Scholarly Work - Center for Biofilm Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/9335
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Item Sample sizes for estimating the sensitivity of a monitoring system that generates repeated binary outcomes with autocorrelation(Sage Publications, 2023-11) Parker, Albert E.; Arbogast, James W.Sample size formulas are provided to determine how many events and how many patient care units are needed to estimate the sensitivity of a monitoring system. The monitoring systems we consider generate time series binary data that are autocorrelated and clustered by patient care units. Our application of interest is an automated hand hygiene monitoring system that assesses whether healthcare workers perform hand hygiene when they should. We apply an autoregressive order 1 mixed effects logistic regression model to determine sample sizes that allow the sensitivity of the monitoring system to be estimated at a specified confidence level and margin of error. This model overcomes a major limitation of simpler approaches that fail to provide confidence intervals with the specified levels of confidence when the sensitivity of the monitoring system is above 90%.Item The impact of automated hand hygiene monitoring with and without complementary improvement strategies on performance rates(Cambridge University Press, 2022-08) Arbogast, James W.; Moore, Lori D.; DiGiorgio, Megan; Robbins, Greg; Clark, Tracy L.; Thompson, Maria F.; Wagner, Pamela T.; Boyce, John M.; Parker, Albert E.Objective: To determine how engagement of the hospital and/or vendor with performance improvement strategies combined with an automated hand hygiene monitoring system (AHHMS) influence hand hygiene (HH) performance rates. Design: Prospective, before-and-after, controlled observational study. Setting: The study was conducted in 58 adult and pediatric inpatient units located in 10 hospitals. Methods: HH performance rates were estimated using an AHHMS. Rates were expressed as the number of soap and alcohol based hand rub portions dispensed divided by the number of room entries and exits. Each hospital self-assigned to one of the following intervention groups: AHHMS alone (control group), AHHMS plus clinician-based vendor support (vendor-only group), AHHMS plus hospital led unit-based initiatives (hospital-only group), or AHHMS plus clinician-based vendor support and hospital-led unit-based initiatives (vendor-plus-hospital group). Each hospital unit produced 1–2 months of baseline HH performance data immediately after AHHMS installation before implementing initiatives. Results: Hospital units in the vendor-plus-hospital group had a statistically significant increase of at least 46% in HH performance compared with units in the other 3 groups (P ≤ .006). Units in the hospital only group achieved a 1.3% increase in HH performance compared with units that had AHHMS alone (P = .950). Units with AHHMS plus other initiatives each had a larger change in HH performance rates over their baseline than those in the AHHMS-alone group (P < 0.001). Conclusions: AHHMS combined with clinician-based vendor support and hospital-led unit-based initiatives resulted in the greatest improvements in HH performance. These results illustrate the value of a collaborative partnership between the hospital and the AHHMS vendor.