ABSTRACT Ophthalmic surgeries pose infection risks. Traditional control methods rely on manual monitoring, creating a need for more precise, data‐driven nursing models. This study evaluated the effectiveness of real‐time data analysis in improving ophthalmic infection prevention and control outcomes. This study was designed as a single‐center retrospective study. We analyzed 213 patients (2022–2024). The conventional group ( n = 105) received conventional care, while the real‐time data analytics group ( n = 108) received real‐time data‐driven infection prevention and control. Measured outcomes included infection rates, complications, visual acuity, quality of life, visual function, pain scores, costs, and cost‐effectiveness. The real‐time data analytics group showed significantly better outcomes: lower infection rates ( p = 0.032), better visual acuity ( p = 0.042), and less pain ( p = 0.024). They also had higher quality of life ( p = 0.036) and visual function scores ( p = 0.032). Direct medical costs and nursing costs were significantly lower ( p < 0.001), with the ICER was −11656.22 yuan/QALY, indicating a dominant economic result. Real‐time data analysis enables dynamic risk monitoring and precise interventions in ophthalmic nursing. This approach reduces infections, improves visual outcomes, lowers costs, and enhances cost‐effectiveness, supporting standardized quality improvement in infection prevention and control.
Chen et al. (2026) studied this question.