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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.
The study was conducted in 58 adult and pediatric inpatient units located in 10 hospitals.
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.
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).
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.
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.
Nickel nanoparticles (NNPs) synthesized by a modified polyol method using ethylene glycol as a reducing agent, palladium chloride as a nucleating agent, and polyvinylpyrrolidone (PVP) as a protective agent were investigated as a potential magnetic adsorbent for the purification of hexahistidine-tagged (His6-tagged) recombinant proteins. The synthesis resulted in nanoparticles having an average diameter of 68 ± 28 nm. The x-ray diffraction pattern confirmed the presence of nickel metal, as well as the presence of unreacted nickel (II) hydroxide Ni(OH)2. Magnetic characterization showed that a magnetization saturation of 39.3 electromagnetic unit (emu)/g at 20,000 Oersted (Oe) was reached rapidly and that the material exhibited ferromagnetic behavior. Protein purification results showed that the synthesized NNPs were highly selective for binding to a His6-tagged recombinant protein single-domain antibody ToxA5.1. In addition, NNPs were used for four adsorption cycles without significant binding capacity losses. These particles have shown great potential such as being easily synthesized, cost-effective, and highly selective magnetic adsorbents for the purification of His6-tagged recombinant proteins.
With growing access to health information, people who suffer from depression are increasingly eager to play an active role in the management of their symptoms. The goal of self-management is to support patients in monitoring and managing their symptoms and provide them with additional resources to promote recovery, enhance quality of life, and prevent relapse. For clinicians, self-management holds promise for improving practice efficiency and efficacy by helping patients maximize their improvement outside of treatment sessions. Self-Management of Depression is written for clinicians who wish to empower their patients to take more active steps to manage depression. Chapters cover care management, self-assessment, exercise, self-help books and computer programs, meditation, and peer-support groups and strategies for how to incorporate self-management into a treatment plan are described. Reproducible handouts to support patients are also available online. This book is relevant to clinical psychologists, psychiatrists, psychiatric nurses, social workers and primary care physicians.