Organizing the chaos of healthcare with smart data discovery (sponsored by Io-Tahoe)
The struggles of managing healthcare data are unique. These systems hold an immeasurable amount of data from disparate sources and are subject to particularly stringent regulations. The evolution of technology is driving the need for modernization that provides healthcare professionals with fully integrated and complete medical records. Healthcare’s reliance on comprehendible data is critical to the mission of providing optimal and affordable care.
Unfortunately, many sources of healthcare data contain thousands of tables and tens of thousands of columns and are still maintained and managed through manual data entry. Clearsense is leveraging the power of machine learning in data discovery to identify specific fields in medical records, such as medications or allergies, in a fraction of the time previously required. Charles Boicey takes a deep dive into how the application of technology, such as machine learning, is speeding up the process of making healthcare data comprehendible, thus shrinking the discovery time from days to hours.
This session is sponsored by Io-Tahoe.
What you'll learn
- Learn how machine learning can help data discovery in medical records in hours instead of days
Charles Boicey is the chief innovation officer for Clearsense, a healthcare analytics organization specializing in bringing big data technologies to healthcare. Previously, Charles was the enterprise analytics architect for Stony Brook Medicine, where he developed the analytics infrastructure to serve the clinical, operational, quality, and research needs of the organization; was a founding member of the team that developed the Health and Human Services award-winning application NowTrending to assist in the early detection of disease outbreaks by utilizing social media feeds; and is a former president of the American Nursing Informatics Association.
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