Clinical Data Managers
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Apply knowledge of health care and database management to analyze clinical data, and to identify and report trends.
The occupation of Clinical Data Managers has been assessed with an automation risk of 57.4%, slightly below its base risk of 58.3%. This means that just over half of the current tasks performed in this role are susceptible to being automated by emerging technologies such as artificial intelligence (AI) and advanced data management systems. Several factors contribute to this moderate-to-high risk, including the structured, rules-based nature of many clinical data management activities. The continuing evolution of AI-driven data platforms specifically increases the potential for automating repetitive data processing and validation tasks that are central to this occupation. Among the most automatable tasks for Clinical Data Managers are: designing and validating clinical databases, processing clinical data (including receipt, entry, verification, or filing), and generating data queries based on logic checks or data entry issues. These functions typically involve standardized processes, logical frameworks, and error-checking mechanisms—all areas where automation and machine learning excel. As a result, these tasks are increasingly being integrated into automated workflows, reducing the need for manual intervention and allowing for greater processing speed and consistency. Conversely, some important aspects of the Clinical Data Manager role show strong resistance to automation. The most resistant tasks include providing support and information to various functional areas such as marketing, clinical monitoring, and medical affairs, developing or selecting software for specific research scenarios, and training staff on technical procedures or program usage. These duties demand interpersonal skills, tailored problem-solving, and the capacity for creative thinking—attributes reflected in the “Originality” bottleneck skill that holds a low automation risk of just 3.0%-3.4%. Such skills typically require nuanced decision-making and adaptive communication, both of which are challenging for AI to replicate, thus anchoring the value of skilled Clinical Data Managers even as automation expands within the field.