Using artificial intelligence/machine learning algorithms to determine risk of opioid overdose
Debra Heinrich
College of Nursing Education, USA
: J Nurs Patient Care
Abstract
Federal, state and local officials and health care professionals are researching, developing and implementing initiatives targeted at reducing opioid use, abuse, overdose and related-deaths. These efforts have focused on prescribing; developing guidelines for opioid prescribing and expanding use of Prescription Drug Monitoring Programs to reduce the prescribing of opioid medications. Yet the death rate continues to climb (~64,000 in 2017, an increase from 2016). With approximately 30% rehabilitation costs being covered by Medicaid programs nationally and the promise that machine learning and predictive modeling bring to healthcare, the focus of the data analytics needs to shift from describing the problem to treating it. Join leaders and partners from DXC Technology as they make the case and address the following; Data sharing difficulties, Problems operationalizing the findings of data analysis, remaining compliant with current rules and regulations regarding privacy and security and Current available solutions to effectively address the changing opioid crisis.
Biography
Debra Heinrich (M.A., M.Ed. ’84) was already working as a nurse when she learned about Teachers College’s Nursing Education program. “A colleague raved about how great the program was and how it would help my career,” says Debra. “The most intriguing aspect was the program’s emphasis on research, so I enrolled. TC was definitely ahead of its time in the early 1980s as far as research was concerned.”
E-mail: dsh2014@tc.columbia.edu
