The project will focus on clinical decision support, an intervention approach used in the clinical setting to improve patient outcomes, and the gap in understanding of the optimal display format and its impact on palliative care end-of-life patient outcomes. Often the care received by hospitalized end-of-life patients is not always the care that is desired, and this study aims to improve patient outcomes by ensuring the clinical decision support used by nurses provides efficient and effective support for care decisions.

Principal investigators include Gail Keenan PhD, RN, FAAN, the R. Murray and Annabel Davis Jenks Endowed Professor and chair of the Department of Family, Community and Health Systems Science; Yingwei Yao, PhD, a research associate professor; and Karen Dunn Lopez, PhD, MPH, RN, an associate professor with the University of Iowa College of Nursing.

What makes this project unique is the focus on the display format of the clinical decision support. Effective use requires the format and the content of the clinical decision support match the nurse’s ability to process and apply the information appropriately and in a timely manner.

Through the electronic health record system, a well-designed clinical decision support can interpret the evidence and present suggestions about care personalized to the patient’s profile.

This project builds on a pilot simulation study of 60 nurses from a large Midwest metropolitan area that examined the impact of interactive clinical decision support prototypes using four different formats of the standard care planning features. The formats included the control, features with text, features with text and table, and features with graph. Patient outcomes were significantly higher for those who received messages in any of the three formats that included text, table and graph compared to the control group.

This grant will support a national stratified randomized control trial to compare the effects of the three clinical decision support prototypes in a follow-up simulation study that will include a tailored condition based on the nurse’s graph literacy score. The researchers hypothesize that a tailored format will result in a faster decision time for nurses and adoption of interventions associated with better outcomes for hospitalized end-of-life patients with cancer and other chronic illnesses.