{"id":247009,"date":"2025-07-07T19:19:25","date_gmt":"2025-07-08T00:49:25","guid":{"rendered":"https:\/\/www.omegahms.com\/?p=247009"},"modified":"2026-02-10T02:53:43","modified_gmt":"2026-02-10T08:23:43","slug":"four-reasons-to-consider-outsourcing-clinical-trial-data-management","status":"publish","type":"post","link":"https:\/\/www.omegahms.com\/blog\/four-reasons-to-consider-outsourcing-clinical-trial-data-management\/","title":{"rendered":"Four Reasons to Consider Outsourcing Clinical Trial Data Management"},"content":{"rendered":"\t\t
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Sustainable and scalable data management services with skilled resources are paramount to meet your clinical trial and research goals.<\/h3>\n

Health systems, oncology centers, academic researchers, and others must curate and standardize clinical trial data to conduct meaningful analysis. This labor-intensive process is mission critical to accelerate research and improve diagnosis and treatment. Yet the increasing volume of clinical trials and data, industry-wide staffing shortages, and evolving regulatory requirements are hindering the curation of high-quality data and overwhelming already burdened clinicians and researchers.<\/p>\n

Sustainable, scalable data management services with skilled resources are essential to ensure high-quality data curation for academic research. As the pressure to process data faster and more inexpensively increases, clinical trial leaders are turning to outsourcing partnerships to meet deadlines and to reduce the time from data abstraction and hypothesis to publication and treatment. Below are four reasons why outsourcing can be a strategic method to meet clinical trial data management<\/a> goals.<\/p>\n

1.\u00a0The increasing volume of RWD is overwhelming to manage.<\/strong><\/h4>\n

As of May 2025, there are approximately 23,000<\/span> clinical trials recruiting patients in the U.S.[i] The duration of Phase III clinical trials nearly doubled between 2010 and 2022, increasing from two years to 3.5 years.[ii] The U.S. FDA now puts the high end of the average at four years.[iii]<\/p>\n

Real-world data (RWD) is scattered across multiple systems. Typically, healthcare data is maintained in multiple disparate systems \u2014 structured data and unstructured patient data in EHRs, claims and billing systems, registries, and more. Clinical trial data includes data in clinical trial management systems (CTMS) and data sets maintained in personal spreadsheets and databases.<\/p>\n

In a survey of professionals involved in clinical data management, 50% of respondents reported using up to five different data sources, and 37% use between six and ten.[iv] A typical Phase III trial now uses close to 10 data sources and generates an average of 3.6 million data points \u2014 three times the amount reported just a decade ago.[v] CTMS and EHR platforms can yield rich data sets but are often siloed.\u00a0<\/p>\n

The increasing number of trials, siloed data sources, data backlogs, and other factors are driving the need to determine the best way to manage, harmonize, and democratize clinical trial data.<\/p>\n

2.\u00a0Staffing shortages of skilled resources continues to be a challenge.<\/strong><\/h4>\n

Healthcare leaders continue to cite staffing shortages and financial concerns as their most pressing challenges. In fact, 77% of life sciences and healthcare companies report having difficulty finding the talent they need.[vi]\u00a0Turnover is prevalent, and researchers are stretched. They are also high-paid resources that should be focused on research rather than curating data.<\/span><\/p>\n

Research organizations are exploring options to meet the growing demands of registry data policies, procedures, reporting analysis, and data submission that call for knowledgeable workers. This opens the door to leverage natural language processing (NLP) and AI for improved efficiencies and productivity,[vii] but these technologies also require a highly skilled and specialized workforce.<\/p>\n

3.\u00a0Clinical trial data management is labor intensive.<\/strong><\/h4>\n

In a widely cited survey of top clinical data management professionals, 95% of respondents reported manual effort is involved in aggregating, cleaning, and transforming clinical trial data, and two out of three respondents experienced issues with the process.[iv]<\/p>\n

In that same survey:<\/p>\n