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Monitoring Hand Hygiene via Human Observers: How Should We Be Sampling?

Published online by Cambridge University Press:  02 January 2015

Jason Fries ,
Alberto M. Segre ,
Geb Thomas ,
Ted Herman ,
Katherine Ellingson  and
Philip M. Polgreen
Jason Fries
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Alberto M. Segre
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Geb Thomas
Affiliation:
Department of Industrial and Mechanical Engineering, University of Iowa, Iowa City, Iowa
Ted Herman
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Katherine Ellingson
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Philip M. Polgreen*
Affiliation:
Carver College of Medicine, University of Iowa, Iowa City, Iowa College of Public Health, University of Iowa, Iowa City, Iowa
*
Division of Infectious Diseases, Department of Internal Medicine, University of Iowa, Carver College of Medicine, 200 Hawkins Drive, Iowa City, IA 52242 ([email protected])
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Abstract

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.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 33 , Issue 7 , July 2012 , pp. 689 - 695
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012

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