Who goes in and out of patient rooms? An observational study of room entries and exits in the acute care setting

•A large observation study of 14,876 patient room entries and exits in 16 hospitals was done performed•Entries and exits are attributed to health care personnel (HCP) and non-HCP.•HCP accounted for 83.6% of all patient roomentries and exits (95% CI confidence interval, 81.3%–87.6%).•HCP have the greatest impact on entries and exits in pediatric and adult units.•The data are useful to hospitals trying automated hand hygiene monitoring systems. The objective of this study is to determine what percentage of patient room entries and exits (opportunities) are attributed to health care personnel (HCP) and non-HCP. A total of 14,876 opportunities were observed by clinicians in 29 units of 16 hospitals. HCP accounted for 83.6%; 95% confidence interval, 81.3%-87.6%. This finding provides hospitals an initial baseline for HCP room traffic when implementing community-based automated hand hygiene monitoring and compliance improvement efforts. The objective of this study is to determine what percentage of patient room entries and exits (opportunities) are attributed to health care personnel (HCP) and non-HCP. A total of 14,876 opportunities were observed by clinicians in 29 units of 16 hospitals. HCP accounted for 83.6%; 95% confidence interval, 81.3%-87.6%. This finding provides hospitals an initial baseline for HCP room traffic when implementing community-based automated hand hygiene monitoring and compliance improvement efforts. Health care facilities can use electronic compliance monitoring, also known as automated hand hygiene monitoring systems (AHHMS), to complement direct observations of hand hygiene (HH) compliance of health care personnel (HCP).1Sax H Allegranzi B Uckay I Larson E Boyce J Pittet D ‘My five moments for hand hygiene’: a user-centered design approach to understand, train, monitor and report hand hygiene.J Hosp Infect. 2007; 67: 9-21Abstract Full Text Full Text PDF PubMed Scopus (512) Google Scholar AHHMS vary in their capacity to capture data. For example, some systems capture data at the person-specific level requiring HCP to wear specialized electronic badges, whereas other systems capture data at the community level (eg, unit level, no badges). For community systems, entering or exiting an occupied patient room by any person is considered an opportunity for HH, and dispenser use by any person is captured as well and is considered an HH event.2Boyce JM. Measuring health care worker hand hygiene activity: current practices and emerging technologies.Infect Control Hosp Epidemiol. 2011; 32: 1016-1028Crossref PubMed Scopus (136) Google Scholar, 3Boyce JM Electronic monitoring in combination with direct observation as a means to significantly improve hand hygiene compliance.Am J Infect Control. 2017; 45: 528-535Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar These innovative AHHMS technologies use different approaches to determine the opportunities (ie, denominator) for HH, but nevertheless can be value-added tools in a multimodal strategy to improve HH. One challenge of the community-level approach is that it does not identify who is accountable for the patient room entries or exits contributing to the opportunities for HH. Total numbers of opportunities from non-HCP (eg, patients, visitors, and others) may be overestimated. It is important to address this challenge because if HCP do not understand the impact that patients, visitors, and others have on community-level data, they may attribute a lower-than-expected compliance rate to non-HCP, thereby forfeiting ownership, accountability, and team engagement (GOJO unpublished data, 2018). Undeniably, observational studies have shown visitor and patient HH compliance rates to be very low.4Wolfe R O'Neill E Hand hygiene compliance by visitors to hospitals—can we do better?.Am J Infect Control. 2012; 40: 899-901Abstract Full Text Full Text PDF PubMed Scopus (7) Google Scholar, 5Burnett E Lee K Kydd P Hand hygiene: what about our patients?.Br J Infect Control. 2008; 9: 19-24Crossref Scopus (15) Google Scholar, 6Birnbach DJ Nevo I Barnes S Fitzpatrick M Rosen LF Everett-Thomas R et al.Do hospital visitors wash their hands? Assessing the use of alcohol-based hand sanitizer in a hospital lobby.Am J Infect Control. 2012; 40: 340-343Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar Although HCP are the focus of most HH initiatives, HH by patients and visitors is also important to prevent the acquisition and transmission of pathogens. A whole system approach that encourages more compliance among visitors and patients, with HCP taking ownership of visitor compliance on their units is ideal. Nevertheless, because the number of opportunities generated by patients and visitors is unknown, it is not clear how non-HCP impact HH compliance rates. Therefore, the objective of this study is to determine what percentage of patient room entries and exits can be attributed to HCP and non-HCP. Opportunities (defined as patient room entry or exit) were observed and recorded by 6 clinicians (5 registered nurses and 1 registered respiratory therapist) employed by GOJO Industries during PURELL SMARTLINK Activity Monitoring System (GOJO Industries, Akron, OH) installation and validation. Unit HCP were informed that the observers were counting the frequency and role of individuals entering and exiting patient rooms. Observations were made via convenience sampling in 29 units of 16 hospitals in the United States and Canada. Unit types observed were: emergency department, intensive care unit, medical surgical, oncology, and pediatrics. Units varied in size from 10-41 beds. Observers moved throughout the unit to collect data from all occupied patient rooms observable from a location in the hallway. The observer could only see certain rooms at a time and would not be able to see who was entering or exiting other rooms. Data was not necessarily collected on each patient room. Also, the observers moved throughout the unit so that there was time spent in every hallway (therefore, each patient room was observed in aggregate). Monitoring sessions could be continuous or over several shorter sessions that typically summed to 1-3hours across shifts or multiple days. Each entry and exit of a patient room was counted as a separate opportunity (eg, if an individual entered a patient room then exited, this activity counted as 2 opportunities). Observers identified the individuals using the following categories: unit HCP (eg, nurses and nurse aides), non-unit HCP (eg, physicians, environmental services, and physical therapy), patients, visitors, and other (eg, clergy and hospice workers), which were then simplified to 3 categories: HCP, non-HCP, and other. In 5 units, observations did not distinguish between unit HCP and non-unit HCP. In13 units, observers did not distinguish between patients and visitors. The time of the observation period was recorded, as well as room exits and entrances. Logistic regression with random effects for observers, hospitals, and units nested in hospitals was used to compare observed shifts (ie, day [7:00 AM to 3:00 PM], afternoon [3:00 PM to 11:00 PM], and night [5:00 AM to 7:00 AM]) and the 5 types of units. The effect of the unit size and hospital was assessed by including a covariate for the number of beds. Wald tests7Kutner M Hachtsheim C Neter J Li W Applied linear statistical models.5th ed. McGraw Hill, New York (NY)2004Google Scholar were performed to compare shifts and unit types, and likelihood profiles were used to generate confidence intervals (CIs) for comparisons of interest. Observers recorded 14,876 opportunities in 29 units of 16 hospitals. The mean number of observations per unit was 513. A small percentage (0.7%) of opportunities involved individuals categorized as other (eg, clergy and hospice workers). HCP were responsible for ≥75% of the opportunities in each of the 16 hospitals (Table1), 83.6% over all hospitals (95% CI, 79.9%-90.5%).Table1Summary of patient room entry and exit opportunities by hospitalHospitalNo. of opportunitiesTotal HCP (%)Total visitors and patients (%)No. of beds per hospitalA634565 (89.1)69 (10.9)397B722607 (84.1)115 (15.9)557C1,175893 (76.0)179 (15.2)100D360299 (83.1)61 (16.9)100E1,8331,595 (87.2)238 (13.0)532F218200 (91.7)18 (8.3)215G2,4002,104 (87.7)296 (12.3)300H702547 (77.9)155 (22.1)350I428353 (82.5)75 (17.5)732J1,3811,187 (89.0)194 (14.1)700K474394 (83.1)80 (16.9)58L868683 (78.7)185 (21.3)159M945781 (82.7)116 (12.3)1550N304269 (88.5)35 (11.5)150O716671 (93.7)45 (6.3)209P1,7161,291 (75.2)425 (24.8)178HCP, health care personnel. Open table in a new tab HCP, health care personnel. When analyzing opportunities by units, HCP were responsible for >76% of the opportunities in all but 3 units (Table2). The percentage of opportunities attributed to HCP was not significantly associated with unit size, as measured by bed number 6.2% decrease in odds of HCP for every 10 beds (95% CI, 22%-28%, P = .612). In 24 of the 29units, it was possible to distinguish between unit HCP and non-unit HCP (data not shown). Of the 12,564 opportunities observed in those 24units, 7,038 (56.0%) involved unit HCP, 3,710 (29.5%) non-unit HCP, and 1,757 (14.0%) visitors and patients.Table2Summary of patient room entry and exit opportunities by unit typeUnit type (no. of units)HospitalNo. of opportunitiesTotal HCP (%)Total visitors and patients (%)No. of beds per unitED (2)J1,3811,187(86.0)194 (14.1)41K474394 (83.1)80 (16.9)10ICU (4)G643567 (88.2)76 (11.8)12G672625 (93.0)47 (7.0)12I249205 (82.3)44 (17.8)26M945781 (82.7)116 (12.3)20Med Surg (13)A634565 (89.1)69 (10.9)22B722607 (84.1)115 (15.9)20C596400 (67.1)104 (17.4)27C579493 (85.2)75 (13.0)16E299274 (91.6)25 (8.4)41E275238 (86.6)37 (13.5)41E714635 (88.9)79 (11.1)41G681547 (80.3)134 (19.7)19G404365 (90.4)39 (9.7)17I179148 (82.7)31 (17.3)30L868683 (78.7)185 (21.3)25N304269 (88.5)35 (11.5)36O716671 (93.7)45 (6.3)23Oncology (2)E545448 (82.2)97 (17.8)30F218200 (91.7)18 (8.3)13Adult units total13 hospitals, 21 units12,09810,302 (85.2)1,645 (13.6)522Pediatric (8)D7950 (63.3)29 (36.7)36D11690 (77.6)26 (22.3)36D165159 (96.4)6 (3.6)36H702547 (77.9)155 (22.1)15P526412 (78.3)114 (21.7)28P460351 (76.3)109 (23.7)28P374315 (84.2)59 (15.8)24P356213 (59.8)143 (40.2%)24Pediatric total3 hospitals, 8 units2,7782,137 (76.9)641 (23.1)227Total of all units16 hospitals, 29 units14,87612,436 (83.6)2,286 (15.4)749ED, emergency department; HCP, health care personnel; ICU, intensive care unit; Med Surg, medical surgical. Open table in a new tab ED, emergency department; HCP, health care personnel; ICU, intensive care unit; Med Surg, medical surgical. Pediatric units showed a 42% lower odds of HCP opportunities (or the equivalent of a 72% higher percentage of patient- and visitor-related opportunities) compared with adult units (P = .0111). Table2 shows the adult units had similar percentages for HCP (67.1%-93.7%) and patients and visitors (6.3%-17.4%). Observations were assigned to specific shifts when possible: day (5,306, 36%), afternoon (2,870, 19%), and night (253, 2%), with 6,447 crossed over 2 shifts (eg, 500 observations obtained between 1:00 PM and 5:00 PM, where 1:00 PM to 3:00 PM would be considered the day shift and 3:00 PM to 5:00 PM would be considered the afternoon shift). The odds of HCP entering and exiting relative to non-HCP was 4.7times (370%) higher at night compared with either day or afternoon shifts (odds ratio = 4.7; P < .0001). There was a 15% decrease in odds in afternoon shifts compared to day shifts (P = .027). This publication quantifies patient room opportunities attributable to various types of individuals in 16 hospitals and a wide variety of units with a large number of total observations. In reviewing the literature, 1 other study found that nurses and visitors comprised the most frequent entries and exits into patient rooms.8Cohen B Hyman S Rosenberg L Larson E Frequency of patient contact with healthcare personnel and visitors: implications for infection prevention.Jt Comm J Qual Patient Saf. 2012; 38: 560-565Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar For HCP to use the data generated from the system to change HH behavior, theymust be able to understand the data and apply meaning to it.9Conway LJ. Challenges in implementing electronic hand hygiene monitoring systems.Am J Infect Control. 2016; 44: e7-e12Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar Educating HCP on who goes in and out of patient rooms may help build trust in the system and increase accountability. Health care facilities implementing community-level AHHMS will benefit from performing direct observation audits in each location to determine the AHHMS accuracy and their own estimates of HCP and non-HCP opportunities.10Limper HM Garcia-Houchins S Slawsky L Hershow RC Landon E A validation protocol: assessing the accuracy of hand hygiene monitoring technology.Infect Control Hosp Epidemiol. 2016; 37: 1002-1004Crossref PubMed Scopus (16) Google Scholar The validity of these results is strengthened by a large number of observations in multiple centers and multiple care settings (the 16 hospitals represent a broad portion of the United States and 1 Canadian province). Although there was substantial variability across facilities, units within facilities, and observers, trends were still determined, which suggests that these results are pertinent to other facilities and units similar to the ones that we studied. Small hospital studies do not confer this same scope of inference. However, our limitations did not account for the day of the week, regional differences that may come from culture, and weather, and fewer observations were made at night (during which 94.9% of opportunities were from HCP). Future studies can improve the strength of the conclusions with equal representation of all shifts. Ultimately, AHHMS can provide robust actionable data for managing risks associated with both HCP and non-HCP from a whole clinical system perspective and help us to learn more about HH behavior. AHHMS as a complement to direct observation can be a value-added tool in a multimodal HH strategy.

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