Insights from advanced analytics at the Veterans Health Administration.
By S. Fihn, Joseph Francis, Carolyn Clancy, Christopher Nielson, Karin Nelson, J. Rumsfeld, Theresa A. Cullen, Jack Bates, Gail Graham. Health Affairs, 2014
337 citations recorded by the source corpus.
Subfields: General Health Professions, Health Information Management
View Paper: https://www.semanticscholar.org/paper/22308a80bcecc7a0cfe7e0198e54d9606760a960
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In General Health Professions
In Health Information Management
Citations in the corpus
- Exploring the path to big data analytics success in healthcare (2017, background): “Compared to other industries, such as retailing and banking industries, however, healthcare industry lags behind in taking advantage of thoughtful analytical tools and methods (Ferranti, Langman, Tanaka, McCall, & Ahmad, 2010; Fihn et al., 2014).”
- Early prediction of circulatory failure in the intensive care unit using machine learning (2020, methodology): “Such a model may perform well on the previously observed data, but will fail to generalize to scenarios with differing clinician behavior 36 .”
- Performance of a Natural Language Processing (NLP) Tool to Extract Pulmonary Function Test (PFT) Reports from Structured and Semistructured Veteran Affairs (VA) Data (2016, methodology): “Documents were extracted from the Veterans Informatics, Information, and Computing Infrastructure, which provides centralized access to the VA CDW in a secure computing environment.(4) This study was approved by the University of Utah Institutional Review Board”
- An introduction to implementation science for the non-specialist (2015, background): “To date, the bulk of attention to learning healthcare systems has focused on the acquisition and analysis of large datasets gathered from real-world clinical practices in as close to real-time as possible [73].”