02020nas a2200253   4500000000100000000000100001008004100002260001200043653002500055653000800080653002200088653001300110653001300123100002900136700001900165700001800184700001600202245010800218856009500326300001000421490000600431520131500437022001401752       2018                            d  c03/201810aKnowledge Management10aDSS10aManagemet Systems10aBig Data10ae-health1 aArturo González-Ferrer1 aGermán Seara1 aJoan Cháfer1 aJulio Mayol00aGenerating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_6_pdf_12585.pdf  a42-460 v43 aTalking about Big Data in healthcare we usually refer to how to use data collected from current electronic medical records, either structured or unstructured, to answer clinically relevant questions. This operation is typically carried out by means of analytics tools (e.g. machine learning) or by extracting relevant data from patient summaries through natural language processing techniques. From other perspective of research in medical informatics, powerful initiatives have emerged to help physicians taking decisions, in both diagnostics and therapeutics, built from the existing medical evidence (i.e. knowledge-based decision support systems). Much of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in its support, but, technology is slowly overcoming interoperability and integration issues. Beyond the point-of-care decision support these tools can provide, the data generated when using them, even in controlled trials, could be used to further analyze facts that are traditionally ignored in the current clinical practice. In this paper, we reflect on the technologies available to make the leap and how they could help driving healthcare organizations shifting to a value-based healthcare philosophy.  a1989-1660