My objective as a translational scientist is to prevent hospital-acquired complications among older critical-care patients. Specifically, my long-term goal is to prevent pressure injuries among critical-care patients through risk stratification, tailored interventions, and real-time clinical decision support. My interest stems from extensive experience as a critical care nurse. During the Iraq War troop surge of 2007, I served as a helicopter nurse and shock-trauma platoon member in Al Anbar, Iraq, where patient care concentrated mainly on survival. Keeping a patient alive was deemed a positive outcome. This focus shifted dramatically for me when a patient died from complications of a pressure injury after surviving severe trauma. I arrived home from Iraq with a passion for preventing complications causing additional suffering, specifically pressure injuries.
My dissertation work, completed in 2017, yielded new insights into the contributing factors of pressure injury development in critical care patients, and produced an innovative machine learning model for identifying critical care patients at risk for pressure injuries. Currently, I am working on refining the prototype machine learning model developed in my dissertation work with funding from the American Association of Critical Care Nurses.
In addition to my work on pressure injury risk stratification, I am a co-investigator on the administrative supplement entitled, Influence of Alzheimer’s Disease and Related Dementias among Obese and Non-obese Nursing Home Residents on Movement Patterns and Repositioning (3R01NR016001-04S1, Yap, PI). This supplement is for the parent NINR funded R01 study entitled, Preventing Pressure Ulcers with Repositioning Frequency and Precipitating Factors (R01NR016001, Yap, PI; Clinical Trial Reg: NCT02996331). The study involves the use of a tri-axial sensor system to detect long-term care residents' movement patterns; aptly leveraging my expertise in gerontology and pressure injuries with my methodological interest in informatics.
- Pressure Ulcer / Pressure Injury
- Predictive Analytics
- Machine Learning
- Delirium in the ICU setting
- Biomedical sensors
- Adult Critical Care