The Impact of Real‐Time Data Analytics on Infection Prevention and Control Practices in Ophthalmology: A Nursing Perspective on Patient Outcomes and Cost‐Effectiveness

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.