TY - JOUR
T1 - Monitoring hand hygiene via human observers
T2 - How should we be sampling?
AU - Fries, Jason
AU - Segre, Alberto M.
AU - Thomas, Geb
AU - Herman, Ted
AU - Ellingson, Katherine
AU - Polgreen, Philip M.
PY - 2012/7
Y1 - 2012/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84863187335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863187335&partnerID=8YFLogxK
U2 - 10.1086/666346
DO - 10.1086/666346
M3 - Article
C2 - 22669230
AN - SCOPUS:84863187335
SN - 0899-823X
VL - 33
SP - 689
EP - 695
JO - Infection Control and Hospital Epidemiology
JF - Infection Control and Hospital Epidemiology
IS - 7
ER -