Modern healthcare technologies have introduced invasive procedures for treating patients and improving their recovery rate. However, healthcare-associated infections (HAIs) still occur, especially during surgery. They concern the healthcare professionals since they compromise their patients’ safety and quality of care. They affect more than 1.7 million patients annually and claim 98,000 lives of the victims (Haque et al., 2018). HAIs, such as urinary tract infection (UTI), infect individuals 48hours after admission. The united states have invested heavily in HAIs eradication though they still affect 11 million citizens, where 36 % suffer from UTIs (Parker et al., 2017). Therefore, the infections might keep on rising if the country fails to apply more effective interventions.
One type of UTI is Catheter-associated urinary tract infections (CAUTIs), resulting from indwelling catheters. It accounts for 40 % of global HAIs cases and 97 % of UTIs in the ICUs (Flores-Mireles et al., 2019; Parker et al., 2017). Moreover, the United States uses more than 30 million catheters, which increases vulnerability to CAUTIs if inserted inappropriately or healthcare providers delay their removal. The inferences imply that CAUTI is a national and global healthcare issue that affects patients’ wellbeing and lowers hospitals’ reputation regarding their safety (Krein et al., 2013; Sihra et al., 2018). The infections can be alleviated via protocol-led and practitioner-led interventions.
The project wishes to prove that CAUTIs can be controlled in the critical care units after hospitals understand the factors leading to the infections. Healthcare stakeholders can rely on dashboards to track and regulate all HAI before rolling out education programs on nursing teams (Wahi & Dukach, 2019). The case surveillance form (CSF) can facilitate the analysis of threat elements like incontinence, bedridden,and immunocompromised patients. The researchers should also analyze catheter utilization rates, patient stay, and infection rates in their analysis (Wanat et al., 2020). However, they should ensure they collect real-time data to improve the reliability of their inferences.
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