Clinical data management for business intelligence

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Building the right platform for clinical data management is a difficult task, but the potential rewards are huge and a major differentiator.

Regardless of the intended use, clinical data management requires special protection of the data due to its particular characteristics , both with regard to the protection of patient identity (data masking software) and also of the critical information stored, as well as achieving and maintaining quality.

Research and healthcare practice generate large amounts of data that, with clinical data management at the service of business intelligence, can be very useful for use in research projects, for the diagnosis and treatment of diseases or, for example, to improve healthcare policy. Protecting this data, integrating it and making it serve the objective in fusion database has as a common denominator the importance of the data , its accuracy. If on the one hand it is critical information, which must be accurate to be useful, on the other hand it must be managed by carrying out a treatment tailored to the needs to achieve the goal we have.

The importance of the data and the establishment of a specific clinical data management plan will depend on this purpose, whether it is seeking to improve clinical practice or to serve as business intelligence focused on reducing costs or shortening deadlines to adjust to a budget in clinical research, for example.

Efficient technology must respond to the peculiarities of clinical data, which are more sensitive due to obvious legal issues and the need for them to be accurate, without deviations of any kind that could spoil the results of the research. As advised by the United States Food and Drug Administration (FDA) Guide, among other characteristics, the data to be included must be precise or correct.


Data Lake: Overcoming the Limitations of Data Warehouse


Clinical data warehouse for research
A data warehouse for clinical research serves as a repository and a place where information of adequate quality is organized and integrated, established in advance according to the objective of the research and under the protection of current data protection legislation . In this regard, international projects must be subject to different legislations.