Breast Cancer Screening
In the US, breast cancer screening demonstrates pervasive geographic, racial, and socio-economic disparities. Research has identified socioeconomic factors, education level, co-morbidities, family history, insurance coverage, beliefs, and knowledge as associated with failure to complete follow-up (examples: additional imaging, biopsy) after an abnormal screening mammogram.
Using a systems engineering model for studying and improving healthcare processes, Radiologists and systems engineers can work on clinical workflow improvements to better target women most at need for same-day readings of images (not typical in clinical workflows currently). Clinical informatics can help physicians avoid biases in screening and treatment decisions.
Clinical informatics can help target screening, surveillance or treatment to those in most need.
- Using Machine Learning to Identify, Risk Stratify, and Guide Personalized Treatment of COVID-19 Patients
- Predicting the number of Coronavirus disease 2019 (COVID-19) cases in a community using big data and simulation modeling
- Determining/Predicting COVID-19 Immunity in the Community and Healthcare Setting using large community-based data sets
- Predicting what outcomes will be most harmful if services are delayed due to COVID-19
- Understanding the impact of delays cancer surgery during the COVID-19 pandemic crisis using big data: An international, multicenter, observational cohort study
"Our graduate programs in Clinical & Health Informatics are designed to prepare graduates to fill the newest wave of jobs in the healthcare space."ELIZABETH BURNSIDE, MD, MPH