsp;and reducing errors. Conversely, generic solutions often demand more manual effort and&nbsp;additional&nbsp;validation steps, increasing the risk of mistakes and inefficiencies.&nbsp;<\/p>\n\n\n\n<p>Reporting on a trial\u2019s data illustrates this principle in practice. Clinical sites often rely on exports or&nbsp;visualisations&nbsp;that pull information from multiple&nbsp;systems and&nbsp;combining that data accurately is essential to producing reports that satisfy both regulatory requirements and site\u2011level needs. When reporting tools are designed specifically for these nuanced use cases, they can apply the right logic for each dataset and reduce the need for manual reconciliation. In contrast, presenting all data in a uniform,&nbsp;generalised&nbsp;format may appear simpler at first glance but often leads to more exceptions, more back\u2011and\u2011forth validation, and a greater burden on site users to resolve inconsistencies.&nbsp;<\/p>\n\n\n\n<p><strong>Best Practices for Data&nbsp;Utilisation&nbsp;in Clinical Trials<\/strong>&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Intended use of report<\/strong>&nbsp;<\/td><td><strong>Sample&nbsp;dataset size<\/strong>&nbsp;<\/td><td><strong>Utilisation&nbsp;of data<\/strong>&nbsp;<\/td><\/tr><tr><td>Upcoming patient visits&nbsp;<\/td><td>1000 rows&nbsp;<\/td><td>Viewable\/actionable&nbsp;on web&nbsp;app&nbsp;<\/td><\/tr><tr><td>Current&nbsp;depot&nbsp;inventory&nbsp;<\/td><td>100000 rows&nbsp;<\/td><td>Exportable to Excel&nbsp;for further analysis&nbsp;<\/td><\/tr><tr><td>Quarterly audit trail of&nbsp;all site-related events&nbsp;<\/td><td>1M+ rows&nbsp;<\/td><td>Full-scale data transfer&nbsp;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>As&nbsp;shown above, the intended use,&nbsp;dataset size, and&nbsp;other&nbsp;problem-specific&nbsp;requirements&nbsp;can vary&nbsp;significantly across&nbsp;different types&nbsp;of&nbsp;site-level&nbsp;information.&nbsp;&nbsp;For instance, the&nbsp;count&nbsp;of quarterly events in an audit trail&nbsp;export&nbsp;is&nbsp;distinctly&nbsp;larger than an export of&nbsp;upcoming patient&nbsp;visits.&nbsp;&nbsp;A&nbsp;system&nbsp;not&nbsp;explicitly&nbsp;designed to&nbsp;handle the&nbsp;unique needs&nbsp;of&nbsp;each&nbsp;dataset&nbsp;could very easily result in&nbsp;an improperly&nbsp;sized and inefficient&nbsp;generalised&nbsp;solution.&nbsp;Purpose-built reporting tools tailored to&nbsp;each scenario will&nbsp;be more effective in&nbsp;delivering the&nbsp;desired&nbsp;business outcomes.&nbsp;<\/p>\n\n\n\n<p><strong>Conclusion<\/strong>&nbsp;<\/p>\n\n\n\n<p>In summary,&nbsp;effective&nbsp;use of technology can have very tangible benefits&nbsp;in the all-important goal of capturing&nbsp;accurate&nbsp;and usable data during the lifespan of a clinical trial.&nbsp;Strong data ownership,&nbsp;stewardship,&nbsp;and&nbsp;validation&nbsp;practices&nbsp;are&nbsp;essential to&nbsp;maintaining&nbsp;data quality, and&nbsp;right-sizing solutions to process&nbsp;well-defined categories of information ensure that&nbsp;the&nbsp;data&nbsp;remains&nbsp;fit&nbsp;for its intended purposes, supporting both regulatory&nbsp;compliance&nbsp;and operational efficiency.&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Capturing usable source data is one of the most critical aspects of a clinical trial.&nbsp;With the global reach of many&nbsp;clinical trials,&nbsp;clinical sites&nbsp;can face unique challenges in ensuring the data is&nbsp;accurate, consistent, and&nbsp;fit&nbsp;for its intended&nbsp;use.&nbsp;Leveraging the right&nbsp;technology helps&nbsp;site users&nbsp;capture,&nbsp;validate, and aggregate&nbsp;trial data&nbsp;more effectively, offering clear advantages over manual processes.&nbsp;&nbsp; This article illustrates how tailored reporting solutions [&hellip;]<\/p>\n","protected":false},"author":1870,"featured_media":6938,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"event_business_unit":[],"class_list":["post-6721","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.3.1 (Yoast SEO v26.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Quality and Monitoring in Clinical Trials: Reducing Errors and Ensuring Compliance\u00a0 - Clinical Technologies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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