Leveraging Information Technology to Guide High Tech High Touch Care (LIGHT2)
LIGHT2 (Leveraging Information Technology for Hi-Tech and Hi-Touch Care) is a federally funded project using 24 Nurse Care Managers to manage the health of 10,000 Medicare and Medicaid patients. Its goal is to reduce exacerbations of chronic diseases, which would improve health outcomes while lowering healthcare costs. Analytics support (“Hi-Tech”) support for the Nurse Care Managers (“Hi-Touch”) has been used to classify patients by past utilization and costs, but these are imperfect predictors of future exacerbations and increasing utilization. Mining the large available health histories of these patients, along with demographic and other data, reveals some expected and some surprising factors which are associated with high risk of increased healthcare utilization. Clinical implementation of these prediction rules will validate their usefulness.
Mohammed Khalilia (محمد عبد الستار قاسم) is a researcher, computer scientist, and data scientist with a PhD in Computer Science from the University of Missouri. Following his doctorate, he joined Georgia Tech’s Computational Science and Engineering school and Emory University as a Postdoctoral Fellow, where his research spanned predictive modeling, relational cluster analysis, and health and nursing informatics.
He then spent nearly five years at Amazon, working across Amazon Web Services (AWS) and Amazon Studios in natural language processing (NLP), speech synthesis, and computer vision. In 2018, he was part of the team that launched Comprehend Medical, Amazon’s NLP service for clinical and biomedical text. At Qualtrics, he developed the company’s first fine-tuned large language model, trained synthetic sampling model, and worked on conversational machine learning, and active learning.
He is also an adjunct professor at Birzeit University, where he teaches NLP courses for doctoral students.