<![CDATA[Background: The implementation of the Indonesian Nursing Diagnosis Standards (IDHS) in clinical practice continues to encounter obstacles, including limited time for assessment, documentation burden, and inconsistency in diagnostic interpretation among nurses. Advancements in artificial intelligence (AI) offer opportunities to design clinical decision support systems that may enhance the accuracy, uniformity, and efficiency of nursing diagnoses. Objective: This study aimed to explore nurses’ experiences, challenges, and expectations regarding the use of IDHS and to assess the initial feasibility of developing an AI-based system to support standardized nursing diagnoses. Methods: A descriptive qualitative design was employed using in-depth interviews with five nurses from diverse clinical settings, including emergency, critical care, inpatient, outpatient, and community services. Thematic analysis was conducted to identify perceptions and needs related to IDHS and AI. Content validity of the preliminary system design was evaluated by three expert validators using the Item-Level Content Validity Index (I-CVI) and the Scale-Level Content Validity Index/Average (S-CVI/Ave). Results: Five major themes emerged: (1) varied experiences in formulating nursing diagnoses, (2) inconsistent use of IDHS in practice, (3) challenges related to documentation and diagnostic interpretation, (4) positive attitudes toward AI integration in clinical workflows, and (5) the need for features such as automated diagnostic suggestions, clarification of clinical criteria, and integration with electronic medical records. Content validity testing demonstrated strong agreement among experts, with I-CVI values ranging from 0.80 to 1.00 and an S-CVI/Ave of 0.92. Conclusion: The study indicates that integrating AI into the IDHS-based diagnostic process holds substantial potential to improve diagnostic quality, standardization, and clinical efficiency. The content validity results support further development and prototype testing in subsequent research phases]]>
