The clinical research landscape is continuously evolving with a direct impact on the innovative technology and data management needs. Technological advancements and data insights are required for the implementation of decentralized clinical trials, use of real-world data, real-world evidence, adaptive designs, and patient reported outcomes.It is important to track the changing requirements of data managers, anticipating future trends, to understand the factors that could spearhead innovation and efficiency.
Increasingly, clinical research involves collection of data from various sources and systems, with disparate data types and formats. Harmonizing, integrating and interpreting such data requires clinical data managers with a more complex background and skillset.
There are several common pitfalls that should be avoided when planning data management, and data managers should adapt to avoid them. One common pitfall is the lack of data standardization, which can be overcome by the use of common data elements to harmonize and integrate multiple data sources and meta data definitions. Data may be collected from Electronic Data Capture, clinical and healthcare Apps, from the patient, labs, imaging centers, and genetic testing and biomarker analysis centers. Increasingly, studies include data obtained directly from EHR/EMR systems.Harmonized and standardized data elements are critical to support data exchange, interoperability, consistent interpretation and integration of data from disparate data resources.Another common pitfall is not fully understanding data sources and associated clinical and/or scientific significance, volume, frequency and programmatic approach to integrate data based on the study protocol and design.
The representation of study data, along with broader data across all data sources and systems, can be efficiently managed through data harmonization engine and delivered as clinical decision support through the study Dashboard. This aggregated data content can also be made available outside the study program in a de-identified manner to support translational research. Such data sharing agreement may be carried out using extracts and reporting tool.
Figure 1. Modern Clinical Data Management and Data Science
4 Strategies for Data Managers to Advance Data Science
There are several key considerations to enhance the role of the data manager:
Data managers who can gauge the momentum of various tangible factors will help in transforming traditional data management to modern clinical data science. This will result in greater ownership of clinical trial data and ensure innovative solutions for data management and operational support for large and complex clinical trials.