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Objective: Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks., Design: Agent-based simulation study., Setting: Simulation environment of a small nursing home., Methods: We collected temporally detailed and spatially fine-grained location information from nursing home healthcare workers (HCWs) using sensor motes. We used these data to power an agent-based simulation of a COVID-19 outbreak using realistic time-varying estimates of infectivity and diagnostic sensitivity. Under varying community prevalence and transmissibility, we compared the mitigating effects of (i) regular screening and isolation, (ii) inter-resident contact restrictions, (iii) reduced HCW presenteeism, and (iv) modified HCW scheduling., Results: Across all configurations tested, screening every other day and isolating positive cases decreased the attack rate by an average of 27% to 0.501 on average, while contact restrictions decreased the attack rate by an average of 35%, resulting in an attack rate of only 0.240, approximately half that of screening/isolation. Combining both interventions impressively produced an attack rate of only 0.029. Halving the observed presenteeism rate led to an 18% decrease in the attack rate, but if combined with screening every 6 days, the effect of reducing presenteeism was negligible. Altering work schedules had negligible effects on the attack rate., Conclusions: Universal contact restrictions are highly effective for protecting vulnerable nursing home residents, yet adversely affect physical and mental health. In high transmission and/or high community prevalence situations, restricting inter-resident contact to groups of 4 was effective and made highly effective when paired with weekly testing.

Objective: To determine whether hand hygiene adherence is influenced by peer effects and, specifically, whether the presence and proximity of other healthcare workers has a positive effect on hand hygiene adherence., Design: An observational study using a sensor network., Setting: A 20-bed medical intensive care unit at a large university hospital., Participants: Hospital staff assigned to the medical intensive care unit., Methods: We deployed a custom-built, automated, hand hygiene monitoring system that can (1) detect whether a healthcare worker has practiced hand hygiene on entering and exiting a patient's room and (2) estimate the location of other healthcare workers with respect to each healthcare worker exiting or entering a room., Results: We identified a total of 47,694 in-room and out-of-room hand hygiene opportunities during the 10-day study period. When a worker was alone (no recent healthcare worker contacts), the observed adherence rate was 20.85% (95% confidence interval [CI], 19.78%-21.92%). In contrast, when other healthcare workers were present, observed adherence was 27.90% (95% CI, 27.48%-28.33%). This absolute increase was statistically significant (P < .01). We also found that adherence increased with the number of nearby healthcare workers but at a decreasing rate. These results were consistent at different times of day, for different measures of social context, and after controlling for possible confounding factors., Conclusions: The presence and proximity of other healthcare workers is associated with higher hand hygiene rates. Furthermore, our results also indicate that rates increase as the social environment becomes more crowded, but with diminishing marginal returns.

We captured 3-dimensional accelerometry data from the wrists of 116 healthcare professionals as they performed hand hygiene (HH). We then used these data to train a k-nearest-neighbors classifier to recognize specific aspects of HH technique (ie, fingertip scrub) and measure the duration of HH events.

Objective: To explore how hand hygiene observer scheduling influences the number of events and unique individuals observed., Design: We deployed a mobile sensor network to capture detailed movement data for 6 categories of healthcare workers over a 2-week period., Setting:   University of Iowa Hospital and Clinic medical intensive care unit (ICU)., Methods: We recorded 33,721 time-stamped healthcare worker entries to and exits from patient rooms and considered each entry or exit to be an opportunity for hand hygiene. Architectural drawings were used to derive 4 optimal line-of-sight placements for observers. We ran simulations for different observer movement schedules, all with a budget of 1 hour of total observation time. We considered observation times of 1-15, 15-30, 30, and 60 minutes per station. We stochastically generated healthcare worker hand hygiene compliance on the basis of all data and recorded the total unit compliance as it would be reported by each simulated observer., Results: Considering a 60-minute total observation period, aggregate simulated observers captured 1.7% of the average total number of opportunities per day at best and 0.5% at worst. The 1-15-minute schedule captures, on average, 16% fewer events than does the 60-minute (ie, static) schedule, but it samples 17% more unique individuals. The 1-15-minute schedule also provides the best estimator of compliance for the duration of the shift, with a mean standard deviation of 17%, compared with 23% for the 60-minute schedule., Conclusions: Our results show that observations are sensitive to different observers' schedules and suggest the importance of using data-driven approaches to schedule hand hygiene audits.

Objective: To assess healthcare personnel (HCP) perceptions regarding implementation of sensor-based electronic systems for automated hand hygiene adherence monitoring., Design: Using a mixed-methods approach, structured focus groups were designed to elicit quantitative and qualitative responses on familiarity, comfort level, and perceived impact of sensor-based hand hygiene adherence monitoring., Setting: A university hospital, a Veterans Affairs hospital, and a community hospital in the Midwest., Participants: Focus groups were homogenous by HCP type, with separate groups held for leadership, midlevel management, and frontline personnel at each hospital., Results: Overall, 89 HCP participated in 10 focus groups. Levels of familiarity and comfort with electronic oversight technology varied by HCP type; when compared with frontline HCP, those in leadership positions were significantly more familiar with ([Formula: see text]) and more comfortable with ([Formula: see text]) the technology. The most common concerns cited by participants across groups included lack of accuracy in the data produced, such as the inability of the technology to assess the situational context of hand hygiene opportunities, and the potential punitive use of data produced. Across groups, HCP had decreased tolerance for electronic collection of spatial-temporal data, describing such oversight as Big Brother., Conclusions: While substantial concerns were expressed by all types of HCP, participants' recommendations for effective implementation of electronic oversight technologies for hand hygiene monitoring included addressing accuracy issues before implementation and transparent communication with frontline HCP about the intended use of the data.

Many efforts to automatically measure hand hygiene activity depend on radio-frequency identification equipment or similar technology that can be expensive to install. We have developed a method for automatically tracking the use of hand hygiene dispensers before healthcare workers enter (or after they exit) patient rooms that is easily and quickly deployed without permanent hardware.