Kassie is a nurse informaticist with experience in bedside nursing, healthcare order set and alert build, and quality improvement in regulatory and safety. She holds a BSN in Nursing and an MSN in Nursing Informatics.
Kassie’s current focus is leading quality improvement projects at Allina Health in areas of regulatory, clinical quality, and safety.
More than 21% of people in the US report experiencing a medical error in their own care, and 33% report an error in the medical care of a relative or friend. With the effectiveness of artificial intelligence and predictive analytics growing in multiple industries, it is time to put it to work to make patients safe. Allina Health is determined to improve the safety of the patients cared for at its facilities by learning from past adverse events, identifying and addressing root causes, and automating the identification of triggers to indicate when a patient is in potential harm, or if harm may have occurred. Allina is on the path to automate patient safety surveillance through the use of triggers and apply this knowledge to develop algorithms embedded in the workflow of clinicians to warn them of potential harm in a way that allows them to intervene before harm occurs. In this session, Allina will share how it has effectively used this approach with three different patient safety challenges.