Nowadays, in order to enable future medical decision making, in the healthcare panorama there is the need of efficient Cloud-systems able to acquire and integrate Big e-health Data, coming from heterogeneous sources, through smart clinical workflows. Indeed, during the treatment at hospital, patients use medical devices generating a huge amount of data that have to be automatically stored into the Cloud storage system. In this paper, we specifically discuss an automated Machine-To-Machine clinical workflow able to manage the migration of Big e-health Data coming from medical devices to a Cloud NoSQL storage system. To validate our solution, we also present and test a real use case in which a clinical workflow is considered to manage big robotic rehabilitation datasets of the IRCCS Messina (Italy) Institute. Experiments prove the goodness of our approach in terms of data acquisition and integration.
How to enable clinical workflows to integrate big healthcare data
CARNEVALE, LORENZO;CELESTI, ANTONIO;FAZIO, MARIA;BRAMANTI, Placido;VILLARI, Massimo
2017-01-01
Abstract
Nowadays, in order to enable future medical decision making, in the healthcare panorama there is the need of efficient Cloud-systems able to acquire and integrate Big e-health Data, coming from heterogeneous sources, through smart clinical workflows. Indeed, during the treatment at hospital, patients use medical devices generating a huge amount of data that have to be automatically stored into the Cloud storage system. In this paper, we specifically discuss an automated Machine-To-Machine clinical workflow able to manage the migration of Big e-health Data coming from medical devices to a Cloud NoSQL storage system. To validate our solution, we also present and test a real use case in which a clinical workflow is considered to manage big robotic rehabilitation datasets of the IRCCS Messina (Italy) Institute. Experiments prove the goodness of our approach in terms of data acquisition and integration.Pubblicazioni consigliate
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