The use of big data in transfusion medicine

Abstract

'Big data' refers to the huge quantities of digital information now available that describe much of human activity. The science of data management and analysis is rapidly developing to enable organisations to convert data into useful information and knowledge. Electronic health records and new developments in Pathology Informatics now support the collection of 'big laboratory and clinical data', and these digital innovations are now being applied to transfusion medicine. To use big data effectively, we must address concerns about confidentiality and the need for a change in culture and practice, remove barriers to adopting common operating systems and data standards and ensure the safe and secure storage of sensitive personal information. In the UK, the aim is to formulate a single set of data and standards for communicating test results and so enable pathology data to contribute to national datasets. In transfusion, big data has been used for benchmarking, detection of transfusion-related complications, determining patterns of blood use and definition of blood order schedules for surgery. More generally, rapidly available information can monitor compliance with key performance indicators for patient blood management and inventory management leading to better patient care and reduced use of blood. The challenges of enabling reliable systems and analysis of big data and securing funding in the restrictive financial climate are formidable, but not insurmountable. The promise is that digital information will soon improve the implementation of best practice in transfusion medicine and patient blood management globally.

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