scholarly journals Observational Bias Within Hospital-Wide Hand Hygiene Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s333-s333
Author(s):  
Amy Marques ◽  
Robert Tucker ◽  
Michael Klompas

Background: Hand hygiene (HH) is critical to prevent hospital-acquired infections. Running a successful HH program requires valid and accurate HH data to monitor the status and progress of HH improvement efforts. HH data are frequently subject to variable forms of bias, for which considerations must be made to enhance the validity of HH data. Objective: We assessed the extent to which observers may be prone to report more favorable HH rates when observing healthcare workers from the same professional group versus members of other job categories. Methods: We analyzed HH data from 48,543 electronically collected observations conducted by frontline healthcare workers in a 793-bed acute-care hospital from January 1, 2019, through July 31, 2019. All auditors received training on HH observations and proper use of the data collection application. Compliance data were sorted into peer versus nonpeer observations by profession. We compared HH compliance rates for members of each professional group when monitoring peers versus nonpeers. We further stratified results by ancillary professions (central transport, unit associates, food services, pharmacy, phlebotomy, rehabilitation services, and respiratory therapy) versus nonancillary professions (doctors, nurses, physician assistants, patient care assistants). Results: Of 12,488 ancillary observations, 7,184 (57.5%) were peer observations and 36,055 were nonancillary observations, of which 15,942 (44.2%) were peer observations. The percentage of peer-to-peer observations versus nonpeer observations varied by profession, ranging from 96% of central transport workers and 91% of environmental services observations to 21% of patient care assistants and 34% of physician’s assistants. Average compliance rates for peer versus nonpeer observations in ancillary groups were 98% (95% CI, 98.7%–99.2%) versus 83% (95% CI, 82.5%–84.5%). Average compliance rates nonancillary groups were 92% (95% CI, 92.0%–92.8%) for peers versus 88% (95% CI, 87.8%–88.7%) for nonpeers (Table 1). Conclusions: We documented a propensity for some categories of healthcare workers to record discrepant rates of HH compliance when observing members of the same peer group versus others. This effect was more pronounced amongst ancillary versus nonancillary services. This study adds to the literature of potential sources of bias in HH monitoring programs. Operational changes in HH program data collection may be warranted to try to mitigate these biases such as increasing the frequency of validation exercises conducted by nonaffiliated observers, weighting peer versus nonpeer observations differently, or switching to automated electronic monitoring systems.Funding: NoneDisclosures: None

Author(s):  
Amy E. Badwaik ◽  
Robert P. Tucker ◽  
Peggy Leung ◽  
Michael Klompas

Abstract We assessed the extent to which healthcare workers report more favorable hand hygiene rates when observing members of their own professional group versus other groups’ observations of them. Healthcare workers consistently reported higher compliance rates for their own group compared to others’ observations of them (97 vs 92%; P ≤ .001).


2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S425-S426 ◽  
Author(s):  
Maxime-Antoine Tremblay ◽  
Mona Abou Sader ◽  
Yves Longtin

Abstract Background The current hand hygiene (HH) auditing and feedback strategy include anonymized data collection using direct observation and feedback of aggregated data. We aimed to evaluate whether an anonymous (without wearable device) HH electronic monitoring system (EMS) could detect patterns associated with individual healthcare workers (HCWs) and estimate their relative HH performance. Methods Observational study of HH compliance via an EMS in 10 rooms in a tertiary care hospital. The EMS measures HH product dispenser activation (an indicator of HH events) as well as entries and exits from patient rooms (a surrogate of HH opportunities). HH rates were obtained by dividing the number of HH events by the number of opportunities. HH rates were aggregated at room-shift level (i.e., an 8-hour period for a single room). For each room-shift, the HH rate was converted to a Z score, which was then associated with the individual HCW assigned to that room-shift. The relative HH performance of individual HCWs was estimated by comparing the mean Z scores of each HCW with the rest of the group by the Student T-test, with a level of significance set at P < 0.001 after adjustment by Bonferroni’s correction. To investigate whether any association could be due to chance, we looked into the potential association between average Z scores and calendar days, as a counterexample. Results Over a 100-day period, there were 45 775 HH events and 136 821 opportunities (global compliance, 33%). Schedules were available for 2980 room-shifts. Fifty-four individual HCWs took part in at least one room-shift (average per HCW, 52 room-shifts; range 1–140). Eight HCWs (15%) had a mean Z score significantly above the group average (Figure 1, green boxes; mean Z score 0.71; range, 0.52 to 0.86; P < 0.001), whereas 9 HCWs (17%) had a significantly inferior Z score (Figure 1, red boxes; mean Z score -0.47, range -0.58 to -0.31, P < 0.001). In contrast, there was no significant difference in Z scores between calendar days (Figure 2; p >0.001). Conclusion Cross-linking a high-volume HH database with HCW schedules identified a significant association between individual HCWs and HH compliance in the rooms to which they were assigned. If confirmed in further studies, anonymous EMS could be used to provide HCWs with personalized relative HH compliance feedback. Disclosures All authors: No reported disclosures.


Author(s):  
Nai-Chung Chang ◽  
Michael Jones ◽  
Heather Schacht Reisinger ◽  
Marin L. Schweizer ◽  
Elizabeth Chrischilles ◽  
...  

Abstract Objective: To determine whether the order in which healthcare workers perform patient care tasks affects hand hygiene compliance. Design: For this retrospective analysis of data collected during the Strategies to Reduce Transmission of Antimicrobial Resistant Bacteria in Intensive Care Units (STAR*ICU) study, we linked consecutive tasks healthcare workers performed into care sequences and identified task transitions: 2 consecutive task sequences and the intervening hand hygiene opportunity. We compared hand hygiene compliance rates and used multiple logistic regression to determine the adjusted odds for healthcare workers (HCWs) transitioning in a direction that increased or decreased the risk to patients if healthcare workers did not perform hand hygiene before the task and for HCWs contaminating their hands. Setting: The study was conducted in 17 adult surgical, medical, and medical-surgical intensive care units. Participants: HCWs in the STAR*ICU study units. Results: HCWs moved from cleaner to dirtier tasks during 5,303 transitions (34.7%) and from dirtier to cleaner tasks during 10,000 transitions (65.4%). Physicians (odds ratio [OR]: 1.50; P < .0001) and other HCWs (OR, 2.15; P < .0001) were more likely than nurses to move from dirtier to cleaner tasks. Glove use was associated with moving from dirtier to cleaner tasks (OR, 1.22; P < .0001). Hand hygiene compliance was lower when HCWs transitioned from dirtier to cleaner tasks than when they transitioned in the opposite direction (adjusted OR, 0.93; P < .0001). Conclusions: HCWs did not organize patient care tasks in a manner that decreased risk to patients, and they were less likely to perform hand hygiene when transitioning from dirtier to cleaner tasks than the reverse. These practices could increase the risk of transmission or infection.


2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S425-S425
Author(s):  
Maureen Banks ◽  
Andrew Phillips ◽  
Keith Chin ◽  
Lou Ann Bruno-Murtha

Abstract Background Hand hygiene (HH) is the cornerstone of infection prevention and improved compliance has been associated with reduced healthcare-associated infections (HAIs). However, traditional methods for HH data collection have limitations and may not accurately reflect true compliance. We sought to evaluate whether an electronic hand hygiene monitoring system (HHMS) can improve data collection, compliance, and reduce HAIs. Methods A HHMS was implemented as part of a pilot at a single facility in June 2018 for all healthcare workers (HCWs) who entered patient rooms. The system prompted HCWs to perform HH with an audible and visual reminder emitted from a badge if a HH event had not been registered within specific timeframes of entering or exiting a patient room. The system captured compliance with preferential handwashing (soap and water) for at least 15 seconds upon exit of Clostridioides difficile (C. difficile) designated rooms. All HH data were collected by the HHMS. Hand hygiene compliance and HAI data were compared for the pre-intervention (June 2017-May 2018) and intervention periods (July 2018-March 2019). No changes were made to environmental cleaning protocols or compliance monitoring, nor in antibiotic stewardship practices. Results HH compliance by direct observation in the pre-intervention period was 91% (1,612 observations). HH compliance with the HHMS during the intervention period was 97% (2,778,402 observations). The mean monthly HH opportunities recorded during the pre-intervention period was 134, while the HHMS captured 308,711, a greater than 2,300-fold increase. The incidence of healthcare facility-onset C. difficile infections (HO-CDI) pre-intervention was 9.60 per 10,000 patient-days (41 GDH+/Toxin+ laboratory-identified [labID] events/42,726 patient-days). With the HHMS, HO-CDI decreased 70% (P = 0.0003) to 2.89 per 10,000 patient-days (9 labID events/31,169 patient-days). No policy changes in environmental cleaning of high-touch surfaces were made or observed during the pilot. Conclusion The use of an HHMS facilitated more comprehensive HH data and improved compliance. The preliminary findings also support an association between more robust HH compliance data and a significant decrease in toxin-producing CDI. Disclosures All authors: No reported disclosures.


2011 ◽  
Vol 32 (10) ◽  
pp. 1016-1028 ◽  
Author(s):  
John M. Boyce

Monitoring hand hygiene compliance and providing healthcare workers with feedback regarding their performance are considered integral parts of multidisciplinary hand hygiene improvement programs. Observational surveys conducted by trained personnel are currently considered the “gold standard” method for establishing compliance rates, but they are time-consuming and have a number of shortcomings. Monitoring hand hygiene product consumption is less time-consuming and can provide useful information regarding the frequency of hand hygiene that can be used to give caregivers feedback. Electronic counting devices placed in hand hygiene product dispensers provide detailed information about hand hygiene frequency over time, by unit and during interventions. Electronic hand hygiene monitoring systems that utilize wireless systems to monitor room entry and exit of healthcare workers and their use of hand hygiene product dispensers can provide individual and unit-based data on compliance with the most common hand hygiene indications. Some systems include badges (tags) that can provide healthcare workers with real-time reminders to clean their hands upon entering and exiting patient rooms. Preliminary studies suggest that use of electronic monitoring systems is associated with increased hand hygiene compliance rates and that such systems may be acceptable to care givers. Although there are many questions remaining about the practicality, accuracy, cost, and long-term impact of electronic monitoring systems on compliance rates, they appear to have considerable promise for improving our efforts to monitor and improve hand hygiene practices among healthcare workers.


2014 ◽  
Vol 35 (9) ◽  
pp. 1189-1192 ◽  
Author(s):  
Matthew P. Muller ◽  
Alexander I. Levchenko ◽  
Stanley Ing ◽  
Steven M. Pong ◽  
Geoff R. Fernie

2010 ◽  
Vol 31 (7) ◽  
pp. 716-721 ◽  
Author(s):  
Daniel J. Morgan ◽  
Stephen Y. Liang ◽  
Catherine L. Smith ◽  
J. Kristie Johnson ◽  
Anthony D. Harris ◽  
...  

Background.Multidrug-resistant (MDR) gram-negative bacilli are important nosocomial pathogens.Objective.To determine the incidence of transmission of MDRAcinetobacter baumanniiandPseudomonas aeruginosafrom patients to healthcare workers (HCWs) during routine patient care.Design.Prospective cohort study.Setting.Medical and surgical intensive care units.Methods.We observed HCWs who entered the rooms of patients colonized with MDRA. baumanniior colonized with both MDRA. baumanniiand MDRP. aeruginosa. We examined their hands before room entry, their disposable gloves and/or gowns upon completion of patient care, and their hands after removal of gloves and/or gowns and before hand hygiene.Results.Sixty-five interactions occurred with patients colonized with MDRA. baumanniiand 134 with patients colonized with both MDRA. baumanniiand MDRP. aeruginosa. Of 199 interactions between HCWs and patients colonized with MDRA. baumannii, 77 (38.7% [95% confidence interval {CI}, 31.9%–45.5%]) resulted in HCW contamination of gloves and/or gowns, and 9 (4.5% [95% CI, 1.6%–7.4%]) resulted in contamination of HCW hands after glove removal before hand hygiene. Of 134 interactions with patients colonized with MDRP. aeruginosa, 11 (8.2% [95% CI, 3.6%–12.9%]) resulted in HCW contamination of gloves and/or gowns, and 1 resulted in HCW contamination of hands. Independent risk factors for contamination with MDRA. baumanniiwere manipulation of wound dressing (adjusted odds ratio [aQR], 25.9 [95% CI, 3.1–208.8]), manipulation of artificial airway (aOR, 2.1 [95% CI, 1.1–4.0]), time in room longer than 5 minutes (aOR, 4.3 [95% CI, 2.0–9.1]), being a physician or nurse practitioner (aOR, 7.4 [95% CI, 1.6–35.2]), and being a nurse (aOR, 2.3 [95% CI, 1.1–4.8]).Conclusions.Gowns, gloves, and unwashed hands of HCWs were frequently contaminated with MDRA. baumannii. MDRA. baumanniiappears to be more easily transmitted than MDRP. aeruginosaand perhaps more easily transmitted than previously studied methicillin-resistantStaphylococcus aureusor vancomycin-resistantEnterococcus. This ease of transmission may help explain the emergence of MDRA. baumannii.


2014 ◽  
Vol 35 (3) ◽  
pp. 225-230 ◽  
Author(s):  
Laura Goodliffe ◽  
Kelsey Ragan ◽  
Michael Larocque ◽  
Emily Borgundvaag ◽  
Sophia Khan ◽  
...  

Objective.Identify factors affecting the rate of hand hygiene opportunities in an acute care hospital.Design.Prospective observational study.Setting.Medical and surgical in-patient units, medical-surgical intensive care unit (MSICU), neonatal intensive care unit (NICU), and emergency department (ED) of an academic acute care hospital from May to August, 2012.Participants.Healthcare workers.Methods.One-hour patient-based observations measured patient interactions and hand hygiene opportunities as defined by the “Four Moments for Hand Hygiene.” Rates of patient interactions and hand hygiene opportunities per patient-hour were calculated, examining variation by room type, healthcare worker type, and time of day.Results.During 257 hours of observation, 948 healthcare worker-patient interactions and 1,605 hand hygiene opportunities were identified. Moments 1, 2, 3, and 4 comprised 42%, 10%, 9%, and 39% of hand hygiene opportunities. Nurses contributed 77% of opportunities, physicians contributed 8%, other healthcare workers contributed 11%, and housekeeping contributed 4%. The mean rate of hand hygiene opportunities per patient-hour was 4.2 for surgical units, 4.5 for medical units, 5.2 for ED, 10.4 for NICU, and 13.2 for MSICU (P < .001). In non-ICU settings, rates of hand hygiene opportunities decreased over the course of the day. Patients with transmission-based precautions had approximately half as many interactions (rate ratio [RR], 0.55 [95% confidence interval (CI), 0.37-0.80]) and hand hygiene opportunities per hour (RR, 0.47 [95% CI, 0.29-0.77]) as did patients without precautions.Conclusions.Measuring hand hygiene opportunities across clinical settings lays the groundwork for product use-based hand hygiene measurement. Additional work is needed to assess factors affecting rates in other hospitals and health care settings.


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