MACHINE LEARNING & AI: A HIMSS EVENT
LAS VEGAS, NV - MARCH 5, 2018
Elizabeth (Beth) Liles, MD, MCR, is a Kaiser Permanente Northwest primary care doctor, board certified in internal medicine, who has practiced for more than 10 years. She joined CHR in 2006 as a research fellow, became a clinical investigator in 2009, and became an investigator in 2015.
Dr. Liles’ research focuses on cancer screening. She recently concluded the FIBER Study, a study of colorectal cancer (CRC) screening methods in the KPNW health plan. She was also a principal investigator on the MY FIT Study, which compared two Fecal Immunochemical Test (FIT) protocols for detecting CRC and advanced adenomas. A recent project brought health plan stakeholders together in a day-long roundtable discussion to provide feedback on cancer screening decision aids, to help move these tools into clinical practice. Her most recent work in collaboration with Paula Carder at Portland State University will examine communication processes occurring between primary care offices and long-term care facilities for older adults.
Dr. Liles earned her medical degree from Memorial University of Newfoundland, Canada, and a master’s of clinical research degree at Oregon Health & Science University.
This session will discuss the results of a study that used machine learning to analyze EMR data to identify individuals at high risk of colorectal cancer (CRC).
The study included 9,108 controls and 900 cancer cases among Kaiser Permanente Northwest adults, ages 40-89. The model identified individuals with a tenfold higher risk of undiagnosed colorectal cancer at curable stages, and flagged colorectal tumors 180-360 days prior to usual clinical diagnosis.
The detection model can be applied to broad populations to identify people at increased risk of CRC (in particular, right-sided CRC), and enables health systems to more effectively target colonoscopy resources.
This study also demonstrated the feasibility for the model’s use in a U.S.-based HMO adult population.
This CRC detection model narrows the screening gaps associated with people who decline fecal tests and/or colonoscopies, and instead opportunistically analyzes existing demographic data and CBC tests.
Key discussion points:
One of the best ways learn is to network with your peers. This session will provide an opportunity to meet speakers and attendees who have similar privacy and security challenges and discuss solutions to those challenges.
Here's how it works:
Speakers will be stationed at different tables in the ballroom, and attendees can circulate and speak one-on-one or in groups with individual speakers. The speakers have been assigned different topics, but other topics can also be addressed.
Mingle, share and learn in this interactive environment.