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.
Abstract Objective: To measure the impact of an automated hand hygiene monitoring system (AHHMS) and an intervention program of complementary strategies on hand hygiene (HH) performance in both acute-care and long-term care (LTC) units. Design: Prospective, nonrandomized, before-and-after intervention study. Setting: Single Veterans Affairs Medical Center (VAMC), with 2 acute-care units and 6 LTC units. Methods: An AHHMS that provides group HH performance rates was implemented on 8 units at a VAMC from March 2021 through April 2022. After a 4-week baseline period and 2.5-week washout period, the 52-week intervention period included multiple evidence-based components designed to improve HH compliance. Unit HH performance rates were expressed as the number of dispenses (events) divided by the number of patient room entries and exits (opportunities) × 100. Statistical analysis was performed with a Poisson general additive mixed model. Results: During the 4-week baseline period, the median HH performance rate was 18.6 (95% CI, 16.5–21.0) for all 8 units. During the intervention period, the median HH rate increased to 21.6 (95% CI, 19.1–24.4; P < .0001), and during the last 4 weeks of the intervention period (exactly 1 year after baseline), the 8 units exhibited a median HH rate of 25.1 (95% CI, 22.2–28.4; P < .0001). The median HH rate increased from 17.5 to 20.0 ( P < .0001) in LTC units and from 22.9 to 27.2 ( P < .0001) in acute-care units. Conclusions: The intervention was associated with increased HH performance rates for all units. The performance of acute-care units was consistently higher than LTC units, which have more visitors and more mobile veterans.
Background: Hand hygiene (HH) has long been a focus in the prevention of healthcare-associated infections. The limitations of direct observation, including small sample size (often 20–100 observations per month) and the Hawthorne effect, have cast doubt on the accuracy of reported compliance rates. As a result, hospitals are exploring the use of automated HH monitoring systems (AHHMS) to overcome the limitations of direct observation and to provide a more robust and realistic estimation of HH behaviors. Methods: Data analyzed in this study were captured utilizing a group-based AHHMS installed in a number of North American hospitals. Emergency departments, overflow units, and units with <1 year of data were excluded from the study. The final analysis included data from 58 inpatient units in 10 hospitals. Alcohol-based hand rub and soap dispenses HH events (HHEs) and room entries and exits (HH opportunities (HHOs) were used to calculate unit-level compliance rates. Statistical analysis was performed on the annual number of dispenses and opportunities using a mixed effects Poisson regression with random effects for facility, unit, and year, and fixed effects for unit type. Interactions were not included in the model based on interaction plots and significance tests. Poisson assumptions were verified with Pearson residual plots. Results: Over the study period, 222.7 million HHOs and 99 million HHEs were captured in the data set. There were an average of 18.7 beds per unit. The average number of HHOs per unit per day was 3,528, and the average number of HHEs per unit per day was 1,572. The overall median compliance rate was 35.2 (95% CI, 31.5%–39.3%). Unit-to-unit comparisons revealed some significant differences: compliance rates for medical-surgical units were 12.6% higher than for intensive care units ( P < .0001). Conclusions: This is the largest HH data set ever reported. The results illustrate the magnitude of HHOs captured (3,528 per unit per day) by an AHHMS compared to that possible through direct observation. It has been previously suggested that direct observation samples between 0.5% to 1.7% of all HHOs. In healthcare, it is unprecedented for a patient safety activity that occurs as frequently as HH to not be accurately monitored and reported, especially with HH compliance as low as it is in this multiyear, multicenter study. Furthermore, hospitals relying on direct observation alone are likely insufficiently allocating and deploying valuable resources for improvement efforts based on the scant information obtained. AHHMSs have the potential to introduce a new era in HH improvement. Funding: GOJO Industries, Inc., provided support for this study. Disclosures: Lori D. Moore and James W. Arbogast report salary from GOJO.
Abstract 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.
