- University of South Dakota (principal investigator: Carole South-Winter, EdD): The team developed a readmission risk algorithm for patients following heart surgery that determines who is at risk and provides insights for care. Previous risk scores did not suggest possible interventions.
- Dakota State University (principal investigator: Yong Wang, Ph.D.): Researchers looked for patterns in how rural and urban patients use various service platforms, including electronic medical records, to search for ways to decrease emergent and urgent care needs.
- University of North Dakota, Population Health (principal investigator: Arielle Seyla, Ph.D.): The team developed an algorithm to predict unplanned medical visits for diabetics, taking into account their current disease management behaviors, such as smoking, and other information, and then providing pathways to care.
- South Dakota State University (principal investigator: Surachat Ngorsuraches, Ph.D.): The team developed a patient engagement score using existing patient data. Patient engagement factors into effective management of chronic conditions, but surveys and other tracking methods are time-consuming. This score can help identify and decrease emergency department visits and hospitalizations.
- University of North Dakota, School of Medicine (principle investigator: Jeff Hostetter, M.D.): The team examined how primary care services can affect patients’ use of preventative behaviors and looked to see how that differs with a team-based approach.
- Sanford Research(principal investigator: Susan Hoover, M.D., Ph.D.): The Population Health Group created an algorithm based on current patient data to determine who needs screening for C. difficile. The goal was to decrease unnecessary testing and to develop a platform to be used to decide on ordering the test.