Hospital hand hygiene compliance improves with increased monitoring and immediate feedback.

BACKGROUND: Health care-associated infections are serious complications impacting 2 million patients and accounting for approximately 100,000 deaths per year. In the present study, we evaluated the effectiveness of a new hand hygiene monitoring program (HHMP) and measured the sustainability of this effectiveness over a 1-year period. METHODS: The HHMP consisted of 4 key components: extensive education, conspicuous and visible monitors, immediate feedback concerning compliance to health care workers, and real-time data dissemination to leadership. The HHMP was implemented in 2 hospital care units. Two different, but similar, departments served as controls, and hand hygiene compliance was monitored via the "secret shopper" technique. All 4 departments were followed for 12 months. RESULTS: Both experimental departments showed statistically significant increases in hand hygiene compliance. Experimental department 1 increased compliance from 49% to an average of 90%, and experimental department 2 increased compliance from 60% to an average of 96%. Both experimental departments were able to sustain these results for at least 6 months. Compliance rates were significantly higher in the experimental departments compared with the control departments. No significant changes were seen in the control departments. CONCLUSIONS: These finding suggest that continuous monitoring by salient observers and immediate feedback are critical to the success of hand hygiene programs.

Combinatorial neural codes from a mathematical coding theory perspective.

Shannon's seminal 1948 work gave rise to two distinct areas of research: information theory and mathematical coding theory. While information theory has had a strong influence on theoretical neuroscience, ideas from mathematical coding theory have received considerably less attention. Here we take a new look at combinatorial neural codes from a mathematical coding theory perspective, examining the error correction capabilities of familiar receptive field codes (RF codes). We find, perhaps surprisingly, that the high levels of redundancy present in these codes do not support accurate error correction, although the error-correcting performance of receptive field codes catches up to that of random comparison codes when a small tolerance to error is introduced. However, receptive field codes are good at reflecting distances between represented stimuli, while the random comparison codes are not. We suggest that a compromise in error-correcting capability may be a necessary price to pay for a neural code whose structure serves not only error correction, but must also reflect relationships between stimuli.