Meaningful recognition of nurses submitted by patients and families using interactive patient care (IPC) technology was analyzed using artificial intelligence (AI) to identify the themes and behaviors associated with extraordinary nursing.
Meaningful recognition positively impacts nursing and organizational outcomes. The use of AI techniques such as natural language processing and machine learning to identify and describe behaviors impacting patient experiences is an emerging science.
Nurse recognition comments were collected from a convenience sample of 3 organizations via an IPC inpatient platform and analyzed using the AI techniques of natural language processing, machine learning, sentiment analytics, and corollary dictionaries based on rules of linguistics.
The top theme of nursing recognition comments was courtesy and respect with the behaviors of empathy/compassion, helpfulness, kindness, attentiveness, and emotional comfort. The theme of skills/knowledge was the 2nd most common, with the behaviors of being professional, knowledgeable, keeping track, competence, dedication, and being thorough.
AI techniques for qualitative analysis of comments collected through IPC reveal nurse themes and behaviors most meaningful to patients and their family members. Nurses can advance the science of AI and guide its evolution so that nurse caring behaviors associated with establishing human connections that positively influence patient and family experience are accurately represented.
Author Affiliations: Regional Vice President/Chief Nursing Officer–West (Dr Clavelle), GetWellNetwork, Bethesda, Maryland; Executive Director (Dr Sweeney), The DAISY Foundation, Glen Ellen, California; Vice President, Research & Analytics (Dr Swartwout), O'Neil Center, GetWellNetwork, Bethesda, Maryland; Vice President (Dr Lefton), Organizational Consulting, Psychological Associates, St Louis, Missouri; Founder and CXO (Dr Guney), Narrative Dx, Austin, Texas.
The authors declare no conflicts of interest.
Correspondence: Dr Clavelle, GetWell Network, 17013 W 64th Cir, Arvada, CO 80007 (firstname.lastname@example.org).
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s web site (www.jonajournal.com).