Difference between revisions of "Journal:Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare"
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| Full article title | Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare |
|---|---|
| Journal | International Journal of Interactive Multimedia and Artificial Intelligence |
| Author(s) | González-Ferrer, Arturo; Seara, Germán; Cháfer, Joan; Mayol, Julio |
| Author affiliation(s) | Instituto de Investigación Sanitaria San Carlos, Hospital Universitario Clínico San Carlos |
| Primary contact | Email: arturogf at gmail dot com |
| Year published | 2018 |
| Volume and issue | 4 (7) |
| Page(s) | 42–46 |
| DOI | 10.9781/ijimai.2017.03.006 |
| ISSN | 1989-1660 |
| Distribution license | Creative Commons Attribution 3.0 Unported |
| Website | http://www.ijimai.org/journal/node/1626 |
| Download | http://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_6_pdf_12585.pdf (PDF) |
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Abstract
Talking 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 perspectives of research in medical informatics, powerful initiatives have emerged to help physicians make decisions, in both diagnostics and therapeutics, built from existing medical evidence (i.e., knowledge-based decision support systems). Many of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in their support; however, 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 drive healthcare organizations shifting to a value-based healthcare philosophy.
Keywords: big data, DSS, e-health, knowledge management, management systems
Introduction
Healthcare made a big step towards modernization with the emergence of the evidence-based medicine (EBM) concept in the late 1980s.[1] EBM is an approach to medical practice that aims to apply the best known scientific evidence into clinical decision-making regarding diagnosis and effective management of specific conditions and diseases. While the EBM concept was generally well received by care professionals, many factors — such as their daily work conditions or their high work load — affect putting into practice this approach in the expected way. A recent report from the Institute of Medicine in 2012 revealed that only 10 to 20 percent of the decisions clinicians make are evidence-based.[2] This fact reflects the need for medical practitioners, supported by their healthcare organizations, to make a shift in their behavior about the way clinical practice is currently carried out.
References
- ↑ Institute of Medicine (1990). Field, M.J.; Lohr, K.N.. ed. Clinical Practice Guidelines: Directions for a New Program. National Academies Press. doi:10.17226/1626.
- ↑ Moskowitz, A.; McSparron, J.; Stone, D.J.; Celi, L.A. (2015). "Preparing a New Generation of Clinicians for the Era of Big Data". Harvard Medical Student Review 2 (1): 24–27. PMC PMC4327872. PMID 25688383. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4327872.
Notes
This presentation is faithful to the original, with only a few minor changes to grammar, spelling, and presentation, including the addition of PMCID and DOI when they were missing from the original reference.
