Publications by Colleges and Departments (MSU - Bozeman)
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Item Drastic hourly changes in hand hygiene workload and performance rates: A multicenter time series analysis(Elsevier BV, 2024-09) Moore, Lori D.; Arbogast, James W.; Robbins, Greg; DiGiorgio, Megan; Parker, Albert E.Background. High hand hygiene (HH) workload is a commonly cited barrier to optimal HH performance. The objective of this study was to assess trends of HH workload as defined by HH opportunities (HHO) and performance rates over different timescales using automated HH monitoring system data. Methods. This multiyear retrospective observational study was conducted in 58 inpatient units located in 10 North American hospitals. HHO and HH rates were analyzed by time series mixed effects general additive model. Results. Median HH rates peaked at 50.0 between 6 and 7 AM with a trough of 38.2 at 5 PM. HHO over hours in a day were the highest at 184 per hospital unit per hour at 10 AM with a trough of 49.0 between 2 and 3 AM. Median rates for day and night shifts were 40.8 and 45.5, respectively (P = .078). Weekend day shift had the lowest median rate (39.4) compared with any other 12-hour shift (P < .1018). The median rates and HHO varied little across days in a week and months. Conclusions. HH workload and performance rates were negatively correlated and changed drastically over hours in a day. Hospitals should consider HH workload in the development and timely delivery of improvement interventions.Item Drastic hourly changes in hand hygiene workload and performance rates: a multicenter time series analysis(Elsevier BV, 2024-09) Moore, Lori; Arbogast, James W.; Robbins, Greg; DiGiorgio, Megan; Parker, Albert E.Background. High hand hygiene (HH) workload is a commonly cited barrier to optimal HH performance. The objective of this study was to assess trends of HH workload as defined by HH opportunities (HHO) and performance rates over different timescales using automated HH monitoring system data. Methods. This multiyear retrospective observational study was conducted in 58 inpatient units located in 10 North American hospitals. HHO and HH rates were analyzed by time series mixed effects general additive model. Results. Median HH rates peaked at 50.0 between 6 and 7 AM with a trough of 38.2 at 5 PM. HHO over hours in a day were the highest at 184 per hospital unit per hour at 10 AM with a trough of 49.0 between 2 and 3 AM. Median rates for day and night shifts were 40.8 and 45.5, respectively (P = .078). Weekend day shift had the lowest median rate (39.4) compared with any other 12-hour shift (P < .1018). The median rates and HHO varied little across days in a week and months. Conclusions. HH workload and performance rates were negatively correlated and changed drastically over hours in a day. Hospitals should consider HH workload in the development and timely delivery of improvement interventions.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 Hand hygiene product use by food employees in casual dining and quick-service restaurants(Elsevier BV, 2023-02) Manuel, Clyde S.; Robbins, Greg; Slater, Jason; Walker, Diane K.; Parker, Albert; Arbogast, James W.Hand hygiene product usage characteristics by food employees when hand sanitizers are made available are not well understood. To investigate hand hygiene product usage in casual dining and quick-service restaurants, we placed automated monitoring soap and sanitizer dispensers side-by-side at handwash sinks used by food employees in seven restaurants. Dispenses were monitored, and multiple dispenses that occurred within 60 s of each other were considered a single hand hygiene event. This resulted in 186,998 events during the study (149,779 soap only, 21 985 sanitizer only, and 15,234 regimen [defined as soap followed by sanitizer at the same sink within 60 s]) over 15,447 days of use. Soap was the most frequently used hand hygiene method by food employees in both restaurant types. Regimen use, despite being the preferred hand hygiene method by both restaurant chains, was the least used hand hygiene method. When pooled over restaurant types, the median daily usage for soap was statistically significantly highest of all methods at 23.5 dispenses per sink per day (p < 0.0001), the sanitizer median daily usage was 4.27 dispenses per sink per day, and regimen use was statistically significantly lowest of all methods at 4.02 dispenses per sink per day (p < 0.0001). When hand hygiene event types were pooled, casual dining restaurants had similar median hand hygiene event rates (11.4 dispenses per sink per day) compared to quick-service restaurants (11.9 dispenses per sink per day; p = 0.890). The number of events by sink location varied, with sinks located at a warewash station having the highest number of events (19.3 dispenses per sink per day; p < 0.0001), while sinks located by a ready-to-eat food preparation area had the lowest number of events (6.8 dispenses per sink per day; p < 0.0001). These data provide robust baseline benchmarks for future hand hygiene intervention studies in these settings.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.