**Executive summary **
Computerised systems are being increasingly used in clinical research. The complexity of such systems has evolved rapidly in the last few years from electronic case report forms (eCRF), electronic patient reported outcomes (ePROs) to various wearable devices used to continuously monitor trial participants for clinically relevant parameters and ultimately to the use of artificial intelligence (AI). Hence, there is a need to provide guidance to all stakeholders involved in clinical trials reflective of these changes in data types and trial types on the use of computerised systems and on the collection of electronic data, as this is important to ensure the quality and reliability of trial data, as well as the rights, dignity, safety and wellbeing of the trial participants. This would ultimately contribute to a robust decision-making process based on such clinical data.
This guideline will describe some generally applicable principles and definition of key concepts. It also covers requirements and expectations for computerised systems, including validation, user management, security, and electronic data for the data life cycle. Requirements and expectations are also covered related to specific types of systems, processes, and data.
**Introduction **
As described above, the change in data and trial types and thereby the use of computerised systems presents new challenges. The European Medicines Agency (EMA) ‘Reflection Paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials’ started to address these when it was published in 2010. However, the development of and experience with such systems has progressed. A more up-to-date guideline is needed to replace the Reflection Paper.
There is no requirement or expectation that the sponsors and investigators use computerised systems to collect data; however, the use of data acquisition tools if implemented and controlled to the described standard, offers a wide variety of functions to improve data completeness, consistency and unambiguity, e.g. automatic edit checks, automated data transfers, validation checks, assisting information and workflow control.
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