Classifying Care Needs and Determining Nursing Diagnoses of Older Patients With Cardiometabolic Multimorbidity.

AIM: This study aimed to gain insight into the thoughts, perceptions and needs of nurses caring for older adults with cardiometabolic multimorbidity regarding a planned machine learning-based clinical decision support system designed to classify care needs and prioritise nursing diagnoses; thereby providing a foundation for the necessity of integrating these systems into nursing practice. DESIGN: Descriptive qualitative design. METHODS: Semi-structured interviews were conducted with twenty nurses caring for older patients with cardiometabolic multimorbidity between September and October 2024. Thematic analysis was performed using an inductive approach using Max Qualitative Data Analysis (MAXQDA) software. RESULTS: Three main themes emerged: Experiences during the care process, classifying care needs and prioritising nursing diagnoses, and views on developing a decision support system. CONCLUSION: This study revealed that nurses need a holistic approach to meet the complex care needs of older patients with multimorbidity, who often carry out a routinised nursing process with standardised diagnoses. Moreover, nurses hold positive views that such a clinical decision support system could enhance care quality and reduce workload; however, they express concerns regarding the integration process. IMPACT: The findings of this study suggest that clinical decision support systems designed to classify the complex care needs of older patients with multimorbidity and support personalised nursing diagnoses offer an opportunity for holistic and quality care. By integrating these systems with authentic nursing knowledge, nurses can increase the visibility of nursing practice and personalise patient care more effectively. REPORTING METHOD: The study was reported following the Consolidated Criteria for Reporting Qualitative Research (COREQ) guideline. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Involvement.