Implementing Best Practices Using Appreciative Inquiry

Saturday, 29 July 2017: 9:30 AM

Joyce J. Fitzpatrick, PhD, RN, FAAN
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
Reynaldo R. Rivera, DNP
Department of Nursing, NewYork-Presbyterian Hospital, New York, NY, USA
Bernadette Khan, MSN
Department of Nursing, NewYork-Presbyterian Hospital/Lower Manhattan, New York, NY, USA
Wilhelmina M. Manzano, MA
Department of Nursing, New York-Presbyterian Hospital, New York, NY, USA

Appreciative Inquiry (AI) is a model of organizational behavior that has recently been applied in a number of health care organizations to improve care. AI is focused on discovering the best that is, and designing a future that could be even better. AI includes 4 cycles: Discovery of the best of what is; Dream what might be; Design what could be; and Destiny: what will be. One important component of the AI experience is that it relies on the collective design of a desired future state that is compelling, thus lifting the conversation to avoid problems and complaints and focus on positive possibilities. We implemented AI by taking the best evidence from the literature related to key areas central to enhancing RN to RN interaction: the bedside shift report and RN floating. We asked clinical nurses providing direct care to describe the evidence and best practices related to each of these areas. The creativity that was generated for each of these challenging situations was impressive, and best practices have been described and implemented on the units. Responses related to “floating” included providing a “buddy system” for nurses who have floated, designing a concise unit orientation system for these nurses, and making sure there is a “check out” to ask the floating nurses “what worked” for you, so as to improve the experience of the next float nurse. Responses related to best practices in bedside shift report including making certain patients are aware of all that is being done to keep them safe, conducting an environmental scan to ensure patient safety, directly communicating to the patient the strengths of the oncoming nurse, thus assuring the patient and the nurse that there is trust in the system. As part of the AI model there is a “check out” after each implementation session. Nurses who have participated in the AI sessions have described the experience as “inspiring”, “empowering” “energizing” and “validating”. Other positive terms for the experience as well as the developmental process will be shared in this presentation. Future plans are to link this work to both nurse and patient satisfaction.