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67 Caught Red-Handed: Results and Implications from an Innovative Hand Hygiene (HH) Audit Program

Saturday, April 2, 2011: 11:45 AM
Coronado BCD (Hilton Anatole)
Becky A. Miller, MD , Duke University Medical Center, Durham, NC
Deverick J. Anderson, MD, MPH , Duke University Medical Center, Durham, NC
Russell Staheli, MPH , Duke University Medical Center, Durham, NC
Brandon Elliott , Duke University Medical Center, Durham, NC
Charlene Carriker , Duke University Medical Center, Durham, NC
Daniel J. Sexton, MD , Duke University Medical Center, Durham, NC
Pamela Isaacs, BSN, MHA , Duke University Medical Center, Durham, NC
Rebekah W. Moehring, MD , Duke University Medical Center, Durham, NC
Luke F. Chen, MBBS, MPH , Duke University Medical Center, Durham, NC

Background:   The presence of an observer can alter HH behavior and artificially increase the rates of HH compliance (Hawthorne effect, HE).  The impact of using HH monitors as a tool to measure the rate of HH compliance between high and low performing units and between different groups of healthcare workers (HCW) has not been described. 

Objective:  1) To measure rates of HH compliance before &after the implementation of a HH audit program; 2) To quantify the magnitude of the HE on rates of HH compliance between different groups of HCWs and high and low performing units.

Methods:   We conducted cohort study in a 1019-bed tertiary-care hospital from 7/1/09 - 6/30/10.  Eight dedicated and trained monitors observed HH compliance of HCWs and ancillary staff before and after entering patient rooms.  Monitors rotated through inpatient units each day to perform direct observations (OBS) and used an electronic device to collect information including:  location, unit, type of HCW, HH before and after patient contact, and use of isolation.   Relative rates (RR) of HH compliance were calculated; chi-square test was used to test hypotheses.

Results:   56,909 HH events were observed during the study period.  Overall HH compliance increased from 5329/6357 (83.8%) in 7/09 to 5944/6484 (91.7%) by 6/10 (RR=2.12; p<0.001).  Physician (MD) compliance rates increased marginally from 698/865 (80.7%) to 640/779 (82.2%) (RR=1.10; p=0.30); however, nurse (RN) compliance rates increased significantly from 3331/4026 (82.7%) to 3706/3954 (93.7%) (RR=3.12; p<0.001) during the study period.  The overall HH compliance was higher the longer the monitor was on the ward – the overall compliance rate of the first OBS made by all monitors was 2689/3244 (82.9%) and the HH rate increased to 1703/1865 (91.3%) for later OBS (i.e. 29th OBS and beyond) (RR=2.17; p<0.001).   Units that were in the best performing tertile for HH compliance only had a slight increase in the rate of HH compliance between the first OBS recorded 910/1008 (90.3%) and the 29th OBS and beyond 621/668 (93.0%) (RR=1.42; p=0.02).  On the other hand, units in the lowest tertile for HH compliance had a greater increase in HH hygiene rates between the first OBS recorded 884/1165 (75.9%) to 401/438 (91.6%) for the 29th OBS and beyond (RR=3.45; p<0.01). (Figure)

Conclusions:    We successfully implemented a new HH auditing program.  The HE was greatest among units with the lowest rates of HH compliance, suggesting that these units are more responsive to HH audits and have a higher HE.  In addition, the difference in the rates of improvement in HH compliance between MDs and RNs suggests that MDs may be less responsive to HH audits.  Changing the culture and expectations for HH are important – HH audit data can provide specific feedback to targeted units and HCWs to improve performance.

Figure:   Rates of Hand Hygiene Compliance for the Top Third & Bottom Third Performing Wards as a Function of Number of Observations