Assessing Hand Hygiene Compliance in Healthcare Workers to Reduce Infectious Disease

Hand hygiene is the first line of defense against the prevalence of infectious diseases in healthcare settings. Therefore, healthcare costs can be reduced. However, having rare incidents of healthcare-associated infections (HAI) does not always mean that hand hygiene compliance is high and at its desired level. This research study aims to develop multi-statistical measurements to assess hand hygiene compliance of the medical and nursing groups at the inpatient wards, 5B, 6B and ICU at the Providence Veterans Affairs Medical Center (PVAMC). The PVAMC was trying to identify whether the few cases or rare incidents of HAI that have been reported in the past few years was caused by or linked to poor hand hygiene practices. Healthcare worker (HCWs) subgroups of nurses and hospitalist doctors were asked to self report their patient contact over one complete week. The URI research team and 25 other secret observers were asked to directly observe the medical and nursing groups’ hand hygiene behavior over two complete months including all working shifts: night, day and evening. These two months were overlapped with the one complete week of selfreporting patient contact. The results indicated that the monthly hand hygiene compliance mean estimation was not as expected by the PVAMC. The monthly hand hygiene compliance mean estimation was around 50%. The results also indicated that as bed days of care (BDOC) increased, hand hygiene compliance decreased. In contrast, the results did not indicate any strong correlation between hand hygiene compliance and HAIs. However, the possibility that the PVAMC has been adopting other infection prevention methods that are associated with the rare HAI incident (for example zero MRSA for the past five years) cannot be eliminated or ignored. Hand hygiene compliance was higher after touching a patient than before, even though both are recommended in the World Health Organization’s 5 moments of hand hygiene. Risk factors for poor adherence to recommended hand-hygiene practices were observed and found to be statistically significant, including being a male patient, working in a step-down unit (5B) and working on weekdays and working in night shift. In addition, an attempt was made to indirectly estimate hand hygiene compliance over a 10 month period by measuring how many times Purell and Soap cartridges were replaced at inpatient wards. Similarly, an attempt was made to indirectly estimate personal protective equipment (PPE) compliance over a three year period using PPE inventory data. In the indirect methods, patient contact data was used to average how many times a patient was seen by the medical and nursing groups. This estimation was used to indirectly estimate the hand hygiene compliance. The indirect hand hygiene compliance via measuring product use (Purell and Soap) was very low compared to the hand hygiene compliance estimated via the direct hand hygiene observation method when the same two months were compared in all inpatient wards. The actual Purell and Soap replacement was not equal to or close to the targeted replacement at any of the inpatient wards. The research study did not find any correlation between BDOC and hand hygiene compliance under such a method. The source of error on the indirect PPE compliance method forced the compliance to go beyond 100% in several months. The research study did not find any correlation between BDOC and PPE compliance. Such methods need more validation, but is an interesting first step for a new proposed method.


LIST OF TABLES
mentions that "hand hygiene is a general term that applies to either handwashing, antiseptic handwash, antiseptic hand rub, or surgical hand antisepsis" (CDC, 2002, p. 3).
2 According to TJC (2009) "Other factors include such things as patient severity of illness, equipment and environmental sanitation practices, and adherence to recommended practices" (TJC, 2009, xvi). Thus, according to Pittet (2003) and CDC (2002), hand hygiene is one of the most important intervention tools to prevent and control the spreading of HAIs. Advanced technologies have been widely used in healthcare to reduce cost of care and improve quality of care. Yet, controlling the transmission of healthcare-associated infections (HAIs) between healthcare workers (HCWs) and patients, and preventing disease from spreading within the hospital environment remain unsolved (Pittet, 2003). ward is considered a step-down unit.
The aim of this research is to study hand hygiene compliance by directly observing healthcare workers (HCWs) at the Providence Veterans Affairs Medical Center (PVAMC) and to obtain a more accurate estimate of the total number of times per week that a doctor or nurse contacts different patients in an effort to identify the best estimate of the true hand hygiene compliance rate at PVAMC. The estimate of the true hand hygiene compliance rate will be measured by first using descriptive or summary statistics of hand hygiene compliance per inpatient ward and all wards combined. Hypotheses testing of selected risk factors for poor adherence to recommended hand hygiene practices will be conducted and correlations of hand hygiene compliance with BDOC and HAIs will be made. Self-reporting data of patient-contacts by doctors and nurses who work at the inpatient units, 5B, 6B and ICU at PVAMC for all shifts (Night, Day and Evening) will be collected. Then, recruited secret observers and the University of Rhode Island (URI) research team member will observe healthcare workers (doctors and nurses) at the inpatient units during hand hygiene critical moments (Before and After Patient-Contact). Patient-contact data collection will be done for one complete week, and secret observers data collection will be conducted one month prior to self-reporting patient-contact data and one month overlapping with the week of patient-contact data collection. One month prior, and one month overlapping the one week self-reporting patient-contact data collection, the URI research member will also do hallway observations to augment the self-report data on patient contacts that comes from the doctors and nurses randomly Historical data of key infectious diseases at PVAMC will be used to assess appropriate statistical analysis methods and recommend improved methods of tracking and reporting data for hand hygiene compliance of HCWs. In the healthcare industry, 5 the challenge is to prevent and control the prevalence and transmission of HAIs to patients inside the hospitals who were admitted for medical needs rather than the treatment of infectious diseases.
According to TJC (2009), "measurement is only the beginning" (TJC, 2009, p.107). Thus, after measuring the hand hygiene rate precisely and accurately, barriers that hinder HCWs from practicing hand hygiene accordingly can be identified and tackled for further hand hygiene compliance improvement. However, tracking and measuring hand hygiene practices of HCWs is no easy task since it involves privacy issues of HWCs as well as privacy issues of patients. Before exploring each method and its strengths and limitations, hospital-associated infections (HAIs) of interest are first explored and defined.
There is always room for improvement in hand hygiene compliance measurement. In this study, a more reliable direct hand hygiene observation method is conducted at a Veterans Affairs Medical Center by recruiting secret observers.
Reliability of the method is met by having a larger sample size of hand hygiene observations that ensures an error size of about 3% from the population hand hygiene compliance parameter, by focusing on two important healthcare worker groups, medical and nursing and by exploring total patient contact of each group to see whether confidence interval of hand hygiene compliance could be corrected using the finite population correction (fpc) factor to reduce the standard error by a value equal to In this study, the indirect hand hygiene manual monitoring of product use is improved mostly by having better average patient contact data based on self-report data for one complete week and also encompassing all device-associated moments (device-invasive days). Such a method is a good alternative for hand hygiene compliance monitoring if the direct hand hygiene observation method is not an option.
In addition, a brand new indirect method of measuring personal protective equipment (PPE) compliance is developed based on an assumption that states PPE quantity ordered is PPE quantity consumed.
Radio Frequency Identification (RFID) technology for hand hygiene measurement and intervention was considered, however, the request was not accepted by the PVAMC because of privacy issues. RFID was also suggested for patient 7 contact data collection, which could be used over at least 6 months to have a better estimate of average patient contact per day, however, the request was also not accepted.
The study has several limitations common to research studies in the healthcare field and in hand hygiene compliance. The

REVIEW OF LITERATURE
There are several aspects of hand hygiene in the literature when it comes to tracking and observing healthcare workers (HCWs). According to CDC (2002), before proceeding with tracking, observing and measuring the hand hygiene compliance of HCWs, one should determine which aspect or aspects to measure and observe.
Consequently, one can determine types of products used to cleanse hands and monitor the use of the appropriate product type after contacting patients. observe. Some studies have combined multiple methods and some others have just applied one method to measure hand hygiene practice among HCWs.

Healthcare and Epidemic Infectious Diseases
Over the past 15 years, several new virus strains have emerged in various areas of the world that have challenged healthcare systems. They have been similar because each of them began in a specific region of the world, and caused concern of an epidemic as people traveled to and from those areas. They are also similar because many of them had a link with transmission hosts in animals (for example the bird flu, H1N1 in the United States, swine flu, SARS in China, MERS and Coronavirus which may spread through camels or bats, etc.). Healthcare-Associated Infections (HAIs) are adverse events that can be spread or contracted while a patient is in the hospital for treatment of unrelated health conditions. In contrast, Community-Acquired Infections (CAI) are adverse events that can be spread or contracted in a community settings.
Unfortunately, some infectious disease cases have risen in community settings as well, which causes epidemics such as Ebola in Sierra Leone and other African countries, which has recently become the first concern of the World Health Organization, WHO.
Preventing and controlling the spread of any kind of infectious diseases would not only result in a significant reduction in costs, but also result in better patient outcomes.

Hospital-Onset Infections Caused by Specific Pathogens
According to Magill

Clostridium Difficile Infection (C.Diff or CDI)
According to Gerding  The increase in morbidity and mortality of CDI makes the healthcare industry worldwide worried (Gerding, Muto and Owens, 2008). Consequently, the burden cost of treating CDI increases as well. Fekety, Kim, Brown, Batts, Cudmore and Silva (1981) state that the environment plays an important role in the transmission of CDI because its spores can live for prolonged periods on hard surfaces. According to Gerding, Muto and Owens (2008), the spores also cannot be removed from the forehands of healthcare workers and the forehands of healthcare workers cannot be disinfected with the use of alcohol-based sanitizers. So, antiseptic handwash or antiseptic hand-rub is no use in removing the spores physically from the hands of healthcare workers (Larson and Morton, 1991;Denton, 1991;Gershenfeld, 1962;Russell, 1991). Thus, healthcare workers must practice hand hygiene with antimicrobial soap and water to ensure CDI spore removal after contacting a CDI incidence case (Larson and Morton, 1991;Denton, 1991;Gershenfeld, 1962;Russell, 1991). The special characteristics of Clostridium Difficile spores make CDI one of the highest contagious bacterial infections in healthcare settings. One special characteristic of the spores is that, according to Fekety, Kim, Brown, Batts, Cudmore and Silva (1981), Fordtran (2006) and Owens (2006), the spores can survive on dry and hard surfaces for months. Thus, special care and attention has to be present when cleaning and disinfecting the environments of healthcare settings during the presence of CDI. However, according to Owens (2006), Mayfield, Leet, Miller and Mundy (2000), Underwood, et al. (2005), some of the cleaning detergent agents are not meant to be used for CDI cases because they might make the situation worse by being a good source for sporulation and then increase the prevalence of CDI. Subsequently, it is highly recommended to use sporicidal chemical cleaning detergent agents especially during outbreaks of CDI (Gerding, Muto and Owens, 2008;CDC, 2002). Yet, more attention should be present when using such agents because, according to Mayfield, Leet, Miller and Mundy (2000), overuse of these detergents for prolonged time puts equipment and inanimate surfaces of healthcare at risk of being deteriorated and puts floor workers such as housekeepers at risk of developing respiratory illness though those agents were found to be effective in controlling and preventing CDI.
According to Dubberke, Gerding, Classen, et al. (2008), some of the CDI prevention strategies are that healthcare personnel including housekeeping, families and visitors have to be on contact precautions every time they enter patients' rooms with CDI, and they have to adhere to hand hygiene compliance by washing hands with 13 soap and water when exiting patients rooms with CDI and when taking gloves off hands.
According to Dubberke and Olsen (2012) Prevention strategies for VAP are to disinfect hands with hand rub or wash hands with antimicrobial soap and water before and after contacting a patient with VAP. It is also recommended to adhere to standard precautions such as wearing gloves before the insertion of ventilating tube, washing hands before and after wearing gloves, and wearing gowns especially when a patient with VAP is expected to be soiling with respiratory secretions (Tablan et al., 2004).

Hand Hygiene
Healthcare-associated infections (HAIs) are adverse events that not just endanger patients' lives but also add extra burden costs to the economy of countries. ). If an overpowered sampling size study were carried out, this is considered unethical, especially when the study involves human-and animal-subjects (Lenth, 2001), because it is considered as a waste of the study resources (McCrum-Gardner, 2010). "The power is the probability of correctly rejecting H 0 " (Montgomery, 2012, p.113), "Power = 1−! = P{reject H 0 | H 0 is false}" (Montgomery, 2012, p.113). In contrast, type I error is called (Alpha) "! = P{type I error} = P{reject H 0 | H 0 is true}" (Montgomery, 2012, p.113), and Type II Error is called (Beta) "! = P{type II error} = P{fail to reject H 0 | H 0 is false}" (Montgomery, 2012, p.113). Thus, a balanced power value between both over-and underpowered sampling size has to be used to minimize the effects of Type I Error and/or Type II Error (Lieberman and Cunningham, 2009).
Usually, "A target value of .80 is fairly common and also somewhat minimal" (Lenth, 2001, p.188); however, this value depends on the type of the study to be carried out (Sullivan and Feinn, 2012 In addition to these lists of methods, healthcare sectors recently have started using technology-based real-time locating systems (RTLS) to track and capture opportunities for hand hygiene. According to Wu, Ranasinghe, Sheng, Zeadally and Yu (2011) and Ajami and Akbari (2012), real-time locating systems (RTLS) are wireless-based systems that are used to identify the location of objects or human beings, for example, assets or healthcare workers HCWs within defined areas and zones. Historically, according to Landt (2005), RTLS was first implemented during the Second World War in the 1940s in the USA. Its application at that time was merely used in the identification of allied airplanes. According to Krohn (2015), RTLS can optimize processes, remove waste of duplicated hospital operations and increase the quality of care and thus reduce healthcare costs.
RTLS is not only deployed in monitoring hand hygiene practices for healthcare workers performance. According to Krohn (2015), RTLS is also deployed in optimizing the workflows of hospital staffing and in managing Emergency Rooms (ER) and Operation Rooms (OR), to name few. According to Vakili, Pandit, Singman, Appelbaum, and Boland (2015), RTLS can be in different forms of technologies such as "Blue-tooth, iBeacon, Wi-Fi, camera vision, ultrasound, radio frequency identification (RFID), infrared (IR), global positioning systems (GPS), and cellular signals" (Vakili, Pandit, Singman, Appelbaum, and Boland, 2015, p.2).
Each proposed method to track and measure hand hygiene performance of healthcare workers has its advantages and disadvantages. Some healthcare sectors have applied bundles of hand hygiene performance tracking methods together to ensure a superior level of hand hygiene compliance rate by capturing most of the critical and required moments for hand hygiene practices.

Direct Hand Hygiene Monitoring Methods
Healthcare sectors have sought direct monitoring of HCWs' hand hygiene performance either by recruiting secret observers or patient observers or by asking HCWs for self-report. Though  WHO (2009) lists three observational effects or bias, which is generated when the direct observation hand hygiene performance method is applied. The first bias is observation bias, which is described as an increase in the quality of hand hygiene performance for healthcare workers as seen by an observer. According to Hugonnet, Perneger and Pittet (2002), Pittet (2002), Landsberger (1958) and Bittner and Rich (1998), this is what it is referred to as the "Hawthorne Effect". Its name was adapted from the Hawthorne Factory of Western Electrics after conducting several ergonomics studies in the USA at the beginning of the 20 th century (WHO, 2009). Observation bias can be reduced or eliminated if the observer's identity is unrevealed. However, if the observer's identity were revealed, trust might disappear. Unrevealing the observer's identity is also hard for long-term observing of hand hygiene opportunities (WHO, 2009).
The second bias is observer bias. Such bias is defined as "the systematic error introduced by inter-observer variation in the observation method" (WHO, 2009, p.159

Patient Assessment Method
Hand hygiene performance could be assessed and measured by patients.  2004, p.235). Such a program empowers patients to be part of hand hygiene performance intervention and thus to be part of their program of care. In both studies, patients were asked to be part of assessing hand hygiene performance of their healthcare workers before a contact has taken place.
Consequently, the hand hygiene compliance rate increased by at least 50%. However, small sample size was an issue for both studies.
Though the patient assessment method requires less healthcare staffing and may reduce cost for hospitals (McGuckin et al., 2001) and may provide "the framework for a synergistic healthcare experience" between healthcare workers and patients (Williams, 2002, p.104), involving and empowering patients in assessing and measuring hand hygiene performance of their healthcare workers has some challenges and difficulties. According to Pittet, Mourouga and Perneger (1999), and Wade, (1995), patients' health status might hinder the accomplishment of such a task.
Patients also might find it inconvenient to observe their healthcare workers and evaluate them. In general, the patient assessment method "is not well documented" (WHO, 2009, p.159), and an objective evaluation of the method has not been conducted yet (Williams, 2002 According to a study conducted by Simmons et al. (1990) where nurses were the focus of the study, the self-assessment questionnaire and surveys did reveal the true perceptions of the nurses about their hand hygiene practices and adherence rate of compliance did not match that of their patients' and families' though the main objective of the study was to find any correlation between handwashing frequency and infectious disease rate. Nurses thought that they were doing a great job in practicing hand hygiene and their compliance rate was above 90%.
The perceptions of the nurses were positively corrected after the infection control nurses critiqued them during the intervention. In the same study, a secret observer was covertly conducting direct observations (Simmons et al., 1990).
Interestingly, data of the self-assessment method did not correlate with that of the direct observation methods (Simmons et al., 1990;WHO, 2009

Indirect Hand Hygiene Monitoring Methods
Healthcare sectors have sought monitoring hand hygiene performance indirectly either manually by measuring the volume of product use (soap, alcohol-based handrub and paper towel) or electronically by measuring the frequency of occurrence or counts associated with the consumption of product use (soap, alcohol-based handrub and paper towel) using electronic counting devices or electronic monitoring systems (TJC, 2009 proposed more reliable methods to calculate the number of indications and the number of actual episodes for hand hygiene based on consumption of product use. These two numbers are used in the estimation of hand hygiene adherence rate.

Manual Monitoring of Product Use
Healthcare sectors have sought monitoring hand hygiene practices via the measurement of product use consumption such as soap, handrub (Purell) and paper 37 towels as a cost-effective method (WHO, 2009). One reason for seeking such an approach to measure hand hygiene performance is that, according to Bittner et al. (2002), sometimes an intervention that is based on feedback collection is better than an intervention that is based on cultural and organizational changes.
Healthcare sectors can measure the consumption of product use by weighing what is left of the product to measure what has been used over a specific period of time. They also can measure the consumption of product use by counting how many units of product have been used and replaced in a ward or a unit at hospitals. These data can also be compared with sales data of product purchased for the entire hospitals (TJC, 2009

Electronically Monitoring of Product Use via Electronic Monitoring Systems
Healthcare sectors have sought advanced technological devices to electronically track specific events of hand hygiene. These devices can be used to distinguish between the consumption of product use and track each healthcare worker's activities individually. They also can remind healthcare workers to practice handwashing at critical moments (Boyce, 2011;TJC, 2009

Radio Frequency Identification (RFID) Method
Recently, advanced information technologies (ITs) have been effectively used in the healthcare industry to increase the quality of patient care and to increase efficiency of healthcare service levels ( solution providers (e.g., IBM, 2003). RFID technology is classified as a wireless automatic identification and data capture (AIDC) technology (Fig. 1). AIDC technologies include bar phones, and Blackberries to d and interact; (ii) Wireless Loc (WLAN), which provides simp tranet access to PCs, PDAs, and with a wireless network card; (ii politan Area Network (WMAN) Wide Area Network (WWAN network used by most cellular and Global Positioning System technology based on a system of the earth).
In general, wireless networks data, resources, vital information tion tools, anytime, anywhere.
Basically, an RFID system is layers: (i) a tag containing a chip to or embedded in a physical obje (ii) a reader and its antennas tha interrogated and to respond wit tact (in contrast to bar codes, whi sight and must be read one at a computer equipped with a midd that manages the RFID equipmen interacts with enterprise applic Mandviwalla, 2005

RFID and Hand Hygiene
Recently, different hand hygiene approaches and systems that are integrated with the use of RFID technology have been proposed and assessed in several hospitals to prevent and control cross-contamination healthcare-associated infections by improving hand hygiene compliance. However, some barriers have been hindering the early use of RFID in hand hygiene tracking application. According to Boyce (2011), it would be more reasonable to use RFID in tracking hand hygiene compliance at a hospital that has already installed RFID infrastructure to be used in other applications such as asset-tracking applications. In this case, the cost factor would be lowered.
Additionally, quality of patient care and healthcare level of service need to be furthered assisted under the use of such technology.
In 2015, one interesting system that has been applied and implemented, according to Wittrup  the system consists of a badge to wear by each healthcare worker that has a sensor that is capable of soap and alcohol detection, a virtual wall that is installed inside and outside each patient room to identify the location of the badge, and a base-station 50 device that works as a badge charger and a data-storage hard disk. At the end of each shift, healthcare workers plug in their badges in the base-station device for charging and the base-station device transfers the hand hygiene compliance data from the badges into the device itself. The base-station device makes the data available for analysis by using a webpage software application or smart-phone application (Wittrup and Burba, 2015). Upon entering or leaving a patient room, the badge will emit a sound and turn yellow after a few seconds if hand hygiene is not practiced. Once healthcare workers practice hand hygiene, whether soap or alcohol, and place their hand over their badges, the badge turns green. If, however, hand hygiene is not practiced for more than the set duration time, the badge emits sounds and turns red.
Though the system is limited to capturing hand hygiene compliance upon entering or leaving a patient room, the adherence rate for hand hygiene compliance has been reported to be above 95% (Wittrup and Burba, 2015).

CHAPTER METHODOLOGY
A direct method will be used to monitor the overall hand hygiene compliance at PVAMC during routine patient care at three inpatient units, 5B, 6B and intensive care unit (ICU). The data will be compared to the current hand hygiene monitoring direct methods conducted by PVAMC, which will be used as a baseline for the study. The collected data from the designated two-month study will be compared to the same two months from the current method for the past four fiscal years, 2012 to 2015. This proposed hand hygiene direct method would not only report the estimate of the best hand hygiene compliance rate of HCWs at PVAMC during the time of the study, but also will identify several risk factors for poor adherence. Thus, the researchable questions of interest for this study are as follows: • What is a better estimate of hand hygiene compliance rate?
• Is there any correlation between hand hygiene opportunities and HAI incidents, MRSA colonization and community-acquired infections (CAI)?
• What are the risk factors for poor adherence (low compliance)?
As a secondary measurement, new indirect hand hygiene monitoring methods of measuring product use and personal protective equipment (PPE) compliance will be proposed to compare each inpatient ward at the PVAMC against each other because they are of similar and different sizes in terms of number of operating beds (OB) and bed days of care (BDOC).
Historical data on HAIs, admission numbers and bed days of care, are all aggregated and summary report data do not involve patient medical records.

Design
In the literature, there are several causes to infectious diseases. However, poor adherence to hand hygiene compliance is a leading cause of HAIs (WHO, 2009).
Effective and efficient hand hygiene compliance monitoring will not only control and prevent HAIs, but also helps to serve "multiple functions: system monitoring, incentive for performance improvement, outbreak investigation, staffing management, and infrastructure design" (WHO, 2009, p.158). There are leading risk factors for poor adherence to hand hygiene compliance (see Table 2.1). In this study, seven risk factors, which are reported in Table 2.1 and by WHO (2009), are chosen to be studied, which are: • HCWs gender.
• Type of unit.
• Patient isolation status. A best estimation of the true rate of hand hygiene compliance and its confidence interval will be measured using the sample distribution of a proportion. A power analysis will be applied to determine the effective sample size of the group study and the total number of observations required for meaningful hypothesis testing.

Study Hypotheses
Next, several hypotheses will be tested, to study the risk factors for poor compliance.
Hypothesis 1: There is no difference in hand hygiene compliance rate by employee gender.

Methods
In order to estimate the true magnitude of patient contact, or the correct number of opportunities for hand hygiene before and after contacting a patient by HCWs, a Patient-Contact data collection form will be used by several healthcare professional Each HCW will self-report the total number of patient contacts for one complete week starting on a Monday night shift and ending on a Sunday evening shift for all shifts, day, evening and night. This will be completed once during the year. One month prior, and one month overlapping the one week of self-reporting patient-contact data collection, a URI research team member will also be present on the hallway of inpatient wards to assess the participants during the self-report data on patient contacts that comes from the doctors and nurses in the inpatient units, 5B, 6B and ICU at PVAMC. The Patient-Contact form (Appendices C, D, E, F, G, H, I, J and K) is designed to capture the following information: employee discipline and gender (i.e. a female attending doctor), date, day, time and shift of patient contact, patient gender, inpatient unit (5B, 6B and ICU), patient room and which bed (bed A or B, left or right, door to window). There will be a unique form for each inpatient unit and for each of the 3 shifts, for a total of 9 sub-forms (Appendices C, D, E, F, G, H, I, J and K). To protect employee and patient privacy, names are not included on the form and will not be reported. Each form will be coded by a letter that refers to the employee discipline (D for Doctor, N for Nurse, etc.) followed by a unique number (D001, N005). No one would know that code except the URI research team. A spreadsheet will be created to only include names and unique assigned codes of doctors and nurses that will participate in the study to self-report their patient-contacts. This spreadsheet will be electronically stored in a restricted folder on the secure research server at the PVAMC that cannot be accessed by anyone except the URI research team. This spreadsheet and its contents will be created after the consent form (Appendix L) is signed by the participants. The participant nurses will be approached individually by the URI research team before the start or after the end of their work shift at each inpatient unit for recruitment and for obtaining the informed consent and their names and unique codes will be recorded in the spreadsheet at the PVAMC. The participant doctors will be approached by the URI research team individually at their offices for recruitment and for obtaining the informed consent and their names and unique codes will be recorded in the spreadsheet at the PVAMC. Then, a printout copy of the patientcontact form will be distributed to each participant. There will be a labeled and locked mailbox at each inpatient unit (5B, 6B and ICU) where participants can drop their patient-contact forms at the end of each shift or day. This will ensure the privacy of participants. The URI research team will be the only one who has access to the locked mailboxes. The URI research team will remotely access the secured folder via the VA Citrix Access Gate (CAG) from URI campus to conduct the required analysis and write up the PhD dissertation on a URI computer.
For two months, one month prior to self-reporting patient-contact data collection and one month overlapping with the week of patient-contact data collection, secret observers, who are HCWs at PVAMC, will be asked to observe hand hygiene compliance at inpatient units (5B, 6B and ICU). During the same months, the URI research team member will also participate in observing hand hygiene compliance at inpatient units (5B, 6B and ICU). In order to capture an effective sample size of hand hygiene indications, 40 secret observers will be recruited. This number is not uniformly distributed per unit per shift. An updated hand hygiene monitoring data collection form (Appendices M, N and O) will be used. The pilot study will focus on three inpatients units, two healthcare professional categories, and two out of five moments for hand hygiene, so the updated hand hygiene monitoring data collection form will capture partial information of the online hand hygiene compliance form (Appendices P and Q) that has been used by the PVMAC, and add more. However, the partial information is valid to be entered in the online hand hygiene management tool (Appendices R and S), which will remove a waste of using the online hand hygiene Patient gender, health status, shift, room and bed numbers are new pieces of information that is not provided in the online hand hygiene monitoring tool used by the PVAMC for hand hygiene. Each updated hand hygiene monitoring data collection form will be coded by a letter that refers to the secret observer (S) and will be followed by a unique number (S001), which only the URI research team will know. A spreadsheet will be created to only include names and unique assigned codes of the secret observers that will participate in the study using the updated hand hygiene monitoring data collection form. This spreadsheet will be electronically stored in a restricted folder on the secure research server at the PVAMC that cannot be accessed by anyone except the URI research team. This spreadsheet and its contents will be created after the consent form (Appendix T) is signed by each participant. The participant secret observers will be approached by the URI research team individually at their offices for recruitment and for obtaining informed consent and their names and unique codes will be recorded in the spreadsheet at the PVAMC. Then, a printout copy of the updated hand hygiene monitoring data collection form will be distributed to each secret observer. There will be a labeled and locked mailbox in the Infectious • Analyzing the occurrence of infectious disease records to see which statistical models fit best and are most applicable.
• Reviewing procedures for collection, recording, and reporting of HAIs and hand-hygiene.
• Assessing any correlations between HAIs data and hand-hygiene compliance records.
• Reviewing calculations of HAIs rates conducted by the VA.

Data Required
The following data is requested from the PVAMC: • 5 years of data of occurrences of identified HAIs of interest (preferably daily, but weekly or monthly data could be acceptable if that is all that is available).
• 3 years of data of purchasing or re-stocking data on hand-hygiene, Purell and Soap.
• 4 years of data of "Secret Observers" hand-hygiene compliance records and dataset.
• Performance metrics, The Strategic Analytics for Improvement and Learning (SAIL) Value Model data, used by VISN 1 and the national VA organization for rating PVAMC on matters related to HAIs and hand hygiene.
• 3 years of data of personal protective equipment (PPE) (Gloves, Gowns and Masks) inventory data.
• Data related to treatment costs of key HAIs, and hospitalization or length of stay LOS estimates.
The data on HAI occurrences, costs, and LOS are all aggregate summary data in existing reports and do not involve specific patient medical records. These required data will be electronically sent to and stored in a restricted folder on the secure research server at the PVAMC. The PVAMC infectious disease preventionist at the Infectious Disease Department will be asked to send these required data to the secured folder. The URI research team will be the only one who have access to the secured folder. The URI research team will remotely access this secured folder via the VA 62 Citrix Access Gate (CAG) from URI campus to conduct the required analysis and write up the PhD dissertation on a URI computer.

Evaluation
After collecting the data for patient-contact, secret observers for hand hygiene, Log-tracking, purchasing, and restocking of Purell and Soap, PPE (Gloves, Gowns and Masks) inventory data, tracking HAIs per operating bed per inpatient unit, and other requested dataset from the PVAMC such as HAIs and SAIL, the following statistical analysis will be conducted using RStudio Version 1.0.1.136 on Mac, which is a freeware open source tool that is used to statistically analyze data because it has no cost and runs on both Mac and Windows Operating Systems.
1. Patient-Contact and secret-observer datasets will be analyzed to identify the bestfit statistical distributions that explain the data.
2. Mean, median and mode and other descriptive statistics.
3. The true hand hygiene compliance rate will be estimated.
4. Hand hygiene compliance rate and its trends will be broken down by shifts (Day, Evening and Night), professional category (Doctor vs. Nurse), inpatient unit (5B, 6B and ICU), days of the week (weekdays vs. weekend), and healthcare workers' gender (male vs. female).
5. Hand hygiene compliance rate will be analyzed over time to identify trends.
The research team will submit a report on hand hygiene analysis as well as a new proposed indirect hand hygiene manual monitoring of product use method and a new proposed indirect method of measuring PPE compliance at the end of the research to the PVAMC quality management and infectious disease department. In addition, the 63 research team plans to present and publish the research findings in conferences and journals related to infectious diseases, hand hygiene, ranking systems in healthcare, performance improvement, and industrial engineering.

Study Population
All VA doctors and nurses who work at the inpatient units, 5B, 6B and ICU, and within the age group of 18-90 will be asked to self-report their patient-contacts. The patient contact data represents the target population of the study. There are no additional criteria to select the participants for the patient-contact self-reporting.
Recruited secret observers will be any VA employees who agree to be a secret observer for the study, but are not part of the patient-contact self-reporting group, who will be asked to randomly observe the patient-contact self-reporting group during the time of the study. The collected observations represent the study population. The

CHAPTER PATIENT CONTACT ANALYSIS
This chapter provides a descriptive statistics summary about patient contact data and also answers the two main objectives for which patient contact data collection is conducted at the first place: • An approximation of patient contact population per inpatient ward.  NS is usually on the floor to finish the nursing degree and get trained. There is usually one NS at each ward except ICU, which has none.

Medical Group
• Attending The medical group usually is assigned to patients all over the three inpatient wards. Attending and fellow are usually permanent employees at the PVAMC; however, residents, interns and medical students are floating in and out on a monthly basis for practicing and training. There are several medical doctorate groups, pulmonary, cardiology and GI, that these medical subgroups belong to. Basically, after finishing medical school, medical students spend one full year of training to be transitioned into what is medically called a post-graduate year (PGY) of 1 to 10 years at most divided as follows: • 1 st Year: Intern.
Practicing medicine and contacting patients unsupervised cannot be permitted until experience is gained by going through these years. Generally, Pulmonary requires finishing the first two years; Cardiology and GI require finishing the third year.

Health Administration Service
• Secretary There is a secretary at each ward during each shift who takes care of entering the newly admitted patient's information into VistA, which is then entered on the Bed-Board Management System (BMS) manually.

VistA is "The Veterans Health Information Systems and Technology
Architecture Information System -is a Health Information Technology ( • Green: used for unoccupied bed/room.
• Blue: used for occupied bed/room by male patient.
• Pink: used for occupied bed/room by female patient.
• Orange: used for booked or reserved bed/room for transferred or admitted patient who is on the way to the ward.
• Red: used for bed/room with environmental issues (water leak or no heat).

Environmental Management Service (EMS)
• Housekeepers Each inpatient ward has several EMS employees whose job it is to clean the hallway, clean patients' rooms and make beds upon discharging patients. During day and evening shifts, rooms with non-isolation and with isolation signs, except for C.Diff, are regularly cleaned once with bleach with or without the existence of patients in the rooms. However, rooms with C.Diff are cleaned twice, with regular bleach first and secondly with special cleaning chemicals. Sometimes UV equipment is used to disinfect the rooms with C.Diff but it is not required. It takes about 5 minutes to clean a bathroom, and 5 minutes to clean one side of the room for each bed, which in total takes 15 minutes for the whole room. which means the bed to the left is A and the bed to the right is B, or a door-to-window type, which means the bed by the door is A and the bed by the window is B.

Patient Contact Participants Summary Statistics
Healthcare groups of interest to the research study are: •

Patient Contact Objectives
After receiving all the patient contact responses, data are entered in Excel, analyzed in R, and the patient contact frequency is reported per inpatient ward by healthcare category and subcategory, medical (attending, resident, intern and medical student) versus nursing groups (RNP, RN, LPN and CNA). Patient contact frequency per inpatient ward and wards combined is summarized in Table 4.4 and Table 4.5. At first glance and as expected, the nursing group has higher patient contact frequency than medical group on average. On average, medical versus nursing patient contact frequency is 7 and 43 (14% versus 86%), 7 and 51 (12% versus 88%), and 8 and 103 (7% versus 93%) for 5B, 6B and ICU respectively as seen in Table 4.4. On average, inpatient wards combined, medical versus nursing patient contact frequency is 7 and 54 (12% versus 88%) as seen in Table 4.5. Interestingly, ICU nursing group patient contact frequency is significantly higher than that of 5B and 6B even though RN is the only nursing subcategory that is available at ICU. ICU patients' health status could be one significant reason. ICU nurses need to visit patients frequently.
Over one complete week, BDOC is 147 (10 female and 137 male), 181 (7 female and 174 male) and 21 (5 female and 16 male) for 5B, 6B and ICU respectively. In summary, to answer the two main objectives for which patient contact data collection is conducted in the first place the following calculations are taken place. Of note, patient contact is conducted over one complete week. Thus, all calculations and averages are done per week.

An Approximation of Patient Contact Population per Inpatient Ward:
To identify how large the Before or After patient contact population is, total patient contact frequency, neglecting healthcare category or subcategory, is summed per inpatient ward and inpatient wards combined. Based on Table 4.4 and Table 4.5, the Before or After patient contact population is 5863, 7578 and 2014 hand hygiene opportunities for 5B, 6B and ICU respectively and 15455 hand hygiene opportunities for inpatient wards combined collectively. To account for both hand hygiene moments, the Before and After patient contact moments, the total population is then 11726 (2*5863), 15156 (2*7578) and 4028 (2*2014) hand hygiene opportunities for 5B, 6B and ICU respectively and 30910 (2*15455) hand hygiene opportunities for inpatient wards combined collectively. Patient contact population is summarized in

Average Patient Visits per Inpatient Ward:
To identify the average patient visits per inpatient ward over one complete week, for each healthcare subcategory the average patient contact frequency is divided by total patients seen and is summed per healthcare category and summed per inpatient ward as seen in Table 4.4 and Table 4.5. Patient contact is neither summarized by working shift nor by isolation status for the moment.
Average patient contact frequency visit is summarized in Table 4 To identify the average patient visits per working shift per inpatient ward over one complete week, for each healthcare subcategory the average patient contact frequency is divided by total patients seen and is summed up per healthcare category and summed up per working shift per inpatient ward as seen in Table 4           To identify the average patient visits per isolation status per inpatient ward over one complete week, for each healthcare subcategory the average patient contact frequency is divided by total patients seen and is summed up per healthcare category and summed up per isolation status per inpatient ward as seen in Table 4.10, Table   4.11, Table 4.12 and Table 4.13 for ICU, 5B, 6B and inpatient wards combined respectively.
At 5B, the medical group total average patient contact frequency visits is 7 and 7 for isolation and non-isolation status respectively. On the other hand, the nursing group total average patient contact frequency visits is 47 and 42 for isolation and non- • Grouping by inpatient wards only and ignoring working shift or isolation status.
• Grouping by inpatient wards and by working shift and ignoring isolation status.
• Grouping by inpatient wards and by ignoring isolation status and working shift.
Thus, it would be better if total average patient contact visits is generated by

Inpatient Wards
Each inpatient ward is explored individually in terms of locations and how many Soap-Purell dispensers are available, how many bed(s) exist at each patient-room and locations of personal protective equipment, which is (PPE) "protective equipment, such as approved head and hair coverings, face shields, safety glasses/goggles, long cuffed rubber/vinyl decontamination gloves, impervious gowns, and shoe covers that are utilized to protect the employee from the environment." ( Totally, there are 26 Soap dispensers, 5 located on the hallway and the remaining located inside bathrooms of patients' rooms. There are 29 Purell dispensers, 12 located on the hallway and 17 located inside patients' rooms but outside of the bathrooms.
Each side of the Y letter consists of an equal numbers of patients' rooms. In addition there are two Anterooms and one Linen room and a conference room inside the ward.

Inpatient Ward 6B
6B ward is an acute care unit that is licensed to 27 beds and has a capacity of 27 beds. There are 12 rooms with double beds (A and B) and 3 rooms with a single bed.
A list of patient's room numbers and bed type A or B of the ward is found in Linen room and a conference room inside the ward.

Inpatient Ward ICU
The ICU ward is an acute care unit that is licensed to 8 beds and has a capacity located on the hallway and 3 located inside three patients' rooms with two rooms with no dispensers. In addition there is a conference room inside the ward.

Primary Inventory and PPE
The PVAMC has one primary inventory for medical and surgical items that is The Personal Protective Equipment (PPE) is a Medical-Surgical item that is considered an example of an auto-generated ordered item stocked from inventory in the primary locations. PPE includes, but is not limited to: • Nitrile Examination Gloves: comes in four different sizes (Small, Medium, Large and X-Large) and is powder-&-latex free and textured and for single use only.
• Gowns. At the PVAMC, an inventory employee takes care of filling in the supply closet at each inpatient unit on a daily basis with gloves, masks and gowns. An inventory cart is used to deliver inventory items to ease the movement of large boxes. Two large elevators are designated for such job besides patient transfer and bed movement from to the room and the required care such as x-rays. It is CNA's job to fill in wallmounted supplies and supply drawers beside each patient room on the floor with gloves, gowns and mask.

Isolation Precautions
There are two types of isolation precautions.  • Administer medications, • Perform skin assessment, • Draw blood, • Assist in the Activities of Daily Living (ADLs) such as eating, bathing, dressing, toileting and walking, • Assist in incontinent and human excreta.

Warehouse
The Warehouse at the PVAMC is located in building 6, which is a maintenance

Physical Contact with Patients
Patients, in general, get exposed to different people who physically have contact with them and others who have to be present inside the room either for medical reasons or non-medical reasons. In the tree diagram shown in Figure 5.  "The System has the ability to be used on tablets or personal electronic devices; however, this has not yet been piloted on a significant scale." (Strymish & Gupta, 2012). At the PVAMC, it is more convenient for the observers to use a printout template for the Hand Hygiene and Precautions Compliance Monitoring Tool (Appendix P) that is filled out by an observer and then the data is entered into the system manually by the same observer from their VA computer, if he or she is registered on the system, or by an infection preventionist who has access to the online form. Any HCWs can get permission to be a direct observer (Secret observer) and can get an access to the Hand Hygiene Management Tool. The infection preventionist recommend adding an option for stethoscope dedicated in patient room and stethoscope cleaning with sanitizing wipe post contacting a patient into the system, as seen in (Appendix Q).
To extract and export data and outputs, there is an option called Run Report, (Strymish and Gupta, 2012 Communication, 2015). Third, an observer or anyone who has access to the hand hygiene event data input form must transfer the written data from the hand hygiene and precautions compliance monitoring form.
The hand hygiene online management tool has a three-option form when entering the collected data online: • Option One is Only Hand Hygiene Compliance Observations: There is no classification between isolation and non-isolation observations. Observations could belong to one or the other.
• Option Two is Only Isolation Precautions: There is no classification between isolation and non-isolation observations. Observations could belong to one or the other since gloves, for example, are sometimes required during nonisolation care.

• Option Three is Both Hand Hygiene Compliance Observations and Isolation
Precautions: There is no classification between isolation and non-isolation observations since observing both hand hygiene and PPE compliance together could also occur during non-isolation events.
The first option, only hand hygiene compliance observations, is designated for hand hygiene observations for both isolation and non-isolation patients where PPE is not observed. The first option is the most common option evaluated at the PVAMC.

PVAMC Direct Hand Hygiene Observation Method
At the PVAMC, the only available method to track, assess and measure the hand hygiene compliance of HCWs is via conducting direct observations (Secret Shoppers or Secret Observers). There are about 1038 full-time employees with 32 subspecialty clinics and 119 authorized beds with 73 operating beds (PVAMC, 2015). Compared with other VA medical centers, the PVAMC is considered to be a medium VA medical center in terms of facility size. The direct observation method is now described, including areas for improvement.
The direct observation method is conducted as follows. An observer, assuming the secret observer is covert and who can be any employee at the PVAMC, takes a round at anytime in any healthcare unit (inpatient or outpatient wards), and observes other healthcare professional categories to see if they, the observed ones, practice hand hygiene at key hand hygiene moments, for example, before or after touching a patient.
The observation might capture whether the observed ones comply to precautions signage by, for example, wearing personal protective equipment (PPE) such as gloves, gowns and masks before going into an isolation room. The secret observer has to have an excuse to enter patient rooms and be there with the medical teams. Typically, each observer carries a hand hygiene and precautions compliance monitoring tool form (Appendix P) to fill out as an opportunity occurs. However, observers at the PVAMC sometimes prefer to memorize what happens and then fill in the form either at their offices or while heading to observe more.
If a hand hygiene opportunity is detected and not practiced, it is recorded as none (non-compliance). On the other hand, if an opportunity is detected and practiced, it is recorded as waterless, referring to alcohol-based hand rub (Purell), or as wash, referring to soap and water (compliance). Sometimes if an opportunity is detected and the observer is able to notify and remind the observed one to practice hand hygiene, the observed one does not get credit whether he/she practices or not, this opportunity is recorded as none (non-compliance). However, it is better to report that as no compliance and report what hygiene product is used. In contrast, sometimes observers do not notify or remind the observed ones to practice and they also record the event as non-compliance. In the first case, when an observed one is notified and does practice hand hygiene, there is no option on the form to tell which product is used because the opportunity is recorded non-compliance. In the latter case, it does not matter since the observed one does not actually practice hand hygiene.
The physical hand hygiene and precaution compliance monitoring form includes options for correctly using personal protective equipment (PPE) and isolation; however, there is no subheading showing what type(s) of PPE is used or should be used. In addition, the form only captures two moments for hand hygiene, which are before and after touching a patient. There is a space for comments on the form to record other captured moments and fill them out on the online hand hygiene event data input form. The physical hand hygiene and precaution compliance monitoring form has space to record only 13 hand hygiene events to record. No personal information of the observed ones is included on the form. Basically, the physical hand hygiene and precaution compliance monitoring form does not resemble the online hand hygiene event data input form at the PVAMC.

Facility-Wide Retrospective Hand Hygiene Compliance
Direct observations data is provided and extracted by an infection preventionist at the PVAMC from the hand hygiene management tool system. The data goes back to   However, it is hard to judge the lower hand hygiene adherence rate of isolation observations from option three because that cannot be compared to the higher hand hygiene adherence rate from isolation and non-isolation observations from option one.
Nevertheless, it is expected that HCWs give more attention to their hand hygiene practices during patient care in isolation rooms. Sometimes HCWs do not feel the need to use hygiene products before wearing gloves for patient contact. Additionally, the hand hygiene adherence rates were found to be equivalent for before touching a patient moment for both isolation (81%) and non-isolation rooms (82%); however, the rate for after patient contact for non-isolation rooms (92%) was higher than for isolation rooms (86%). This could imply that HCWs at the PVAMC believe or have faith on PPE to prevent HAIs transmissions and were not necessarily need to practice hand hygiene while wearing gloves. Based on the information above, observations that are associated to either Soap or Purell hygiene product are converted to hygiene product cartridges as depicted in the two most frequent hand hygiene moments captured are before and after touching a patient, as expected. The overall hand hygiene adherence rate for before touching a patient is about 82% while the rate after touching a patient is about 92% as seen in Table 5.2. This could imply that HCWs at the PVAMC could slightly underestimate practicing hand hygiene before touching a patient and value practicing hand hygiene after touching a patient. As expected, the numbers of observations captured for the other three moments for hand hygiene are quite small especially for after blood/body fluid exposure risk and before clean/aseptic procedures. In general, the compliance rate is above 80% for all moments, as seen in Table 5   before touching a patient, 8% higher for after touching a patient and 44% higher for after touching patient surroundings.   observations. Nantucket has no information regarding whether direct observations method is conducted there or not. based on a sample size of 1030 observations. Of note, the percentages shown in Figure   5.11 are non-compliant percentages.   Facility-wide summarizing hand hygiene adherence rate by moments and employee class together reveals important facts as can be seen in Table 5.3 and Table   5.4. The medical group (attending, fellow) hand hygiene rate before touching a patient is moderate, 80% and high for after touching a patient 94%. On the other hand, the medical group (residents, interns and medical students) hand hygiene rate before and after touching a patient are disappointingly, 74% and 76% respectively. The nursing group (RN and LPN) hand hygiene rate before and after touching a patient are high, 85% and 96% respectively. Again, HCWs seem to slightly underestimate practicing hand hygiene before touching a patient and highly value that after touching a patient. Interestingly,

Inpatient-Wards Retrospective Hand Hygiene Compliance
After analyzing hand hygiene compliance facility-wide, the analysis is carried    Hand hygiene observations are also summarized by hand hygiene moments per inpatient ward as depicted in Figure 5.18. Figure 5.18 shows that the two most frequent hand hygiene captured moments are before and after touching a patient, as expected. The hand hygiene adherence rates for before touching a patient are 73%, 55%, and 76% for 5B, 6B and ICU respectively as seen in Table 5.5. It is considered very low for all inpatient wards especially for 6B. The hand hygiene adherence rates for after touching a patient are 88%, 83%, and 90% for 5B, 6B and ICU respectively as seen in Table 5.5. Rates after touching a patient outweigh rates before touching a patient across all inpatient wards. Again, this could imply that HCWs at the PVAMC could slightly underestimate practicing hand hygiene before touching a patient and  value practicing hand hygiene after touching a patient. As expected, the observations captured for the other three moments for hand hygiene are quite small, especially for after blood/body fluid exposure risk and before clean/aseptic procedures.     Year     touching a patient is 81%, 54% and 70% for 5B, 6B and ICU respectively. The nursing group (RN and LPN) hand hygiene rate before touching a patient is better than the medical groups but still is low for 6B, 82%, 67% and 91% for 5B, 6B and ICU respectively. On the other hand, the nursing group (RN and LPN) hand hygiene rate after touching a patient is very high, 93%, 87% and 98% for 5B, 6B and ICU respectively. Again, HCWs seem to underestimate practicing hand hygiene before touching a patient and value that after touching a patient.
Interestingly, Figure 5.22 shows that the most frequent hand hygiene moments conducted by EMS are after touching patient surroundings, before touching a patient and after touching a patient. However, rates are very low for all three moments across inpatient wards. Additionally, data analysis shows that there is only one observation corresponding to Before Clean/Aseptic Procedures moment for 5B and 6B and EMS has no observations after body fluid exposure risk moment though their role is to clean patient rooms and to make beds with or without the existence of a patient. In general, EMS has to be considered for further hand hygiene observations and analysis.  Hand hygiene rates per inpatient-wards by employee class, hand hygiene moments and hygiene products is depicted in Figure 5.23 and summarized in Table   5.13, Table 5.14 and   Recall that the hand hygiene management tool at the PVAMC consists of three different forms: • Option One is Only Hand Hygiene Compliance Observations: There is no classification between isolation and non-isolation observations. Observations could belong to one or the other.
• Option Two is Only Isolation Precautions: There is no classification between isolation and non-isolation observations. Observations could belong to one or the other since gloves, for example, are sometimes required during nonisolation care.

• Option Three is Both Hand Hygiene Compliance Observations and Isolation
Precautions: There is no classification between isolation and non-isolation observations since observing both hand hygiene and PPE compliance together could also occur during non-isolation events.

Under this section, data associated to (Option Three) Both Hand Hygiene
Compliance Observations and Isolation Precautions is analyzed. Facility wide, there are 440 PPE and hand hygiene isolation observations in total which all belong to isolation events. The breakdown of the data by year and isolation types is seen in Table 5.19 and depicted in Figure  Year Total  Contact  Droplet  Airborne  2012  67  3  1  71  2013  91  6  0  97  2014  93  8  2  103  2015  154  14  1  169  Total  405  31  4  440   Table 5.

Figure 5.24 Facility Wide Hand Hygiene and PPE Observations by Precaution Types and Year
After removing data entry errors, the data was filtered to include only employee classes of interest (attending, fellow, resident, intern, medical student, nurse practitioner, registered and licensed practical nurses, nurse student and environmental management service), hand hygiene moments of only before and after touching a

Contact Precautions
Patients    It is necessary that the total samples when PPE is and is not present with or without practicing hand hygiene is analyzed across inpatient wards. Based on Figure   5.27, there are (before and after moments combined) 87% This is analyzed in depth when the form Only Precautions data is analyzed in section 5.3.4.

Droplet Precautions
Patients who are on droplet precautions could have, for example, Flu or Mumps.
For complete lists, please see the back of the droplet precautions sign in Appendix EE.
The droplet precautions sign states that masks are required all of the time when providing care. Gloves, gowns and goggles for eye protection are required as needed.
Additionally, hand hygiene has to be practiced before and after touching a patient and before and after wearing gloves if gloves are required.
Following the lead of the contact precautions analysis, first is with the analysis when employee class is not included.
masks compliance with no perception to hand hygiene for 5B, 6B and ICU respectively. Masks compliance is considered high for 5B and 6B wards except for ICU. Masks compliance is not evaluated per hand hygiene moments because masks is required before touching a patient and entering the room and masks compliance means whether it is present or not at the moment of care. This is analyzed in depth when the form Only Precautions data is analyzed in the next section. Of note, there is one incident reported where all PPE are assumed to be not applicable even masks, which is totally against the droplet precautions requirements.

Figure 5.28 Inpatient-Wards Hand Hygiene and PPE Observations for Droplet Precautions
Next the data is analyzed for when employee class is included.

Airborne Precautions
Patients who are on airborne precautions could have, for example, Tuberculosis

Retrospective PPE Compliance
Sometimes HCWs are observed during patient care based on whether they wear the right PPE at the moment of care without evaluating hand hygiene compliance.
Such observations are used to report PPE compliance alone. Under this section, data associated to the only Isolation Precautions form of the hand hygiene management tool is analyzed. Facility wide, there are 964 PPE isolation observations in total. The breakdown of the data by year and isolation types is seen in Table 5.21 and depicted in  observations. Droplet precautions come next with 45 observations. Airborne precautions are last with only 8 observations. There is a significant difference between the total samples of contact versus droplet and airborne precautions combined.
Year   After removing data entry errors, the data was filtered to include only employee classes of interest (attending, fellow, resident, intern, medical student, nurse practitioner, registered and licensed practical nurses, nurse student and environmental management service) and locations of inpatient wards (5B, 6B and IC), and then the data drops down from 964 to 696. The data breakdown based on year, inpatient wards and isolation type is seen in On the other hand, for contact precautions with no masks, the total combinations, for having two PPE products, gloves and gowns, each with three possible outcomes yes, no and not applicable (NA), are 9: Gloves Yes, No, NA * Gowns Yes, No, NA = 3 * 3 = 9 Based on the available data, the 27 possible scenarios drops down to 11 scenarios for contact precautions when masks item is included, 7 scenarios for droplet precautions and only 3 scenarios for airborne precautions. On the other hand, 9 possible scenarios drops down to 5 scenarios for contact precautions when masks item is excluded.

Contact Precautions
The first analysis is when masks are included and employee class is not included.

Droplet Precautions
Following the lead of contact precautions analysis, first is the analysis when employee class is not included.  The nexst step is to analyze the data when employee class is included. Figure   5.37 shows that only RN and LPN, the medical group (attending and fellow, resident, intern and medical student) and EMS are observed based on the data. It is hard to report the medical groups and EMS PPE compliance due to their sample size.  188 6B and ICU, respectively. There is large variability in RN and LPN PPE compliance across inpatient wards. However, mask compliance with the necessity of wearing gloves and gowns cannot be judged because of the low sample size.

Airborne Precautions
Following the lead of contact and droplet precautions analysis, next is the analysis when employee class is and is not included. Figure 5.38 shows that only RN and LPN is observed based on the data. Thus, the analysis is carried out by including employee class since all data is reported on RN and LPN only.

Observational Study of Hand Hygiene Compliance
After conducting a retrospective analysis of the direct hand hygiene observations method based on the PVAMC data, an analysis of the direct hand hygiene observations method based on data collected by the URI research team is conducted.
Summary statistics are reported and the researchable questions the hypotheses testing are addressed.

Descriptive Statistics
The The secret observers including the URI research member were able to collect 2432 hand hygiene observations, in total, during September and October as seen in    for After Touching a Patient.
In terms of inpatient wards, the hand hygiene collected observations are 885 (36%), 1212 (50%) and 335 (14%) for 5B, 6B and ICU, respectively as seen in Figure   5. 39 In terms of isolation status, the hand hygiene collected observations are 1018 (42%) and 1414 (58%) for isolation and non-isolation respectively as seen in

Hand Hygiene Population and Compliance
After providing summary statistics of the hand hygiene compliance, the next is to provide hand hygiene compliance estimation and to report a 95% confidence interval per inpatient ward and all inpatient wards combined.
Based on patient contact data in Chapter 4 as seen in Figure 4  In September, by comparing the monthly total sample size in Figure 5.42 with the monthly total patient contact in Figure 5 Figure   5.41, Isolation BDOC is higher in October than in September except for 5B. Isolation BDOC could also be equal or higher than non-isolation BDOC since there are other infections that require patient isolation, which are not part of the research study.
In calculating and developing confidence interval estimates for the population parameter hand hygiene compliance, the type of population has to be determined first.
The type of population can affect the width of the confidence interval and therefore, it can increase the accuracy of the hand hygiene compliance mean estimation. In addition, the type of population can determine how large the sample size is (power analysis and sample size). In statistics, according to Kozak (2008), there are two types of population, which are finite or infinite population. "Each population the elements of which exist in a particular time is finite" (Kozak, 2008, p.60). In the case of finite population, an adjustment of the standard error of the confidence interval is made by using the finite population correction (fpc) factor !!! !!! (Berenson, Levine and 204 Krehbiel, 2002) where N is population size and n is sample size. The criteria to decide whether fpc has a significant or insignificant effect on the confidence interval width is as follows (Berenson, Levine and Krehbiel, 2002): With two assumptions exist, "the population variance ! ! is unknown and Population is normally distributed or the sample size is large" ( where ! ! is the sample proportion of success, n is the sample size or the total trials, N is the population size and !! ! is the value corresponding to a cumulative area of (1 − !) from the standard normal distribution (Berenson, Levine and Krehbiel, 2002).
In the current research study, though the population of the patient contact is considered finite because it is countable and exits in a particular time, for one complete week of October, the collected sample size (n) at any one of the inpatient wards or at all wards combined divided by their population size (N) is not even close to 2%. Thus, the criteria above states that any correction made to the confidence interval is insignificant. As a result, the regular monthly confidence interval is calculated instead for each inpatient ward and all wards combined based on the following equation using ! = 0.05 and the results are reported in Table 5.23.
205 By looking at Figure 5.43, the hand hygiene compliance mean could go as low as 40%, which belongs to 5B during September, and as high as 68%, which belongs to 6B during September. The Hand hygiene compliance mean at 5B stays the same but with higher variability during September. On the other hand, there is a huge drop at 6B from September to October; but the lower hand hygiene compliance of 6B is still higher than both 5B's. There is an increase in hand hygiene compliance mean at ICU from September to October; however, ICU has the highest variability in the study due to the small sample size. Based on all inpatient wards combined, the PVAMC's hand hygiene compliance mean is considered very low during the months of the study, 55% and 48% for September and October, respectively. When correlating the weekly hand hygiene compliance mean with the weekly BDOC per inpatient ward and all wards combined during September and October as seen in Figure 5.44, correlation is found except at all wards combined. There is a strong negative correlation between the weekly hand hygiene compliance mean and BDOC at 5B (−0.90), 6B (−0.55) and ICU (−0.59). As BDOC increased the hand hygiene compliance decreased.

206
Now, two important scenarios are created and statistically tested, which lead to a future recommendation for the PVAMC: • First: The PVAMC claims that their hand hygiene compliance is at its desired level, acceptably high, and their belief is entirely based on the very low number of HAIs in addition to the direct method observations that were collected over five years since April 2012. These observations, however, are actually a very small sample. The question was asked to determine how large the sample size should be to detect an effect of either 5% to reject such a claim. A 90% hand hygiene compliance mean is assumed.
• Second: The investigator or the URI research team believes that the PVAMC's HCWs take a 50-50 chance of practicing hand hygiene and would like to assess whether the hand hygiene compliance mean is either higher or lower than 50%. Is there evidence of statistically higher or lower hand hygiene compliance as compared to the study's hand hygiene compliance results? How large should the sample size be to detect an effect of either 5% to reject such a null hypothesis though the samples that were already collected?
Though the study's results already show that the PVAMC's hand hygiene compliance is not at its best and is not very close to 90%, statistical evidence has to be shown for the two assumptions, 90% and 50%, separately. The following hypothesis tests are created for all inpatient wards combined and per month, September and October as seen in Table 5.24 for 90% for the first scenario and in Table 5.25 for 50% for the second scenario. The total trials in September are 1161 with number of success of 648 and the total trials in October are 1271 with number of success of 616. Based 207 on the p-value in Table 5.24, the null hypotheses of the two-tailed and the lower-tailed tests are rejected based on a p-value that is lower than ! = 0.05 and failed to reject the null hypothesis of the upper-tailed test based on a p-value that is larger than ! = 0.05 for September and October. There is statistically significant evidence at ! = 0.05 to show that the monthly hand hygiene compliance mean at the PVAMC is not equal to 90% and indeed is lower than that.
One Sample Hypotheses Test September (0.55) Based on the p-value in Table 5.25 for September, the null hypotheses of the two-tailed and the upper-tailed tests are rejected based on a p-value that is lower than ! = 0.05 and failed to reject the null hypothesis of the lower-tailed test based on a pvalue that is larger than ! = 0.05. There is statistically significant evidence at ! = 0.05 to show that the monthly hand hygiene compliance mean for September at the PVAMC is not equal to 50% and indeed is larger than that. This conclusion was already proved based on the collected sample size during September (55%).
Based on the p-value in Table 5.25 for October, all null hypotheses are failed to reject based on a p-value that is larger than ! = 0.05. There is statistically insignificant evidence at ! = 0.05 to show that the monthly hand hygiene compliance mean for October at the PVAMC is not equal to, larger or lower than 50%. However, it is marginal insignificantly for a lower-tailed test with a p-value of 0.07, which is not that much larger than ! = 0.05. This conclusion is already proved based on the collected sample size during October (48%).
One Sample Hypotheses Test September (0.55) October (0.48) p-value Two-Tailed Test Lower-Tailed Test Upper-Tailed Test ! ! : ! = 0.5 ! ! : ! > 0.5 0.0003279908 0.9230721 There are two ways to determine how large of a sample is needed to have 95% confidence that an estimate of hand hygiene compliance is within a certain amount of error percentage of the true value of the hand hygiene compliance. The first method is to use a preliminary sample such as the samples that were collected already during September and October. The second method is to use no preliminary sample in case of starting a new study investigation. In the first method the following formula is used In the second method the following formula is used

Hypothesis Testing
This subsection includes results from the hypotheses testing that was described in the methodology section in Chapter 3. These eight hypotheses deal with identifying any risk factor(s) for poor adherence to recommended hand-hygiene practices. Each hypothesis is tested individually, its sample proportion and p-value of the test are reported and a conclusion is drawn based on p-value as follows.
Hypotheses number 1, 2, 5, 6 and 8 are tested using a test for two proportions.
Results for the hypothesis test for two proportions (Hypotheses number 1, 2, 5, 6 and 8), with a 95% confidence interval and p-value are summarized together in Table 5.34.
Additionally, hypotheses number 3, 4 and 7 are tested using a test for several proportions, which is based on the Chi-Squared Statistic test. Results for the hypothesis test for several proportions (Hypotheses number 3, 4 and 7) and p-value are summarized together in Table 5.35.
Hypothesis 1: There is no difference in hand hygiene compliance rate by employee gender. Female's and males' compliant and non-compliant and total samples are reported in Table 5.26. The first hypothesis test is to estimate the difference between the proportion of subjects practicing hand hygiene by female HCWs and male HCWs, In other words, the hypothesis tests whether the hand hygiene compliance of female HCWs and male HCWs is different or not. Based on Table 5.34, ! ! is failed to be rejected and thus there is statistically insignificant evidence at ! = 0.05 to show that the hand hygiene compliance differs by HCWs' gender, female versus male, based on a p-value that is equal to 0.736 as seen in Table 5.34. Based on such a result, HCWs' gender is not identified as a risk factor for poor adherence to recommended hand-hygiene practices at the PVAMC. However, the hand hygiene compliance mean for both HCWs genders are considered low, 52% and 51% for female and male HCWs, respectively.

Hypothesis 2:
There is no difference in hand hygiene compliance rate by professional category, doctors vs. nurses. Doctor's and nurse's compliant and non-compliant and total samples are reported in Table 5.27. The second hypothesis test is to estimate the difference between the proportion of subjects practicing hand hygiene by doctors and nurses, In other words, the hypothesis tests whether the hand hygiene compliance of doctors and nurses is different or not. Based on Table 5.34, ! ! is failed to be rejected and thus there is statistically insignificant evidence at ! = 0.05 to show that the hand hygiene compliance differs by HCWs' job category, doctors versus nurses, based on a p-value that is equal to 0.300. Based on such a result, HCWs' job category is not identified as a risk factor for poor adherence to recommended handhygiene practices at the PVAMC. However, the hand hygiene compliance mean for both doctors and nurses are considered low, 53% and 51% for doctors and nurses respectively.

Hypothesis 3:
There is no difference in hand hygiene compliance rate by type of unit, 5B vs. 6B vs. ICU.  Table 5.28. The third hypothesis test is to estimate whether the proportions of subjects practicing hand hygiene at the inpatient wards, 5B, 6B and ICU, ! ! : ! !! = ! !! = ! !"# , are the same or whether the hand hygiene compliance for at least one of the wards is different. Based on Table 5.35, ! ! is rejected and thus there is statistically significant evidence at ! = 0.05 to show that the proportion of hand hygiene compliance differs by inpatient ward, 5B, 6B and ICU, based on a p-value that is equal to 0.00000026. Based on such a result, inpatient ward is identified as a risk factor for poor adherence to recommended hand-hygiene practices at the PVAMC. However, the hand hygiene compliance mean for all inpatient wards are considered low, 45%, 57% and 53% for 5B, 6B and ICU respectively. In addition, 5B is statistically significantly lower, but 6B and ICU are not statistically different. This  Table 5.29, since the absolute difference of 5B-6B is only greater than its critical range, and the absolute difference of 5B-ICU is marginally smaller than its critical range, the proportion of 5B is significantly different from the proportion of 6B and is marginally significantly different from the proportion of ICU.     Before's and after's touching a patient compliant and non-compliant and total samples are reported in Table 5.30. The fifth hypothesis test is to estimate the difference between the proportion of subjects practicing hand hygiene before touching a patient and after touching a patient, ! ! : ! !"#$%" − ! !"#$% . In other words, the hypothesis tests whether the hand hygiene compliance of before or after touching a patient is different or not. Based on Table 5.34, ! ! is rejected and thus there is statistically significant evidence at ! = 0.05 to show that the hand hygiene compliance differs by hand hygiene moment, before and after touching a patient, based on a p-value that is almost equal to 0. Based on such a result, hand hygiene moment is identified as a risk factor for poor adherence to recommended handhygiene practices at the PVAMC. In addition, the hand hygiene compliance mean for before touching a patient is very low, 38% and for after touching a patient is considered low too, 63%.

Hypothesis 6:
There is no difference in hand hygiene compliance rate by patient gender.
In other words, the hypothesis tests whether the hand hygiene compliance when caregiving is provided for female patients versus male patients is different or not. Based on Table 5.34, ! ! is rejected and thus there is statistically significant evidence at ! = 0.05 to show that the hand hygiene compliance differs when caregiving is provided for female patients versus male patients, based on a p-value that is almost equal to 0. Based on such a result, patient gender is identified as a risk factor for poor adherence to recommended hand-hygiene practices at the PVAMC. In addition, the hand hygiene compliance mean based on male patients is low, 51% and based on female patients is much better but still not at a good level, 70%.

Hypothesis 7:
There is no difference in hand hygiene compliance rate by working shift, Night, Day and Evening.  Isolation status's compliant and non-compliant and total samples are reported in Table 5.33. The eighth hypothesis test is to estimate the difference between the proportion of subjects practicing hand hygiene when caregiving is provided for patients on isolation versus on non-isolation, ! ! : ! !"#$%&'#( − ! !"#!!"#$%&'#( . In other words, the hypothesis tests whether the hand hygiene compliance when caregiving is provided based on patient's isolation status is different or not. Based on Table 5.34, ! ! is failed to be rejected and thus there is statistically insignificant evidence at ! = 0.05 to show that the hand hygiene compliance differs when caregiving is provided based on patient's isolation status, based on a p-value that is almost equal to 0.799. Based on such a result, patient's isolation status is not identified as a risk factor for poor adherence to recommended hand-hygiene practices at the PVAMC. However, the hand hygiene compliance mean based on patient's isolation status is low, 52% and 51% for isolation and non-isolation, respectively.

Hypothesis # Groups of Comparison
Vs. Nurses After Touching a Patient  Vs.

The Correlation of HAI and Hand Hygiene Compliance
It is important to study the relationship between hand hygiene compliance and HAIs. It is expected that HAIs go down as hand hygiene compliance goes up. Simply, the more HCWs adhere to recommended hand hygiene practices, the less likely HAI incidents will be. At the PVAMC, there were about 76 HAIs incidents between 2012 and 2015 combining all inpatient wards. There were 0 MRSA, 11 CAUTI, 1 CLABSI, 4 VAP and 60 C.Diff as seen in Table 5   Hawthorne effect, the observer bias and the selection bias) when this approach is implemented. However, it does not identify the true hand hygiene moments or actions.
Thus, the denominator for the calculation of hand hygiene compliance is missing.
However, some studies used some surrogate denominators to make this approach more reliable such as using patient-days or workload measures. Another drawback is that breaking down hand hygiene opportunities by professional categories is almost impossible since different people use the hygiene products, especially hand soap and alcohol-based handrub on the floor, such as patients and patients' families (WHO, 2009). A list of advantages and disadvantages of the indirect hand hygiene monitoring methods by measuring the consumption of the hygiene products is available in Table   6.1.

Advantages Disadvantages
• Inexpensive • Reflects overall hand hygiene activity (no selection bias) • Validity may be improved by surrogate denominators for the need for hand hygiene (patient-days, workload measures, etc.) • Does not reliably measure the need for hand hygiene (denominator) • No information about the appropriate timing of hand hygiene actions • Prolonged stocking of products at ward level complicates and might jeopardize the validity • Validity threatened by increased patient and visitor usage • No possibility to discriminate between individuals or professional groups

Objectives
Developing a method for estimating hand hygiene compliance based on the consumption of hygiene products used in three inpatient wards at the PVAMC is the objective of this portion of the study by determining a correlation between bed days of care (BDOC) and the consumption of products used, Soap and Purell. As a secondary measurement, the consumption of hygiene products leads to an estimate of the break down of hygiene products used in the inpatient wards, 5B, 6B and ICU at PVAMC, which then is compared with the monthly purchase and sales data of Purell and Soap from the warehouse.

Setting
The consumption of hygiene products used was measured at each inpatient ward.

Design and Method
A study is conducted to estimate hand hygiene compliance based on the consumption of hygiene products used at each inpatient ward individually and collectively. Each Soap and Purell dispenser at each ward is assigned with a unique integer number written on a sticker that is inside the dispenser to avoid any scratching or dropping off when cleaning walls and more importantly complying with the PVAMC rules of no wall-mounting. A Purell and Soap log sheet (Appendices U, V and W) was created to track the replacement of each Purell and Soap cartridge during the 10-month study on a daily basis. A log sheet is supplied for each single month.
The log sheet is placed in each housekeeper closet at each ward. Every time a cartridge is replaced, the housekeeper who is in charge marks an (x) in front of the appropriate cell on the log sheet. At the end of the 10 months, the following data were collected from the daily Soap and Purell cartridge replacement observations and the IPEC system at the PVAMC to calculate the hand hygiene compliance and test for correlation between product consumption and BDOC for each ward: • Total number of Purell and Soap bottles replaced per month and total volume in milliliters (mL) of these replaced cartridges.
• Total number of Purell and Soap aliquots or hits generated from the total replaced cartridges.
• Bed days of care (BDOC) and admissions of patients at each ward from IPEC system for 2012-15 years.
The following data are collected to estimate the break down use of hygiene products of each ward: • Purell-and-Soap purchase and sales data from the warehouse for the same 10 months of the study (the first 10 months of 2015).

Inpatient-Wards Bed Days of Care vs. Patient Admission
The analysis starts by identifying correlation between patient admissions and bed days of care (BDOC) for the three-inpatient units individually and collectively and also by aggregating the data for all years by ward. The objective from testing the correlation is that it is expected to have an increase in the consumption of hygiene products as BDOC increases. Once the correlation is determined, BDOC is correlated to the consumption of hygiene products used to evaluate the hand hygiene compliance.
The following hypothesis is evaluated first: At the beginning, it is better to identify the best distribution(s) that fit the response (depend) variable, BDOC. BDOC is an unbounded positive count-depended variable 231 that its variance (595102.8 BDOC ! ) exceeds its mean (1499.833BDOC). A Negative Binomial distribution rather than a Poisson distribution would better handle such overdispersion by having one extra parameter that takes care of adjusting the variance independently of the mean. From the histogram, Figure 6.1, the negative binomial distribution is the best-fit distribution for BDOC. The two peaks appearing in the histogram are due to the fact that ICU has far less BDOC than 5B and 6B because of the difference in the bed capacity of each ward.
The easiest way to identify any correlation between two variables is via graphic visualization as seen in the scatter plot in Figure 6 coefficient for 6B is that higher admissions means higher discharges, transfers or deaths and less length of stay (LOS). Whoever gets to 6B does not stay long enough or at least the majority behaves like that. Based on the total number of Soap and Purell replacements combined, Figure   6.6, which is a Pie chart, and

Estimate of the Denominator
Before jumping into the analysis, the sources of error are listed, which could increase or decrease the error in both the numerator and denominator of hand hygiene compliance adherence rate found with this indirect method.

Sources of Error in the Numerator and Denominator:
• These hand hygiene replacement data do not include partial use of cartridges of Purell and Soap on the floor during the time of the study.
• Some of the replacements may not have been reported by mistake.
• Some of the replacements may not have been done by people who did not report it because they did not know about the study.
• Other groups such as EMS or patients and family members use some of Purell and Soap.
• Use of Purell and Soap for hand hygiene true moments versus non-related practices of hand hygiene cannot be distinguished.
The results are shown in Table 6.7. Plotting the hand hygiene adherence rates by BDOC over the 10 months reveals that the rates are very low as expected from the total number of hygiene products replacement per month per ward as seen in Figure   6.10. Based on Figure 6.10, the BDOC versus hand hygiene adherence rate scatter plot, there is hardly any correlation or pattern between BDOC and the rate of hand hygiene.   The indirect hand hygiene monitoring method based on the measurement of the consumption of the hygiene products is still a valid proposed approach. However, the following considerations should be kept in mind for better judgment of hand hygiene compliance rate: • Patients' contacts differ based on patient's health status and needs per ward. • Counting the cartridges should be done per ward per shift, time of installation and replacement should be noted, and any cartridges with remaining volume should be labeled.
• The frequency of each dispenser should be tracked and compared with BDOC of the room associated to that dispenser for a comparison between isolation and non-isolation patients.
Correlating the replaced hygiene products with BDOC and plotting that over time seems enough to assess hand hygiene compliance without going further into estimating the denominator and calculating hand hygiene adherence rate.
Finally, the inpatient wards are ranked based on hand hygiene compliance in ascending order, 5B, ICU, and 6B with a hand hygiene compliance mean of 17.9%, 13.11% and 11.34% respectively, as seen in At first glance of Figure 6.12, targeted replacement of the hygiene products

Introduction
In healthcare settings, monitoring and reporting hand hygiene compliance is the only concern. Therefore, several hand hygiene-monitoring methods were developed and continuously improved in the past. also have similar advantages and disadvantages, as shown in Table 7.1.
• Reports all PPE use, to avoid selection bias.
• Improves validity through using PPE inventory data to develop the denominator.
• Does not capture when the PPE is suppose to be used.
• Does not account for PPE stocked at each inpatient wards.
• Does not capture PPE use by patients or visitors.
• Does not distinguish between different employee groups.

Objectives
Developing a method for estimating PPE compliance based on the inventory consumption of PPE products used in three inpatient wards at the PVAMC is the objective of this study by determining a correlation between bed days of care and the consumption of PPE products use, Gloves and Gowns.

Setting
The • Masks: come in one universal size and is for single use only.
• Gowns: come in one universal size and is for single use only. Its stocking level is 292 and its reorder level is 146.
The primary inventory is manually checked three days per week, on Monday, Wednesday and Friday, for restocking.  • An observational data of PPE products usage of other groups at the PVAMC such as environmental management service (EMS) and food and nutrition services who frequently consume PPE for their regular work.

Inpatient-Wards Bed Days of Care vs. PPE Use
Practicing hand hygiene during key moments is considered the first protection step taken by HCWs to prevent and control the transmission of infectious diseases. The PPE of interest to this study are: • Gloves, • Gowns, and • Masks.
Since practicing hand hygiene before and after wearing hand gloves, wearing gowns and masks before having contacting patients is mandatory, identifying patterns or correlation between: • BDOC versus PPE and       is substituted into these best fit models to produce the prediction values. Of note, gloves prediction values are calculated for all sizes combined. The dependence of PPE use at the inpatient wards at the PVAMC based on the true moments of PPE is modeled. The data considered for this study are: • BDOC for year 2014 and 2015 (12 months) and 2016 (first 9 months).
• PPE for year 2014 (12 months), predicted PPE for 2015 (12 months) and 2016 (first 9 months).   year, can be seen in Table 7      Similarly, the best-fit model for gowns counts based on BDOC can be seen in  In conclusion, based on the models outputs, the null hypothesis ! ! that states BDOC does not have an impact on PPE quantity ordered is rejected. Consequently, as BDOC increases, indeed PPE quantity ordered increases too.
In summary, the objective of statistically investigating the relationship between BDOC versus PPE quantity ordered is found.

PPE Ordering and PPE True Moments
Now, after determining the relationship between BDOC and PPE, the hypothesis testing for modeling the dependence of PPE quantity ordered based on PPE true moments of use is as follows:  The assumption states that PPE quantities ordered are assumed to be equal to the amount consumed on the floor during required moments such as wearing gloves before providing care to patients who are on precautions, although defects such as damaged gloves could occur. The objective of this model is to indirectly estimate the PPE compliance based on PPE inventory data for PPE moments in which HCWs should use gloves, gowns and masks during caregiving to patients who are on precautions. Basically, a patient could be on precautions, which requires wearing gloves, a gown for contact precautions plus surgical mask for droplet precaution or N-95 mask for airborne precautions. In addition, all other patients are considered to be on standard precautions, where there is no isolation sign hung up on the room door, but a HCW may need to wear gloves for certain kinds of medical or non-medical treatment. Now, instead of using BDOC as a predictor, an attempt is made to estimate how many 285 times PPE are required during caregiving, which is called PPE Moments, named after hand hygiene five moments. PPE quantities ordered are used as the numerator.
However, in an indirect method of measuring PPE compliance, attempts are made to create a denominator from an estimate that leads to a compliance percentage.
Essentially, the denominator represents how many PPE are supposed to be used versus the numerator which represents how many PPE are actually used during key moments of PPE use. The following steps summarize the estimate of the denominator calculations for both gloves and gowns:  9) The Gloves Quantity Ordered, which is the gloves compliance numerator, and is found by taking the number of cases of gloves ordered in the month (! !" ), multiplied by 100 gloves in the box, and divided by 2 because each healthcare worker must wear a pair of gloves. The Gowns Quantity Ordered, which is the gowns compliance numerator, is found by taking the number of cases of gowns ordered in the month, multiplied by 10 gowns per case.
The denominator is estimated to find the compliance of only those employee groups who are of interest to this study (medical groups such as attending, residents, interns and medical students and nursing group such as RN, LPN and CNA). Thus, one source of error in the numerator can be addressed by removing the known portions of PPE used by other groups on the floor such as housekeepers from EMS and nutrition and food services. The following steps are made to reduce errors imposed by the earlier assumption stating that PPE quantities ordered are assumed to be equal to the amount consumed on the floor: 1) Housekeepers are required to clean patients' rooms and make beds at least twice a day. One type of cleaning is called terminal clean, which is upon patient discharge and labeled on Environmental Checklist as Step 1 as seen in Appendix CC, and another clean is called regular clean, which does not appear in Appendix CC. Thus, the housekeepers' gloves consumption is calculated by multiplying 2 gloves times 2 cleans times total BDOC. There is no need to distinguish between transmission precautions BDOC and standard precautions 288 BDOC since housekeepers are required to wear gloves for every room clean.
However, an extra clean is required for rooms with C.Diff based on the environmental checklist on Appendix CC. Then, this number is subtracted from the gloves quantities ordered in the numerator. For gowns, it is necessary to distinguish between isolation versus non-isolation BDOC since gowns are required during cleaning patients' rooms and making beds in rooms with precautions signs. Thus, the housekeepers' gowns consumption is calculated by multiplying 1 gown times 2 cleans times transmission precautions BDOC, which is ! ! . Then, this number is subtracted from the gowns quantities ordered in the numerator.
2) Nutrition and food services deliver three meals to patients in inpatient wards on a daily basis. Thus, they are required to wear gloves at least once per meal delivery per day. They are not allowed to enter isolation rooms. Thus, there is no need to distinguish between transmission precautions BDOC and standard precautions BDOC. The nutrition and food services' gloves consumption is calculated by multiplying 2 gloves times 3 meals times days per month, which varies. Then, this number is subtracted from the quantity of gloves ordered in the numerator.
Shifts are important; however, PPE quantities ordered are not broken down by shift. In addition, HCWs have to comply with PPE use when providing care for patients who are on precautions; however, HCWs are not required to use PPE when providing care for patients who are not on precautions except for certain procedure such as drawing blood or inserting intravenous lines (IV). CLABSI Line Days, 289 CAUTI Catheter Days and Ventilator Days are not reported on an isolations and nonisolations basis. Thus, an assumption is made that these practices are made on nonisolation patients that do not require gowns. Masks are not used in such a calculation because it is hard to determine or estimate a true moment of using masks.
The derived PPE compliance equation is as follows: Sources of error could increase or decrease the numerator and/or the denominator of PPE Compliance. Subsequently, the percentage could go beyond 100%.

Sources of Error in the Numerator could originate from:
• The uncertainty that comes from the assumption that PPE data are inventory ordered data.
• The fact that some PPE are discarded, misused and damaged (defect).
• A hypothesis test could be tested later for the percentage of patient contacts relative to isolation patients vs. non-isolation patients per inpatient wards to reduce the error imposed by such average visits in which this average visits could be different per inpatients ward per patients isolation classification.
• Not distinguishing between using PPE with Hand Hygiene or not: Standard Precautions or Transmission Precautions.
• The fact that it is hard to quantify how many are used in the floor or how many are left on the floor per month because they are unknown for sure.

291
Sources of Error in the Denominator could originate from: • The fact that the average visits of isolation patients versus non-isolation patients vary per HCWs groups on a daily basis per ward.
• The missing isolations information in IPEC system for other HAIs such as C.Diff, which is not reported by ward and other isolations precautions for other diseases such as droplet (Flu) or airborne (TB) or Neutropenic Precautions (Protective Environment) (Appendix GG).
• The missing isolations information from device-associated, which is not reported by isolation versus non-isolation. That could affect gowns compliance since all device-associated events are assumed to occur for non-isolation patients and thus no gowns are required.
• The assumption that states HCWs wear gloves once per day for non-isolations patients for all visits. It could be more than that or zero for some cases.

295
The same procedure that was used for gloves and gowns vs. BDOC is now used to find the best fit model for gloves moments vs. gloves ordered, as seen in Figure   7.22. The procedure is repeated to find the best fit model for gowns moments vs.
gowns ordered, as seen in Figure 7.23.  In summary, the objective of statistically investigating the relationship between PPE true moments versus PPE quantities ordered is found. Now after revealing how the interested groups of HCWs at the PVAMC behave in terms of consuming PPE per PPE moments, PPE compliance % is calculated and a bar plot is generated for gloves and gowns, as can be seen in Figure 7.24 and Figure   7.25, respectively.
For gloves compliance, it can be seen in Figure 7.      The direct hand hygiene observation method showed a high hand hygiene compliance mean in the first week of the study at 5B, 6B and at all wards combined.
The URI research team believed that could be due to a Hawthorne Effect. The hand hygiene compliance mean of the remaining seven weeks of September and October did not reach the first week level. The monthly hand hygiene compliance mean at the inpatient wards individually and collectively from September to October did not significantly change in either direction. Yet, the weekly hand hygiene compliance mean experienced some fluctuation.
There were zero cases or incidents of healthcare-associated infections (HAI) at all inpatient wards during the two months of the study. There were some cases of community-acquired infections (CAI) such as CDI, MRSA, MRSA colonization and VAE. Thus, correlation between HAI and hand hygiene compliance seemed unreasonable. However, there was a strong negative correlation between the weekly hand hygiene compliance mean and BDOC at 5B (-0.90), 6B (-0.55) and ICU (-0.59).
As BDOC increased, the hand hygiene compliance decreased. However, when correlating HAIs with the hand hygiene compliance using the PVAMC's four year direct hand hygiene observations, a moderate correlation was found with a correlation coefficient of about −0.50.
Hypothesis testing revealed that healthcare worker's gender (female vs. male), healthcare worker's job category (doctors vs. nurses) and patient's isolation status were not identified as statistically significant risk factors for poor adherence to hand hygiene recommended practices. In contrast, hypothesis testing revealed that hand hygiene moments (Before and After touching a patient), patient's gender (female vs. male), inpatient wards (5B, 6B and ICU), days of the week and working shift (night, day and evening) were statistically significant risk factors for poor adherence to hand hygiene recommended practices. From a patient's perspective, HCWs ought to be notified to practice hand hygiene before providing care. A male patient is at higher risk than a female patient though more male patients are admitted to the PVAMC. A patient would be less worried to be admitted to 6B and ICU than to 5B though 5B is a step-down ward. A patient would be less worried to stay at the PVAMC during weekends rather than weekdays and during day and evening shifts rather than night shift.
The monthly hand hygiene compliance over a 10 month study monitoring Purell and Soap usage as an indirect hand hygiene method revealed that compliance was as low as 1.53% and as high as 80.35%. Huge variability was seen over the 10-month period across all inpatient wards. Correlation was not found between the monthly hand hygiene compliance and the monthly BDOC based on such a method. This method revealed that the actual replacement of Purell and Soap cartridges was far below the targeted replacement. During the same study period, the indirect method of measuring PPE compliance revealed that PPE compliance was overestimated for certain months because of the source of error on both the numerator and denominator. However, gloves compliance could go as low as 3%, which was observed at 5B. Gown compliance, on the other hand, seemed reasonably estimated for 5B and 6B. ICU was a special case in this method because of patients' health status. PPE has to be overstocked at ICU for emergency cases. Correlation was not found between BDOC and gloves or gowns compliance except at ICU. A medium negative correlation (−0.53) was identified between BDOC and gloves compliance. As BDOC increased, the gloves compliance decreased.
In conclusion, the study findings confirmed that though HAI cases were very rare, hand hygiene compliance was statistically proven to also be very low. The rare incidents of HAIs at the PVAMC were either caused by another factor or by chance.

Limitations and Future Work
In conducting a direct hand hygiene observation method, randomization is always a concern. The study was conducted at a single facility, in three inpatient wards and the focus was on two important groups, the medical and nursing groups only. The Hawthorne Effect always exists in such a method. Such bias was seen during the first week of the study in September, at 5B and 6B though these two wards are larger than ICU and numbers of HCWs are higher as well. Though larger sample size was collected during the two months study, breaking down the sample by subgroups such as registered nurse practitioner (NP), licensed practical nurse (LPN) 308 and nursing student (NS) disabled the detection of any statistically significant changes in hand hygiene compliance. Though the medical and nursing groups are the most important HCWs in terms of patient contact, limiting the study to them affects the hand hygiene compliance representation of the PVAMC. These two groups are subgroups of a larger list of HCWs and other professional categories at the PVAMC.
In addition, the study focused on the two most important hand hygiene moments, before and after touching a patient. In conducting patient contact, one complete week Sources of error on both the numerator and denominator must be reduced by collecting 309 or observing other groups, such as other medical specialists, who utilize PPE along with the medical and nursing groups. Similarly, other groups should be included in the patient contact study including specialists and surgeons. Sources of error on the denominator of the estimate of gloves moments also could be reduced by observing how many times gloves are used when care is provided to patients who are on nonisolation.
The major limitation was that the patient contact study was conducted once and not as was planned to be repeated three times over the year. In addition, the PVAMC IRB approval and the participation of the PVAMC employees for patient contact and direct hand hygiene observation methods prevented overlapping all methods in one single period of time. The patient contact method was only approved for one complete week at a time. The medical and nursing groups at the PVAMC suggested the patient contact self-report during the first week of October. The recruited secret observers at the PVAMC agreed to collect hand hygiene observations over two months.
Regardless, if the PVAMC is convinced with the current findings of the study, the study could be conducted in the future over at least 6 months. RFID would be a more preferable method to record patient contact data for accuracy, rather than self- reporting. More secret observers should be recruited and since discrepancy is expected, 95% agreement accuracy should be conducted, and a housekeeper would be assigned per inpatient ward per working shift to track Purell and Soap replacement for the indirect hand hygiene manual monitoring of product use.

Recommendations
The PVAMC has made an important initiative toward measuring precise and accurate hand hygiene compliance at the inpatient ward level. The

Purpose of study and how long it will last:
The purpose of this research study is to obtain a more accurate estimate of the total number of times per week that a doctor or a nurse contacts different patients at the Providence Veterans Affairs Medical Center.

Description of the study including procedures to be used:
If you decide to take part in this study, you will be asked to complete a patient-contact form for the inpatient unit (5B, 6B and ICU) where you work. Each time you enter a patient room, you will be asked to put a tally mark (/) on a data collection sheet, for the room number, bed, and patient gender. This form should take approximately 1-2 minutes per patient-contact. You will be asked to document each patient contact during your entire shift for 1 entire week, and for an additional 1-3 weeks later in the year.

Description of any procedures that may result in discomfort or inconvenience:
There are not any foreseeable discomforts associated with the study.

Expected risks of study:
There are not any foreseeable risks associated with the study. The decision to take part in this study is entirely voluntary and your employer will not know what you decide. Your responses will not be reported with your name or any identifying information other than your workgroup code. All form fields should be completed. If you decide to take part in the study, you may quit at any time.

Expected benefits of study:
Although there is no direct benefit to you for taking part in this study, the researcher may learn more about assessing hand hygiene. Thus, the research findings may benefit the hospital in general.

Other treatment(s) available:
There are not alternative treatments associated with this study.

Costs to participants and compensation:
Costs to Participants: There is no cost to participate in the study, other than the time needed to collect the required dataset. Your participation in this study is confidential. The spreadsheet that contains names and unique codes of all participants, and data collected will be electronically stored in a restricted folder on the secure server at the PVAMC. Only the research team will have access to the restricted folder. All physical forms will be kept secured in a double locked cabinet at the Providence VA Medical Center after the study is done and are only accessible by the research team.
RESEARCH PARTICIPANT'S RIGHTS: I have read or have had read to me all of the above.
The Study Staff has explained the study to me and answered all of my questions. I have been told of the risks or discomforts and possible benefits of the study. I have been told of other choices of treatment available to me.
I have been told that I do not have to take part in this study, and my refusal to participate will involve no penalty or loss of rights to which I am entitled. I may withdraw from this study at any time without penalty or loss of VA or other benefits to which I am entitled.
The results of this study may be published, but my records will not be revealed unless required by law. The Institutional Review Board at the Providence VA Medical Center or other federal oversight offices may monitor my records for quality assurance purposes. Federal agencies including, but not limited to, the Food and Drug Administration (FDA), the Office for Human Research Protection (OHRP), the Office for Research Oversight (ORO), the Office of the Inspector General (OIG) and the Government Accounting Office (GAO) may have access to the records as allowed by law. If an FDAregulated test article is part of this study, the FDA may choose to inspect research records that include research subject's individual medical records. Records will be maintained in accordance with the Department of Veterans Affairs Record Control Schedule 10-1.
If I experience a side effect or adverse (bad or unexpected) reaction as a result of my involvement in this study, I will report these to the study investigator Associate Professor Valerie Maier-Speredelozzi at (401) 874-5187 who will arrange for any medical treatment that is necessary. After hours, I will call the operator at (401) 273-7100 and ask to speak to the infectious disease physician on call.
In case there are medical problems or questions, I have been told I can call Dr. Melissa Gaitanis at (401) 273-7100 extension 3609 during the day. or After hours, I will call the operator at (401) 273-7100 and ask to speak to the infectious disease physician on call. If any medical problems occur in connection with this study the VA will provide emergency care.
The VA has the authority to provide medical treatment to participants (veterans and non-veterans) injured by participation in a VA study. If you are injured as a result of being in this study, the VA will provide the necessary medical treatment in accordance with federal law. If you want to make a legal claim against the VA or anyone who works for the VA, special laws may apply. The Federal Tort Claims Act (28 U.S.C. 1346(b), 2671-2680) is a federal law that controls when and how a person can bring a claim against the U.S. Government. If you sign this document you are not giving up your right to make a legal claim against the United States. participation is over for the following: 1) concerns, 2) complaints, 3) problems, 4) suggestions, 5) more information, 6) questions about my rights as a research participant or 7) verifying the validity of the study and authorized contacts.
I voluntarily consent to participate in this study. I confirm that I have read this consent form or it has been read to me, and I agree it explains what this study is about and how and why it is being done. I will receive a signed copy of the consent form document after I sign it.