Building a nursing diagnosis subset for mental health care: Results from an e-Delphi survey.

Purpose: To identify a consensus-based subset of NANDA-I nursing diagnoses (NDs) specifically relevant to mental health and addiction care, facilitating their integration into clinical practice and electronic health records (EHRs). Methods: A multiphase e-Delphi study was realized engaging 33 international nurses with experience in the fields of interest and in standardized nursing languages. Participants evaluated the relevance of 267 NANDA-I NDs (12th edition) using a 9-point scoring system, allowing for the immediate inclusion of 130 NDs with high consensus (median scores of 7–9). Further five uncertain NDs were included after undergoing a second Delphi round. Findings: A total of 135 NDs (50.6% of NANDA-I taxonomy) were identified as essential for mental health and addiction care. Domains such as self-perception, coping/stress tolerance, and interpersonal relationships had the highest inclusion rates, reflecting the psychosocial and cognitive complexity of care in these settings. Conversely, NDs belonging to domains like growth/development, safety/protection, and elimination/exchange were selected in a more focused way, limiting to conditions reflecting expected side effects of psychiatric medications or substance misuse or risks for other- or self-directed injuries. Conclusions: The identified subset of NDs seems to have the potential to capture the multifaceted nature of mental health and addiction nursing. This targeted approach addresses the unique needs of these populations and highlights nursing's critical role in holistic care delivery. Implications for Nursing Practice: Implementing this NDs subset into EHRs can streamline clinical reasoning, enhance interdisciplinary communication, and align interventions with patient needs. By focusing on a refined set of diagnoses, nurses can improve care quality, optimize outcomes, and contribute to evidence-based decision making in mental health and addiction care. Future research should evaluate the subset's impact on patient outcomes and healthcare efficiency.