Exploring Missed Nursing Care in the NICU: Perspectives of NICU Nurses in Saudi Arabia's Eastern Health Cluster.
Nasreen AlsalemFatima Abu RashidSaleh AljarudiMohammed I Al-BazrounRoqayah Mirza AlmatroukFatimah M AlharbiLames Al MansourNahid Baker AbuzaidPublished in: Pediatric reports (2023)
(1) Background: Missed nursing care, an omission error characterized by delayed or omitted nursing interventions, poses significant risks to patients' safety and quality of car.; (2) Methods: This is a quantitative cross-sectional study on 151 nurses who work in NICUs in three main networks in the Eastern Health Province, Saudi Arabia: Dammam ( n = 84), Qatif ( n = 53), and Jubail ( n = 14). The study uses a self-reported questionnaire (MISSCARE) and applies the 5-point Likert Scale. Statistical analysis data were analyzed using SPSS version 23.0. (3) Results: The primary reasons for missed care were shortage of nursing staff and unbalanced patient assignments. Missed nursing care negatively affects job satisfaction and was positively correlated with nurses' intentions to quit their jobs. Inadequate equipment, supplies, and breakdowns in communication between nurses and other healthcare professionals were also significant factors contributing to missed care. (4) Conclusions: Missed nursing care is associated with overwork, nursing shortages, and lower job satisfaction, impacting the quality of care provided in the NICU. Improving working conditions, nurse staffing, and patient assignment planning should be prioritized to address this issue effectively.
Keyphrases
- healthcare
- mental health
- quality improvement
- saudi arabia
- south africa
- preterm infants
- end stage renal disease
- palliative care
- newly diagnosed
- public health
- chronic kidney disease
- health information
- big data
- physical activity
- ejection fraction
- social support
- cross sectional
- high resolution
- prognostic factors
- machine learning
- psychometric properties
- social media
- smoking cessation
- patient reported outcomes
- pain management
- deep learning
- climate change
- replacement therapy
- patient satisfaction