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To determine whether a hospital-wide universal gloving program resulted in increased hand hygiene compliance and reduced inpatient Clostridioides difficile infection (CDI) rates.
We carried out a multiple-year before-and-after quasi-experimental quality improvement study. Gloving and hand hygiene compliance data as well as hospital-acquired infection rates were prospectively collected from January 1, 2015, to December 31, 2017, by secret monitors.
The University of Rochester Strong Memorial Hospital, an 849-bed quaternary-care teaching hospital.
All adult inpatients with the exception of patients in the obstetrics unit.
A hospital-wide universal gloving protocol was initiated on January 1, 2016.
Hand hygiene compliance increased from 68% in 2015 reaching an average of 88% by 2017 (P < .0002). A 10% increase in gloving per unit was associated with a 1.13-fold increase in the odds of hand hygiene (95% credible interval, 1.12–1.14). The rates of CDI decreased from 1.05 infections per 1,000 patient days in 2015 to 0.74 in 2017 (P < .04).
A universal gloving initiative was associated with a statistically significant increase in both gloving and hand hygiene compliance. CDI rates decreased during this intervention.
In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data.
We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management.
In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects.
We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.
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