Jingjing Shang
Professor of Nursing
Columbia University Medical Center
Abstract: Predictive risk models leveraging AI and machine learning are transforming infection prevention and quality of care in home healthcare (HHC). This keynote will delve into their development and impact, drawing from three pivotal studies. A model developed from 112,788 HHC admissions using stepwise logistic regression accurately identified high-risk patients, aiding in care planning and infection control. A systematic review of machine learning models in post-acute care highlighted advanced algorithms and multimodal data sources but stressed the need for better validation and inclusion of socio-environmental factors. Additionally, a qualitative study revealed HHC nurses found the models useful but needed contextual information and actionable guidance. Integrating these predictive models can enhance infection prevention, improve care quality, and incorporate clinician insights, ultimately leading to better patient outcomes and more efficient care delivery.
Short Bio: Jingjing Shang, PhD, RN, FAAN is a professor and health services researcher at Columbia University School of Nursing. She is the Principal Investigator of multiple federal and organization-funded grants in which she studies infection prevention and control, patient safety, and quality of care in home health care. Her other research interests include home healthcare policy, quality of care, predictive risk modeling, and the nursing workforce. She has multiple high-quality publications and has presented at international and national conferences. Her publication "A Predictive Risk Model for Infection-Related Hospitalization among Home Healthcare Patients" won the Journal of Health Quality's 2020 Impact Article of the Year Award. She served on NIH, AHRQ, CDC, and ACS grant review panels. She was inducted into the Sigma Theta Tau International Nursing Researcher Hall of Fame in 2022.