Developing a Mental Illness Nursing Diagnoses subSET: study protocol for a e-delphi survey (MINDSET study)

introduction: nursing care, despite constituting a significant portion of healthcare costs, often remains overlooked in healthcare data systems, which primarily focus on medical data. Incorporating standardized nursing language (SNL) into electronic health records has shown promise in predicting outcomes across various clinical settings. however, nurses' unfamiliarity with standardized terminologies poses a significant barrier to their implementation. NANDA-International (NANDA-I) nursing diagnoses (NDs) offer a standardized framework, yet their application in mental health (MH) settings remains underexplored. objectives: the MINDSET study aims to establish a consensus among mental health nurses to develop a subset of nursing diagnoses tailored for MH and addiction EHRs. methods: a multi-phase e-delphi study will involve mental health nurses experienced in NANDA-I NDs from various countries. through successive rounds of surveys, experts will assess the relevance of NDs in MH settings, with consensus determining the final subset. results: the expected outcome is a concise subset of nursing diagnoses agreed upon by experts, facilitating their integration into clinical practice. this subset may offer nurses a manageable set of diagnoses closely aligned with MH contexts, enhancing their applicability and utility in daily care. subsequent research could explore the prevalence of these diagnoses in MH settings and their associations with patient outcomes. Impact: the development of a tailored subset of nursing diagnoses holds potential to enhance nursing practice in MH settings, enabling more effective assessment and intervention strategies, ultimately improving patient outcomes.