INFECTIONS: THEORY BUILDING THROUGH INDUCTIVE AND DEDUCTIVE. APPROACHES ..... failure-free results (Hartmann et al., 2009). HRO theory ..... public through published reports or through internet sites (Kim & Black, 2011). The.
ORGANIZATIONAL CONTEXT AND HEALTHCARE-ASSOCIATED INFECTIONS: THEORY BUILDING THROUGH INDUCTIVE AND DEDUCTIVE APPROACHES by HEATHER M. GILMARTIN B.S.N., Boston College, 1992 M.S.N., Yale University, 2000
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy College of Nursing 2014
This thesis for the Doctor of Philosophy degree by Heather M. Gilmartin has been approved for the College of Nursing by
Sarah Thompson, Chair Karen H. Sousa, Advisor Regina Fink Connie Price Joyce Verran
Date
7/24/14
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Gilmartin, Heather, M. (PhD, Nursing) Organizational Context and Healthcare-associated Infections: Theory Building Through Inductive and Deductive Approaches Thesis directed by Professor Karen H. Sousa. ABSTRACT Background: Healthcare-associated infections (HAIs) are a leading cause of morbidity and mortality in the U.S. The move to eliminate HAIs has been slow and reports of individual successes have been dwarfed by the number of programs that witnessed initial success, yet have experienced challenges with sustainment. This has been attributed to the lack of theory-based frameworks that describe and evaluate the relationships within infection prevention programs. The purpose of this study was to test a middle-range theoretical model to identify and explain the relationships between the concepts of adherence to HAI prevention interventions, organizational context, and HAI outcomes using structural equation modeling (SEM). The Quality Health Outcomes Model (QHOM) guided the study. Methods: Measures to represent the latent variables of adherence to central lineassociated bloodstream infection (CLABSI) prevention interventions, organizational context, and CLABSI outcomes were selected for this secondary analysis from 614 hospitals that participated in the Prevention of Nosocomial Infection and Costeffectiveness – Refined dataset, a large national survey of infection prevention and control programs. One-half of the dataset was used for exploration of the concepts, the second half for confirmation of the model.
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Results: SEM indicated support for the proposed middle-range theoretical model, for the model fit the data well. The relationship between adherence to CLABSI interventions and organizational context was confirmed. The relationship between organizational context and CLABSI outcomes was not statistically significant. The construct validity of three instruments that represent the concepts of the work environment, organizational climate, and CLABSI interventions were confirmed. Lastly, organizational context was confirmed as a second-order model. Conclusions: The results support the conceptual model, based on the QHOM, as applied to infection prevention in hospitals. The study highlighted the complexity of measuring organizational context and exposed weaknesses in the measurement and reporting of HAI outcomes as a patient safety indicator. The findings suggest that patient safety programs may have a greater impact if they focus on an organizational context that facilitates high-levels of adherence to infection prevention interventions over reporting adverse outcomes. The form and content of this abstract are approved. I recommend its publication. Approved: Karen H. Sousa
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DEDICATION To my parents, who have been my sources of strength and inspiration since the very beginning. To my husband, this is as much your achievement as it is mine. To my two boys, may this remind you that anything is possible. Thank you all for your support, love and constant reminders of what is truly important.
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ACKNOWLEDGMENTS Many thanks go to my advisor, Karen Sousa, for her gentle guidance over the past four years. This work is a testament to her passion for nursing research and her skills as a mentor, statistician, and systems researcher. I am grateful for the members of my committee: Regina Fink, Connie Price, Sarah Thompson, and Joyce Verran. Your faith in my research proposal and writing skills was invaluable. I would like to thank Patricia Stone, Carolyn Herzig, and Monika Pogorzelska-Maziarz of Columbia University for the use of the P-NICER dataset for this dissertation. Lastly, I would like to acknowledge my classmates, Kristine Gauthier, Michaela McCarthy, and Carrie Murray. Without your friendship, hospitality and unwavering cheer, I would never have finished.
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TABLE OF CONTENTS CHAPTER I.
II.
III.
IV.
BACKGROUND
1
Healthcare-associated Infections
1
Organizational Context
5
Theory of Organizational Context in Healthcare
7
Study Aims
10
Significance
11
Limitations
11
Summary
12
CONCEPTUAL MODEL
13
The Quality Health Outcomes Model
13
Measurement of QHOM Concepts
17
QHOM Concepts Studies with Patient Outcomes
33
Summary
41
METHODS
43
Study Design
44
Risks and Benefits
45
Data Analysis Plan
45
Study Aims
46
Power Analysis
50
ORIGINAL P-NICER STUDY FINDINGS
51
Review of Prevention of Nosocomial Infections and CostEffectiveness Study
51
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V.
VI.
P-NICER Survey Items Identified as QHOM Concepts
55
Preliminary Findings of the Original P-NICER Study
60
Limitations of the Original P-NICER Study
62
Summary
66
FINDINGS
68
Data Reduction Methods
68
Statistical Assumptions for Data Analysis
75
Descriptive Statistics of Full Dataset
78
Correlational Analysis
85
Inter-concept Correlations
87
Summary
93
Splitting of the Dataset for Factor Analysis
93
Development of Measurement Models
94
Summary for Measurement Models
108
Development of Structural Models
108
Summary
114
CONCLUSIONS
119
Findings and Interpretations – Aim One
120
Findings and Interpretations – Aim Two
122
Findings and Interpretations – Aim Three
123
Study Limitations
127
Opportunities for Future Research
129
Conclusion
131
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REFERENCES
133
APPENDIX
A. Concept and Data Tables
153
B. Key for Models
192
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LIST OF TABLES TABLE 1.
Guideline for Goodness of Fit Indices
48
2.
ICU Reporting of CLABSI Outcome: By Month
70
3:
Missing Data: Structural Variables
72
4:
Missing Data: Work Environment Variables
73
5:
Missing Data: Organizational Climate Variables
74
6:
Missing Data: CLABSI Interventions
75
7:
Individual or Unit Level Items Removed from LCQ Instrument
84
8:
Correlational Analysis: Significant Items from the Organizational Climate, Intervention and Outcome Analysis
91
9:
Parameters for Measurement Models
94
10:
Modification to LCQ Items to Improve Fit Indices
101
11:
Model Parameters: Second-Order Factors
108
12:
Model Parameters: CLABSI Interventions on Organizational Context
109
13:
Model Parameters: Organizational Context on CLABSI Outcomes
110
14:
Model Parameters: Adherence to CLABSI Interventions on Organizational Context on CLABSI Outcomes
111
A1:
Organizational Context: Definition of Terms and Dimensions
153
A2:
Hospital Structural Characteristics
156
A3:
Infection Prevention and Control Program Characteristics
158
A4:
Descriptive Statistics: Relational Coordination Instrument
160
A5:
Descriptive Statistics: Organizational Climate Variables
165
A6:
Descriptive Statistics: Leading a Culture of Quality Instrument
166
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A7:
Adherence to CLABSI Prevention Policies 1-6 Scale
171
A8:
Adherence to CLABSI Intervention Factor Structure
172
A9:
Relational Coordination Factor Structure
175
A10: Leading a Culture of Quality Factor Structure
179
A11: Correlated Second-Order Model: Work Environment and Organizational Climate: Standardized Coefficients 182 A12: Second-Order Factor Model: Organizational Context: Standardized Coefficients
184
A13: Structural Model: Adherence to CLABSI Interventions on Organizational Context: Standardized Coefficients
186
A14: Structural Model: Organizational Context on CLABSI Weighted Mean: Standardized Coefficients
188
A15: Structural Model: The Quality Health Outcomes Model Applied to Infection Prevention in Hospitals: Standardized Coefficients
190
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LIST OF FIGURES FIGURE 1.
The Quality Health Outcomes Model
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2.
The Quality Health Outcomes Conceptual Model Applied to Infection Prevention in Hospitals - Proposed
42
3.
Flowchart of Dataset Reduction Method
76
4.
CLABSI Outcome Variable
84
5.
Measurement Model: Adherence to CLABSI Interventions
96
6.
Measurement Model: Relational Coordination Instrument
100
7.
Measurement Model: Leading a Culture of Quality Instrument
102
8.
Second-Order Factor Model: Work Environment
105
9.
Second-Order Factor Model: Organizational Climate
106
10.
Correlated Second-Order Model: Work Environment and Organizational Climate
107
11.
Second-Order Factor Model: Organizational Context
112
12.
Structural Model: Adherence to CLABSI Interventions on Organizational Context Model 113
13.
Structural Model: CLABSI Weighted Mean on Organizational Context
116
14.
Structural Model: The Quality Health Outcomes Model Applied to Infection Prevention in Hospitals
117
The Quality Health Outcomes Conceptual Model Applied to Infection Prevention in Hospitals- Final
118
A1.
Adherence to CLABSI Interventions Scree Plot
173
A2.
Adherence to CLABSI Interventions Component Matrix
174
A3.
Relational Coordination Scree Plot
177
A4.
Relational Coordination Rotated Component Matrix
178
15.
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A5.
Leading a Culture of Quality Scree Plot
180
A6.
Leading a Culture of Quality Rotated Component Matrix
181
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LIST OF EQUATIONS EQUATION 1.
Model Parameter Identification
49
2.
Weighted Mean
71
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LIST OF ABBREVIATIONS x2: α: a: β: c: cv: df: ε: f: ns:
Chi-square Alpha (Cronbach’s) Number of model paths Beta (Regression weight) Number of model correlations Covariance Degrees of freedom Error terms Number of factors Non-significant
ρ
Correlation coefficient
p: p.: q: rs : ACT: AHRQ: BSN: CAUTI: CDC: CFA: CFI: CLABSI: CMS: EFA: EMR: HAI: HRO: HRQOL: HSPSC: ICU: IOM: IP: IRB: KMO: LCQ: NHSN: NQF: NWI-R: OSC: PCA: PES: P-NICE: P-NICER: PSCHO:
Number of model data points Value indicating statistical significance Number of model parameters Spearman rank order correlation Alberta Context Tool Agency for Health Research and Quality Bachelor of Science in Nursing Catheter-associated urinary tract infection Centers for Disease Control and Prevention Confirmatory factor analysis Comparative fit index Central line-associated bloodstream infection Centers for Medicare and Medicaid Exploratory factor analysis Electronic medical record Healthcare-associated infection High reliability organization Health-related quality of life Hospital Survey on Patient Safety Culture Intensive care unit Institute of Medicine Infection Preventionist Institutional Review Board Kaiser-Meyer-Olkin test Leading a Culture of Quality Instrument National Health Safety Network National Quality Forum Revised Nursing Workforce Index Organizational Social Context Instrument Principal component analysis Practice Environment Scale Prevention of Nosocomial Infections and Cost-Effectiveness Prevention of Nosocomial and Cost-Effectiveness – Refined Patient Safety Climate in Healthcare Organizational Survey
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PSI: RCI: RMSEA: SEM: SENIC: SSI: QHOM: UTI: VAP: WLSMV:
Patient safety indicators Relational Coordination Instrument Root mean square error of approximation Structural equation modeling Study on the Efficacy of Nosocomial Infection Control Surgical site infection Quality Health Outcomes Model Urinary tract infection Ventilator-associated pneumonia Weighted least mean square estimation estimate
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CHAPTER I BACKGROUND It is a tragic irony that a healthcare system that is meant to improve health, can harm as much as it heals. In 1999, the Institute of Medicine (IOM) reported that one “jumbo jet” of patients die each day from a medical error in the United States (Kohn, Corrigan, & Donaldson, 1999). The response to this alarm bell has been impressive, with programs such as the Leapfrog Group, a coalition representing large healthcare purchasers, advocating for “safety leaps” to rapidly make healthcare safer. In addition, agencies such as the National Quality Form (NQF), the Agency for Healthcare Research and Quality (AHRQ), the Joint Commission on Accreditation of Healthcare Organizations, the Centers for Medicaid and Medicare Services (CMS), and the Institute for Healthcare Improvement produced patient safety measures, patient safety indicators, patient safety goals, reporting databases, and a nationwide patient safety campaign to help healthcare systems, facilities, and providers work towards safer patient care (Altman, Clancy, & Blendon, 2004; Wachter & Pronovost, 2006). The purpose of all of these initiatives is to protect the public and ensure that patients receive the safest and highest-quality care. Unfortunately, after many years and billions of dollars spent, it is unknown if patients are any safer (Forster, Dervin, Martin, & Papp, 2012; Ladden, 2014; Pronovost, Miller, & Wachter, 2006; Thornlow & Merwin, 2009). Healthcare-associated Infections Healthcare-associated infections (HAIs) are one type of medical error, are a commonly measured patient outcome, and are a leading cause of morbidity and mortality in the U.S. (Klevens et al., 2002). An HAI is defined as an infection that develops while a patient is receiving treatment in a healthcare setting and is unrelated to the patient’s
original condition (Meier, Stone, & Gebbie, 2008). In the U.S., the annual incidence of HAIs has been estimated at two million cases (Scott, 2009), with up to 99,000 patients dying each year (Klevens et al., 2002). These statistics make HAIs the fifth leading cause of death in acute-care hospitals (Septimus et al., 2014; Stone, Braccia, & Larson, 2005). The direct financial costs to the healthcare system due to the five major HAIs was recently estimated to be $9.8 billion (95% CI, $8.3-11.5million) in 2012 dollars (Zimlichman et al., 2013). Moreover, HAIs have become a major source of multiple drug-resistant organisms, most prominently methicillin-resistant Staphylococcus aureus, which is contributing to the spread of this disease into the community (Meier et al., 2008). Of equal importance are reports that patients who suffer an HAI experience lifelong effects, including a loss of trust in the system, and serious concerns if they were to return to a hospital (Burnett et al., 2010). Due to these dangerous and costly problems, the U.S. government has set a goal of preventing, reducing, and ultimately eliminating HAIs by the year 2020 (HHS, 2010). Currently, researchers are working to understand the causes of HAIs and routes of transmission to reduce or eliminate the risk of contracting a HAI. Techniques, tools, and a handful of best practices are available and some interventions have been tested at the bedside and demonstrated improvements in individual organizations (Pronovost, Marsteller, & Goeschel, 2011). This body of research has been synthesized into guidelines developed by the Centers of Disease Control and Prevention (CDC) and professional societies. These include the prevention of ventilator-associated pneumonia (VAP) (Coffin et al., 2008), the prevention of catheter-associated urinary tract infections (CAUTI) (Lo et al., 2008; Lo et al., 2014), the prevention of catheter-associated
2
bloodstream infections (CLABSI), (Marschall et al., 2008) and the prevention of surgical site infections (SSI) (Anderson et al., 2008). Due to the efforts of researchers and clinicians around the world, HAIs have moved from an inevitable part of healthcare, to one where the risks of HAIs are greatly diminished, and at times are viewed as fully preventable (Meier et al., 2008; Septimus et al., 2014; Yokoe et al., 2014; Yokoe et al., 2008). A harsh reality though, is that examples of measurable and sustained progress in the field of infection prevention and patient safety are rare (Altman et al., 2004; Ladden, 2014; Pronovost et al., 2011; Shortell & Singer, 2008). The progress towards the elimination of HAIs has been slow and reports of individual successes have been dwarfed by the number of programs that witnessed initial success, yet experienced challenges with sustainment (Cardo et al., 2010; Septimus et al., 2014). That being said, the field of HAI prevention offers the greatest potential for filling the gaps in patient safety and outcomes research, for HAIs are one of the few valid outcome measures that are linked to prevention practices by empirical evidence (Pronovost et al., 2011; Talbot et al., 2013). At present, SSIs, CLABSIs, VAPs, and CAUTIs account for approximately 75% of HAIs in the acute-care hospital setting (Stone, 2009), even while evidence-based documents exist to guide organizations in the creation of prevention programs. The gap in the HAI literature is no longer what to do, but why some initiatives are successful in some hospitals and not in others and how to sustain the programs (Cardo et al., 2010; Krein et al., 2010; Ladden, 2014; Septimus et al., 2014; Shortell & Singer, 2008). It has been suggested that the long-term success of patient safety programs will only occur when the field moves from a patient or conditionspecific based thinking to a systems-based worldview.
3
The call for a systems approach to healthcare is not new. The IOM reports, To Err is Human (Kohn et al., 1999), Crossing the Quality Chasm (Medicine, 2001), and Keeping Patients Safe: Transforming the Work Environment of Nurses (Page, 2004) stated that system concepts found within the context of an organization (i.e. the structural characteristics, the organizational culture, the safety climate, and the work environment) must be considered to truly redesign the U.S. healthcare system. Though there has been progress on many systems-level factors, including more robust research, increasing regulatory pressure, more vocal consumer advocacy, and health care reform, there continue to be major gaps (Ladden, 2014; Mathews & Pronovost, 2012). A 2010 project initiated by AHRQ (Shekelle et al., 2010) attempted to address these gaps. The authors identified that context-sensitive patient safety practices must be studied, for it is believed that organizational context is key for translating patient safety practices from clinical interventions to a system-wide perspective (Shekelle et al., 2010). The authors report that researchers do not measure or report on contextual factors, nor do they provide an explicit description of the theory for the chosen intervention (Shekelle et al., 2010). This impacts how consumers of research interpret study results (i.e. why the intervention works) and impacts the applicability of implementing findings in their settings (i.e. what contextual factors must be in place to successfully adopt and sustain an intervention) (Shekelle et al., 2010). The authors recommend that future research include theory-based frameworks to describe and evaluate key elements, contexts, and targeted behaviors along with empirically testing the influence contextual factors have on the success of patient safety practices (Shekelle et al., 2010).
4
Organizational Context How organizational context affects organizational outcomes has been one of the fundamental questions within the discipline of organizational behavior (Verbeke, Volgering, & Hessels, 1998). Organizational context includes the constructs of structural characteristics, organizational culture, work environment, and safety climate. Though the majority of the organizational context science in healthcare is immature, there is some evidence of an impact on patient outcomes. Structural characteristics such as hospital location and size (Wan, 1992), teaching status (Fine, Fine, Galusha, Petrilo, & Meehan, 2002), surgical volume (Flood, Scott, & Ewy, 1984; Ritchie, Maynard, Chapko, Every, & Martin, 1999), Medicare case-mix (Wan, 1992), technology availability (Mark, Salyer, & Wan, 2000; Shortell, Zimmerman, & Rousseau, 1994), financial status (Encinosa & Bernard, 2005), and building functioning electronic medical records (Wachter & Pronovost, 2006) have been reported in some studies to positively influence patient outcomes. Organizational culture has been positively associated with a patient safety culture (Singer, Falwell, et al., 2009), long-term hospital performance (Jacobs et al., 2013), an improvement in processes (specifically hand washing frequency), and outcomes (vancomycin-resistant enterococci incidence) (Larson, Early, Cloonan, Sugrue, & Parides, 2000), and nursing turnover (Banaszak-Holl, Castle, Lin, Shrivastwa, & Spreitzer, 2013). The work environment has shown the most consistent influence on positive patient outcomes. Nurse staffing (Aiken, Clarke, Silber, & Sloane, 2002; Needleman, Buerhaus, Mattke, Stewart, & Zelevinsky, 2002), nursing skill mix (Blegen, Goode, & Reed, 1998; Mark et al., 2000), nurse education levels (Aiken et al., 2012), working
5
conditions and level of burnout (Aiken et al., 2012; Aiken et al., 2002; Kirwan, Matthews, & Scott, 2013; Kutney-Lee, Wu, Sloane, & Aiken, 2013), along with employing intensivists in intensive care units (ICU) (Wachter & Pronovost, 2006), and high levels of relational coordination (Havens, Vasey, Gittel, & Lin, 2010) have been positively associated with patient outcomes in some studies. Safety climate has been studied for its relationship to nurse and patient outcomes (Hofmann & Mark, 2006; Mardon, Khanna, Sorra, Dyer, & Famolaro, 2010; Rosen et al., 2010; Singer, Lin, Falwell, Gaba, & Baker, 2009), but the findings are inconsistent at this time. The science investigating the relationships between organizational context and patient outcomes is evolving. Historically, patient outcomes have been studied using measures such as 30-day morbidity, mortality, re-admissions and composite scores of nurse-sensitive adverse events due to the ease of availability and the large variation in outcomes. The study of specific types of HAIs, such as CLABSIs, as a patient outcome is relatively new. The availability of standardized HAI data from the CDC’s National Health Safety Network (NHSN) is opening opportunities for this type of research. To allow for a broad review of the literature for this research project, studies that have investigated organizational context and all types of patient outcomes were included as part of the literature review to help provide background to the body of research and guidance for the development of this study. A gap in the field of organizational context and patient outcomes is an explanation of why some studies have positive findings and others do not. In addition, there is little known about what concepts may mediate or moderate outcomes. For example, does a particular work environment facilitate an organizational culture that
6
encourages a safety climate that consistently leads to positive patient outcomes? This question cannot currently be answered for findings from individual studies are often evaluated in isolation (Hartmann et al., 2009). There is a need for a middle-range theory that can guide researchers in interpreting studies and for planning future projects that can sustainably impact patient outcomes. Theory of Organizational Context in Healthcare Scientific inquiry must rest on theoretical foundations in order for studies to have broad applications (Hatler, 2004). Organizational research is not atheoretical; studies are undertaken using an assortment of conceptual approaches (Hearld, Alexander, Fraser, & Jiang, 2008). These conceptual mutations are important to the growth of a field of study (Verbeke et al., 1998). The current challenge is when the conceptual variations continue at an abstract level, challenging the use at the functional, or practice level. The development of middle-range theories that can be empirically tested would aid in the explanation of findings, convergence of conclusions, and generalization of underlying relationships (Hearld et al., 2008; Mark, 1996; Scott-Findlay & Golden-Biddle, 2005; Shekelle et al., 2010). At present, researchers often choose to focus on specific problems rather than on the underlying factors that contribute to the situation due to the challenge when applying an abstract theory to an individual study. Theory-driven research serves several purposes: 1) placing the study in a particular research domain, 2) provision of an organizing framework to guide and explain the mechanisms of action for the concepts under study, 3) support a conceptual taxonomy, and 4) to direct thinking about the nature of the work, rather than simply the tasks to be accomplished (Alison, McLaughlin, & Walker, 1991; Huckabay, 1991;
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Shekelle et al., 2010). While atheoretical investigations can provide data, these types of investigations are rarely helpful in developing a knowledge base for action that can be applied across settings and along the care continuum (Hatler, 2004). Healthcare researchers studying organizational context predominantly use two grand theories to guide their research. The first is based on the structure-process-outcome approach to quality assessment pioneered by Donabedian (1980). This method highlights the contribution of structure and process features of care delivery to outcomes. According to Donabedian’s (1980) framework, structure includes relatively stable characteristics that facilitate the provision of health services. Examples include size, ownership status, staffing, technological sophistication, and culture. Donabedian (1980) describes process as the clinical services provided to the patient. Examples include leadership, collaboration, communication, and climate. Donabedian (1980) describes outcomes as changes in a patient’s current and future health status that can be attributed to antecedent health care. Examples frequently studied include adverse events, morbidity, mortality, preventable readmissions, and quality improvement processes such as adherence to guidelines. Though the categories of structure and process contain overlapping concepts (i.e. context could include both structure and process variables), the theory described by Donabedian (1980) continues to guide researchers in the fields of medicine, patient safety, health systems, and nursing. Recent studies have tested and adapted the work of Donabedian (1980) to provide a less abstract guide for the planning and evaluation of research studies (Aiken, Sochalski, & Lake, 1997; Brewer, Verran, & Stichler, 2008;
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Mitchell, Ferketich, Jennings, & Care, 1998; Pronovost, Holzmueller, et al., 2006; Stone, Mooney-Kane, Larson, Horan, et al., 2007). The second grand theory that is being used in the study of organizational context in healthcare is the theory of High Reliability Organizations (HRO). This theory grew from the fields of aviation and nuclear power industries and has shown that it is possible to function in a hazardous, complex, and fast-paced environment and still achieve nearly failure-free results (Hartmann et al., 2009). HRO theory emphasizes a number of important components for attaining a strong safety climate (Hartmann et al., 2009). For example, to minimize errors the theory suggests that a large proportion of individuals throughout the organization need to place a high priority on safety (Gaba & Cooper, 2000). In addition, an organizational infrastructure to support safety through training and process monitoring is necessary (Hartmann et al., 2009). Health systems researchers have introduced HRO theory into studies, but at this time the theory remains very complex, which challenges its utility in studies that are attempting to explain relationships among conflicting findings (Gaba & Park, 2000; Hartmann et al., 2009; Rosen et al., 2010; Singer, Lin, et al., 2009). Though both grand theories have been influential in revealing relationships between organizational context and patient outcomes, the conceptual framework for this study, the Quality Health Outcomes Model (QHOM) has been chosen for it addresses the relationships among the system or context, interventions, the client, and outcomes (Mitchell et al., 1998). The QHOM is a conceptual model of nursing that is an adaptation of the structure-process-outcome model of Donabedian (1980). There are four reasons the QHOM has been selected to guide this study. The first is that the QHOM will allow for
9
an investigation into the mechanisms of influence between the variables of context, interventions, client and outcomes. Second, the QHOM will guide the interpretation of the relationships between the variables of interest. Third, the model will provide taxonomy for integrating the results of the inductive and deductive approaches in this study. Finally, the QHOM will provide conceptual support in testing and confirming a theory of organizational context and HAI. In chapter II, the QHOM is fully introduced, followed by a description of the QHOM concepts that have undergone measurement and will end with the QHOM concepts that have been studied with patient outcomes. Last of all, an initial model, using concepts shown in the literature to link context, interventions, client characteristics, and HAIs is presented. Study Aims The purpose of this study was to test a middle-range theoretical model to identify and explain the relationships between the concepts of adherence to HAI prevention interventions, organizational context, and HAI outcomes. The population of interest includes acute-care hospitals in the U.S. and the patients who had contracted a CLABSI in these hospitals. The data were drawn from an existing dataset collected in the conduction of the Prevention of Nosocomial Infection and Cost-effectiveness-Refined (PNICER) study (National Institutes of Health, RO1NR010107: Stone, P.), which is described in detail in chapter III. Three specific aims are addressed in this proposal: •
Aim 1: To conduct a preliminary analysis of the dataset to determine the value of specific variables to operationalize in the conceptual model.
•
Aim 2: To develop measurement models for each latent variable with the secondhalf of the randomized P-NICER dataset.
10
•
Aim 3: To confirm the middle-range theoretical model using structural equation modeling. Significance Despite the billions of dollars and countless hours focused on creating a safer
healthcare system, little evidence exists that patients are safer. Efforts to link healthcare activities and patient outcomes are ongoing, yet there is much work needed to realize these relationships (Ladden, 2014). Some studies have found positive associations, but the findings must be interpreted in isolation due to the lack of guiding theory explaining the relationships. An understanding of the concepts of organizational context and the link with improved patient outcomes, specifically HAIs, could reveal aspects that predispose some hospitals to safer care. The absence of a middle-range theory and concrete models to guide this program of research has hindered knowledge development. This study was designed to fill this gap by refining and empirically testing a model to explain the relationships between the concepts of adherence to HAI prevention interventions, organizational context, and HAI outcomes. Limitations Limitations to this research include the challenges of finding the boundaries of the concepts within organizational context (Shekelle et al., 2010), the conceptual confusion surrounding organizational culture and climate (Denison, 1996), and the overlapping concepts of patient safety culture and safety climate (Pronovost & Sexton, 2005; Singer, 2003; Singer, Falwell, Gaba, & Baker, 2008; Stone, Harrison, et al., 2005). Second, many contextual concepts, such as organizational climate, are not frequently measured or reported in the literature. Due to this, relationships between the concepts may need to be
11
tested without empirical justification. Lastly, due to the efforts of researchers in creating interventions that are implemented by providers in high-risk units, the incidence of HAIs is decreasing (CDC, 2014). This may present statistical challenges when attempting to interpret the relationships of interest, but the potential benefits to patients’ welfare and costs to society support these efforts. The methods to address these limitations are described in chapters II and III. Summary Without a doubt, HAIs impose significant economic consequences and human suffering on the nation’s healthcare system and the people the system serves. One of the greatest barriers to patient safety in the U.S. is the lack of understanding of the role organizations play on patient outcomes. Patient safety will improve when the underlying system of care improves. The study described in this paper aims to address patient safety at the systems level. Through this work, a better understanding of the relationships between adherence to HAI prevention interventions, organizational context, and HAI outcomes will be revealed.
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CHAPTER II CONCEPTUAL MODEL This chapter will address the conceptual model that will guide the research study. In addition, descriptions of the QHOM concepts that have been measured and studied with various patient outcomes are discussed. Last of all, a conceptual model, using the QHOM concepts that have been studied in the literature with patient outcomes and are available in the P-NICER dataset is presented The Quality Health Outcomes Model The conceptual model that was used in this study is the Quality Health Outcomes Model (QHOM) which was developed by the American Academy of Nursing Expert Panel on Quality (Mitchell et al., 1998) (Figure 1. The Quality Health Outcomes Model). The QHOM contains four major constructs: system or context, intervention, client, and outcomes and is an extension of the time-honored structure-process-outcome framework for quality assessment (Donabedian, 1980). The term context, versus system, will be used in this project due to the adoption of this term in the healthcare literature (Shekelle et al., 2010). The QHOM is a model influenced by the field of nursing for it includes the concepts of the nursing paradigm: person, environment, health and nursing (Fawcett, 2000). Since its creation, the QHOM has been used as a grand theory, a source of derivation that has inspired the creation of new theories which reflect the interdisciplinary nature and multiple contextual factors that influence health systems research (Brewer et al., 2008; Mayberry & Gennaro, 2001; Mitchell & Lang, 2004; Radwin, 2000).
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Context
Outcome
Intervention
Client Figure 1. The Quality Health Outcomes Model (with permission) (Mitchell et al., 1998) Conceptual models are defined as a set of interrelated concepts that symbolically represent and convey a mental image of a phenomenon (Peterson, 2009). Due to the abstract nature of models, they cannot be tested or validated, but they can provide structure for theory development (Peterson, 2009). The term conceptual model has been used interchangeably with conceptual framework and theoretical framework. The term conceptual model was used in this study. Theory is defined as a set of interrelated constructs (concepts, definitions, and propositions), based on assumption, woven together through a set of propositional statements used to provide a perspective on reality (Fitzpatrick, 1997; Peterson, 2009). Theory is meant to provide a taxonomy and is a method of communicating the nature of the research to those within and outside the field (Peterson, 2009). In the structural hierarchy of knowledge, grand theories are identified as the most abstract, practice theories are the most concrete, and middle range theories in the logical middle (Peterson,
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2009). The advantage of working with middle-range theories is that they permit testing and confirmation of relationships (Peterson, 2009). This study aims to test a middle-range theoretical model to identify and explain the relationships of the concepts of adherence to HAI prevention interventions, organizational context, and HAI outcomes. For a theory to be useful in organizational research, some common characteristics should be present. First, a theory should include the structure of the program that will explain why changes are expected to work. Second, the underlying causal mechanisms that link the relationships among the elements associated with the change must be explained. This combination of approaches provides guidance for the identification of both the intervention and the ways in which it is expected to work (Verran, 1997). Third, the focal level, or the segments of an organization undergoing study must be supported by the theory (Verran, 1997). As outlined in the following paragraphs, the QHOM meets these expectations and the testing of the model in acute-care hospitals with infection prevention programs may explain some of the linkages between HAI prevention interventions, organizational context, and HAI outcomes. The QHOM is a unique systems model for it challenges the traditional view that interventions or treatments directly produce expected outcomes, as adjusted for client characteristics. The authors of the QHOM recommend the use of nurse-sensitive outcomes that are the results of care structures and processes that integrate functional, social, psychological, physical, and physiologic aspects of people’s experience in health and illness (Mitchell et al., 1998). The QHOM was intended to be more closely aligned with the dynamic processes of patient care and outcomes than other more linear models (Mitchell et al., 1998).
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The QHOM suggests no single direct connection linking interventions and outcomes. This is due to a lack of research that consistently shows relationships between structures or process variables and outcomes, when either structure or process was measured alone (Aiken et al., 1997; Mitchell & Shortell, 1997). The QHOM suggests that interventions impact and are impacted by both context and client characteristics in producing desired outcomes. In addition, the connection between context and client suggests that no single intervention acts directly through either context or client alone. The effect of an intervention is mediated by client and context characteristics and is thought to have no independent direct effect on patient outcomes (Mitchell et al., 1998). The refinements are intuitively appealing and testing of the model may empirically document the intervening mechanisms by which contextual factors influence outcomes. The authors of the QHOM aimed to capture the contribution of nursing, specifically the provision of nursing care, on patient outcomes. Historically, measurable patient outcomes included death, disability, dissatisfaction, disease, and discomfort (Lohr, 1988). These were evaluated in relation to treatment interventions or technology assessment. Once measures such as health-related quality of life (HRQOL) were added, the possibility of measuring outcomes that respect the effect of the health problem on functioning and well being opened the door to nurse-sensitive measures. The authors suggest the following categories for operationalization: achievement of appropriate selfcare, demonstration of health-promoting behaviors, HRQOL, perception of being well cared for, and symptom management (Mitchell et al., 1998). In addition, the NQF, in an effort to consistently capture nurse-sensitive measures, created a performance measure
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set to guide organizations in the collection and reporting of nurse-sensitive outcomes. HAIs such as CLABSIs are noted inclusions (NQF, 2004). The QHOM has the potential to provide a better understanding of which aspects of organizational context contribute most to patient outcomes. To guide the use of the QHOM by researchers, the authors offered several key questions that could be answered using the model. This included “What specific organizational variables and delivery of care variables are related to specific patient outcomes?” (Mitchell et al., 1998, pg. 45). The alignment between the proposed questions and the aims of this study strengthens the selection of the QHOM for this project. Measurement of QHOM Concepts The QHOM identifies four main concepts: context, client characteristics, interventions, and outcomes. The authors of the QHOM did not provide specific definitions for each concept, but some guidance was provided for researchers to use as a starting point. Organizational context as a main concept has been measured through the sub-concepts of structural characteristics, the work environment, organizational culture, and climate. Client characteristics have been measured through patient level data such as age, gender, and acuity level. Interventions have been measured through unit level processes such as implementation of treatments or adherence to medications. Lastly, the concept of outcomes has been measured by patient level data such as infections or falls. To provide a deeper understanding and definitional boundaries for the concepts within the QHOM, the conceptual definitions and dimensions, along with instruments that have attempted to measure the concepts are presented. The information from this review
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guided the selection of variables from the P-NICER dataset for inclusion in model testing. Organizational Context Variables Organizational Context. One of the great challenges in the field of organizational context is finding the boundaries of the concepts (Shekelle et al., 2010). Multiple definitions have been suggested, yet there is a noted lack of consensus. Nursing and health systems researchers have defined context as the environment or setting in which people receive healthcare services (Estabrooks, Squires, Cummings, Birdsell, & Norton, 2009). This can be viewed as an infinite concept, for healthcare occurs in a variety of settings, at different levels, by different communities and cultures, and with different economic, social, political, fiscal, historical and psychosocial factors (McCormack et al., 2002). To aid in the measurement of this concept, the term can also be defined as the physical environment in which patient care takes place. Such a setting has boundaries and structure that together shape the environment (McCormack et al., 2002). Though the concept can be studied through an analysis of the complexity of factors that enable effective patient care, this project will use the concept of context as a method to describe the way in which organizational systems and structures interact with each other (McCormack et al., 2002). The concept of organizational context has also been measured through multiple sub-constructs that more directly influence organizational behavior and performance (i.e. aspects of the work environment, culture, and climate). The Organizational Social Context (OSC) instrument and the Alberta Context Tool (ACT) are two examples of instruments that attempt to measure the concept and sub-concepts of organizational
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context. The review of these instruments will inform the use of the concept of organizational context within the QHOM, by presenting the constructs that have been empirically tested. The OSC was developed to assess the relationships between context and the adoption and implementation of evidence-based practices in mental health services. The key constructs of the OSC include organizational culture, climate, and work attitudes (Glisson et al., 2008). The instrument was tested in 100 U.S. mental health clinics. Analysis of the subscales of the instrument, using factor analysis, demonstrated three subscales that operationalized culture (rigidity, proficiency, resistance), three subscales that operationalized climate (stress, engagement, functionality), and one subscale that operationalized work attitudes (morale) (Glisson et al., 2008). The fit indices (RMSEA = 0.05; CFI = 0.96) demonstrated adequate support for the model. The validity of this instrument in this population and in a study of public health and social services in Finland (Rostila, Suominen, Asikainen, & Green, 2011) was adequate, but a limitation of the tool is the lack of testing in hospital settings and the primary focus on implementation of research in the practice setting. The ACT, a 56-item, 5-7 Likert response scale tool, was developed to assess how the organizational context of healthcare settings supports research implementation by providers, as well as the role of context on provider and patient outcomes (Estabrooks et al., 2009). The authors designed the instrument to measure the constructs of culture, leadership, and evaluation. The instrument was tested in seven pediatric hospitals in six Canadian provinces. Subsequent exploratory factor analysis (EFA) resulted in a 13-factor solution for the ACT, accounting for 59.26% of the variance in organizational context
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(Estabrooks et al., 2009). The resultant factors included the intended measured concepts of organizational culture, leadership, and evaluation. In addition, six new concepts were identified: social capital, informal interactions, formal interactions, structural and electronic resources, and organizational slack, which is defined as time, space, and human resources. Psychometric testing of the tool demonstrated adequate validity and reliability in pediatric acute-care settings and long-term residential care (Cummings, Hutchinson, Scott, Norton, & Estabrooks, 2010; Estabrooks, Squires, Hayduk, Cummings, & Norton, 2011; Estabrooks, Squires, Hutchinson, et al., 2011). A limitation of this instrument is the exclusive testing of the instrument in Canada, a healthcare system different than the U.S., and the primary focus on implementation of research by healthcare providers in the practice setting, not patient outcomes. Though both the OSC and ACT have noted weaknesses, including the lack of investigation into the relationships between organizational context and patient outcomes, they do attempt to measure the concept of organizational context and its relationship with adoption of evidence-based practice interventions. The next logical step is to use the instruments in a study of patient outcomes. In summary, this review of the OSC and ACT provided some insight into the concepts and sub-concepts of organizational context that have been empirically tested (Table A1: Organizational Context: Definition of Terms and Dimensions). Further investigation and testing of the concepts in a middle-range theory that explains the relationships between organizational context and HAIs may enhance studies that investigate the influence of organizational context in healthcare.
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Structural Characteristics. Structural characteristics are comprised of the relatively stable qualities of a healthcare system that facilitate or constrain the delivery of care (Hatler, 2004). Measurement of these variables has not required the development of instruments, for the concepts are well defined in the literature (Shekelle et al., 2010). For example, the American Hospital Association has a national database of hospital structural characteristics that provides definitions for variables such as bed size (i.e. the total number of beds authorized by a state-licensing agency), setting (i.e. urban, suburban, or rural based on metropolitan statistical area population standards), teaching status (i.e. membership in the Association of American Medical Colleges Council of Teaching Hospitals) and high-technology (i.e. an organization with facilities for open-heart surgeries, major organ transplants, or both). An additional structural characteristic pertinent to this study is the legislative mandate to publicly report HAIs. Known as mandatory public reporting, the program is defined as either state legislation or regulation that requires hospitals to conduct surveillance and report rates for designated HAIs to commissions or state health departments (McKibben et al., 2005). The risk-stratified data is made available to the public through published reports or through internet sites (Kim & Black, 2011). The program is meant to enable consumers to make more informed choices about their healthcare and improve overall healthcare quality by reducing HAIs (McKibben et al., 2005). The resources and infrastructure necessary to facilitate reporting programs include trained personnel, instruction manuals, training material, and data collection, entry and analysis tools, along with extensive computer resources.
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Magnet certification is an award granted to hospitals by the American Nurses Credentialing Center for demonstration of organizational characteristics that embody nursing excellence. These characteristics include transformational leadership, structural empowerment, knowledge, innovation and improvements, and exemplary professional practice (Mills & Gillespie, 2013). The resources and infrastructure necessary to earn and then maintain Magnet status include trained personnel, quality and research departments, and tools to facilitate strong nursing practice. Magnet certification, along with HAI mandatory reporting qualify as structural characteristics in this study for once Magnet certification is earned and HAI reporting is mandated in a state, the components of the programs become stable structures within an organization. Other structural characteristics such as volume, a hospital severity of illness measure (i.e. Medicare case-mix), and an electronic medical record program are routinely included in research studies. Definitions for these characteristics vary slightly according to the source of the data. It is generally agreed upon that the boundaries of structural characteristics have become standardized and there is a general consensus on the characterization of these concepts. Due to this, the structural concepts presented will be selected from the P-NICER dataset, if available, for inclusion in the model testing. Work Environment. Unfortunately, the definitions and dimensions of the remaining organizational context concepts are not as well characterized. The concept of work environment has been studied predominantly with nurses, the largest professional workforce in healthcare. The nurse practice environment became a field of study during the national nursing shortage in the early 1980’s. It was noted that certain hospitals seemed to have fewer problems attracting or retaining professional nursing staff
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(Aiken & Patrician, 2000). In a germinal study by the American Academy of Nursing, the organizational characteristics that accounted for the high rates of nurse satisfaction and retention in these Magnet hospitals were revealed (McClure, Poulin, Sovie, & Wandelt, 1983). They included the dimensions of staffing, leadership, rewards and recognition, engaged management, open communication, good relationships with physicians, professional development, career advancement, and a slightly richer nursing skill mix (Sovie, 1984). Instruments have been developed to test the concept of the nurse work environment (Aiken & Patrician, 2000; Estabrooks et al., 2002; Lake, 2002). The three tools include variations of the original Magnet characteristics identified by McClure et al. (1983). The Revised Nursing Work Index (NWI-R), developed by Aiken & Patrician (2000), consists of 57-items within four subscales. The Practice Environment Scale (PES), developed by Lake (2002), consists of 49-items within five subscales. The NWIR, adapted for Canadian hospitals by Estabrooks et al. (2002), consists of 51-items within nine subscales. All three scales use a 4-scale Likert response (strongly agree-strongly disagree). Individually, the instruments are touted as valid measures of the nurse practice environment, but the validity of these measurement tools has been questioned recently. Using the original published data and structural equation modeling, Cummings, Hayduk, & Estabrooks (2006) tested the data for model fit. Unfortunately, the models did not fit the data, indicating poor validity (Cummings, Hayduk, & Estabrooks, 2006). Due to this, the concept of the nurse practice environment has been deemed a poorly specified concept (Cummings, et al. 2006).
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That being said, the NQF has selected the NWI-R by Aiken & Patrician (2000) as an instrument to best measure the nurse practice environment in the U.S. They suggest that the instrument is acceptable to use due to the over 70 publications since 2002 supporting its use both in the U.S. and internationally. Along with the evidence that suggests that nurses’ practice environments are part of a causal chain linking nursing care to nurse and patient outcomes. Individual factors of the work environment, such as teamwork, collaboration, communication, and relational coordination have been studied as aspects of the work environment that impact patient outcomes. Relational coordination is defined as the management of the interdependencies between people who carry out tasks. The theory argues that the effectiveness of coordination is determined by the quality of communication among participants in a work process (i.e. in the prevention and control of infections), which depends on the quality of underlying relationships, particularly the extent to which they have shared goals, shared knowledge, and mutual respect (Gittell, 2006). High levels of relational coordination have been linked with improved patient outcomes (Gittell et al., 2000; Havens et al., 2010). In summary, this review has provided insight into the concepts of the work environment that have been empirically studied (Table A1: Organizational Context: Definition of Terms and Dimensions). Further investigation and testing of the concepts in a middle-range theory that explains the direction and relationships between adherence to HAI prevention interventions, organizational context, and HAI outcomes may enhance studies that investigate the influence of the work environment in healthcare.
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Organizational Culture. Organizational culture has been defined as the underlying, organizational level principles, values, and norms of an organization (Schein, 1997). The perception of culture comes from making sense of the environment at the systems level and is a product of group dynamics. What differentiates culture from other contextual concepts is that culture is an attribute of the system and not an attribute of an individual (James et al., 2008). Organizational culture researchers attempt to understand or uncover the key, value-laden issues faced by an organization (Gillespie, Denison, Haaland, Smerek, & Neale, 2008). Historically, investigation of an organizations’ culture entailed qualitative methods with sociological or anthropological origins to identify the unique values and beliefs that characterize a group or organization (Denison, 1996). Today, most culture research in healthcare is conducted through questionnaires that purport to measure a broad set of system level characteristics. There are at least 70 instruments available to researchers to measure the concept of organizational culture, but there is little agreement as to how culture should be conceptualized (Jung et al., 2009). Within the literature, over 100 dimensions associated with organizational culture have been reported. These range from observable phenomena such as rituals, teamwork, focus, leadership, rewards, and vision to abstract ideas such as warmth, satisfaction, and supportiveness (Jung et al., 2009). Typologies that cluster such dimensions into categories are also commonly used. The debate about how best to research culture is ongoing (Jung et al., 2009). Some authors suggest that the measurement of culture should not be a precise act, but be seen as a general means to describe the global concept of “the way things are done” in an organization (Jung et al., 2009).
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An important factor in culture research is the idea that culture reflects a systemlevel orientation and is a property of the system, not of an individual (James et al., 2008). This individual-versus-system orientation is key to separate the concepts of culture and climate, two areas of research that have struggled from conceptual confusion (Denison, 1996; James et al., 2008; Verbeke et al., 1998). The authors of the P-NICER study did not include any variables that fall within the conceptual boundaries of organizational culture. Due to this, a study of the relationship between adherence to HAI prevention interventions, organizational culture, and HAI outcomes will not be included in this project due to dataset limitations. Organizational Climate. Organizational climate has been defined as the overall meaning derived from the aggregation of individual perceptions of a work environment and can be viewed as the outcome of aggregating individuals’ psychological climates (James et al., 2008). Climate researchers generally measure an individual’s perception (or group’s shared perception) for specific constructs using standard questionnaires which can then be used to compare respondents across groups or organizations (Gillespie et al., 2008). Researchers focus on narrow, identifiable organizational characteristics (i.e. safety, service) (Ashkanasy, Wilderom, & Peterson, 2000) which are easier to measure for they are tangible (Gershon, Stone, Bakken, & Larson, 2004; Verbeke et al., 1998). Since its inception, climate research has been conducted through quantitative methods with the goal of generalizing findings across social settings (Denison, 1996). Verbeke (1998) noted that organizational climate has been defined in at least 54 different ways by researchers. This issue continues today. Common dimensions noted in the definitions of climate include the categories of characteristics and perceptions
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(Verbeke et al., 1998) and the immediacy of climate for individuals. Climate has been predominantly described as something that can be sensed by those who enter the environment through things such as the physical appearance of the place, emotionality and attitudes exhibited by employees (Ostroff, Kinicki, & Tamkins, 2003). The benefit of organizational climate research is that the concept is considered relatively temporary and subject to direct control. In other words, a climate can be altered. Organizational climate, specifically the safety climate or patient safety culture, has been operationalized through instruments such as the Patient Safety Climate in Healthcare Organizations Survey (PSCHO) (Singer et al., 2007) and the AHRQ Hospital Survey on Patient Safety Culture (HSPSC) (AHRQ, 2012). The PSCHO, a 42-item, 5point Likert scale instrument, was developed as part of a patient safety research program sponsored by AHRQ to assess the safety climate in hospitals in the U.S. (Singer et al., 2007). The authors define safety climate as the surface features of the safety culture discerned from the workforce’s attitudes and perceptions at a given point in time (Gaba, Singer, Sinaiko, Bowen, & Ciavarelli, 2003). The authors address the debate on whether the instrument is measuring culture or climate through the description of the instrument as a snapshot view of the state of safety climate, which will act as an indicator of the underlying safety culture (Singer et al., 2007). The PSCHO is based on research regarding High Reliability Organizations. The instrument is a nine-dimension model: three dimensions are organizational factors, two are work unit factors, three are individual factors, and one factor relates to report-type questions about the actual incidence of unsafe care (Singer et al., 2007). The organizational factors include senior managers’ engagement in patient safety,
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organizational resources for patient safety, and overall emphasis on patient safety. The work unit dimensions consist of unit norms for patient safety and unit recognition and support for safety efforts. The individual factors include fear of shame, fear of blame, and learning and self-awareness of safety risks. The report-type items consist of questions about whether the respondent has witnessed or been directly involved in the provision of unsafe care. Initial psychometric testing of the instrument demonstrated good discriminant validity and good convergent reliability, (range 0.20-0.77 across the nine dimensions), with six of the nine scales demonstrating acceptable Cronbach’s α levels (Singer et al., 2007). Ongoing testing in diverse hospital settings and populations have demonstrated divergent results, with some studies finding support for an 11-factor scale (Hartmann et al., 2008; Rosen et al., 2010) and others for an eight-factor scale (Singer, Falwell, et al., 2009; Singer, Gaba, et al., 2009; Singer, Lin, et al., 2009). In addition, one study that used the PSCHO reported that hospitals with better safety climates overall had lower relative incidence of patient safety indicators (Singer, Lin, et al., 2009) though this was not supported in a subsequent study in a different population (Rosen et al., 2010). Strengths of the PSCHO include the consistency of the concepts and the theory of High Reliability Organizations, the fact that the authors address the culture and climate confusion, the use of questions that address the individual, unit, and organizational perception, and the ongoing testing of the instrument in diverse hospital populations. A weakness of the instrument is the inconsistent link between safety climate and patient outcomes.
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The AHRQ HSPSC, a 42-item, 5-point Likert scale instrument, was developed to assess the culture of patient safety in healthcare organizations, with an emphasis on error and event reporting (AHRQ, 2012). The goal of this AHRQ funded instrument was to develop a reliable, public-use safety culture instrument that hospitals could administer on their own to assess patient safety culture from the perspective of their employees and staff. The instrument includes ten safety culture dimensions: supervisor/manager expectations and actions promoting patient safety; organizational learning – continuous improvement; teamwork within units; communication openness; feedback and communication about error; staffing; hospital management support for patient safety; teamwork across hospital units; and hospital handoffs and transitions. In addition, two outcome dimensions are measured: overall perception of safety and frequency of event reporting. Initial psychometric testing indicated acceptable reliability (Cronbach’s α 0.630.84) and adequate correlations (range 0.23-0.60). Subsequent testing supported the validity and reliability of the 12-dimension scale at the individual, unit, and hospital level in large, diverse hospital settings (Blegen, Gearhart, O'Brien, Sehgal, & Allredge, 2009; Sorra & Dyer, 2010). Since its development, the instrument has been used in thousands of U.S. and international hospitals and is recommended by the NQF as a tool for hospital quality departments to use to diagnose strengths and weaknesses of their safety culture and benchmark their scores with other hospitals (Blegen et al., 2009). A weakness of AHRQ HSPSC instrument is that the validity of the instrument as predictive of safety outcomes is unknown. Future research would benefit from the inclusion of patient outcomes as a variable of study.
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The overlapping and often interchangeable use of the terms culture, climate, safety climate, and patient safety culture, as exemplified in the PSCHO and the HSPSC, is a challenge for researchers and consumers of research. Due to the similarities within the concepts, some researchers suggest that the concepts have a reciprocal relationship within the context of an organization (Ashkanasy et al., 2000) and the concepts should be related or merged with each other (Moran & Volkwein, 1992). Others suggest that the concepts should remain separate topics of research although they are similar (Rousseau, 1988). For this project, the concepts are viewed as separate, yet complimentary concepts within the context of an organization. The dimensions of culture will reflect a systemlevel orientation, while climate will represent perceptions at the individual or group level (James et al., 2008). In summary, this review has provided insight into the concepts of organizational climate that have been empirically investigated (Table A1: Organizational Context: Definition of Terms and Dimensions). Further investigation and testing of the concepts in a middle-range theoretical model that explains relationships between adherence to HAI prevention interventions, organizational context, and HAI outcomes will enhance studies that investigate the influence of the organizational climate in healthcare. To conclude the review of the conceptual definitions, dimensions and measurement of the contextual concepts, it must be noted that beyond the structural characteristics, the organizational concepts have limitations and are not yet fully specified. The blurring of boundaries in this body of research is challenging and solutions to minimize the confusion continue to be debated. At this time, the most feasible method to advance the field of organizational context in healthcare is to use the contextual
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dimensions of the concepts that have been identified and operationalized in the literature, and are available in the P-NICER dataset, for testing and confirmation of a middle-range theoretical model. A potential weakness in using dimensions from existing tools to guide this study is that they may not reflect the latest theories and concepts, for tool development tends to lag behind conceptual development (French et al., 2009). This may result in inadvertent bias towards the more technical and less theoretical aspects of the concepts. In addition, the measurement of complex phenomena using quantitative methods alone will never provide a complete view of a concept. That being said, theoretically guided research can enhance a program of inquiry and explain relationships that bring clarity to a subject. Client Characteristics The client characteristics variable in the QHOM has been defined and operationalized through patient level data. The authors of the P-NICER study did not include any patient level data in their study. Due to this, the relationship between client characteristics, interventions, context and outcomes will not be possible in this study due to dataset limitations. Interventions The intervention variable in the QHOM was operationalized using the clinical process measures of intensity of adherence to select infection control interventions. Intensity is defined both by the number of applicable processes for any given infection type, as well as observed clinical adherence with the intervention. The infection control process of interest in this study was the CLABSI prevention bundle (Pronovost, Needham, et al., 2006). This evidence-based intervention includes the use of an insertion
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checklist by the healthcare team and five individually recommended processes: (1) monitoring hand hygiene at line insertion, (2) using maximal barrier precautions for line insertion, (3) using chlorhexidine at line insertion site, (4) selecting the optimal catheter site, and (5) checking the line daily for necessity (Marschall et al., 2008). Outcomes The outcome variable in the QHOM was operationalized using CLABSI data reported to the NHSN system from adult ICU patients who have contracted a CLABSI during inpatient care. A CLABSI was defined as a laboratory-confirmed bloodstream infection where a central line was in place for greater than two calendar days on the date of the event (Network, 2013). This outcome was chosen for CLABSIs have been suggested as one of the few valid HAI outcome measures that have been studied with a prevention intervention bundle (Pronovost, Miller, et al., 2006). CLABSIs reported to NHSN are defined using standardized protocols developed by epidemiologists from the CDC and are monitored using surveillance methods and case review with both laboratory and clinical criteria. This, along with the creation of epidemiology and infection control departments to independently monitor, reduce, and report infections, supports the choice of CLABSIs as an outcome variable in this study. In addition, NHSN CLABSI data is a NQF endorsed patient-centered outcome measure for nursing-sensitive care (NQF, 2004). In summary, the conceptual definitions of the QHOM concepts, along with the instruments that have operationalized the dimensions of the concepts have been presented. Though there continues to be some conceptual confusion and ongoing debate on the contextual conceptual boundaries, the dimensions identified in Table A1 were used to guide the selection of variables from the P-NICER dataset. The next section
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presents the QHOM concepts that have been studied with patient outcomes, which is the outcome of interest for this study. QHOM Concepts Studied with Patient Outcomes The purpose of this study was to test a middle-range theoretical model to identify and explain the relationships between the concepts of HAI prevention interventions, organizational context, and HAI outcomes. Studies that have evaluated the relationship between a contextual characteristic, interventions, and patient outcomes, ideally an HAI, are the primary focus of this section. As previously noted, the majority of research in this area has studied patient outcomes such as 30-day morbidity, mortality, re-admissions and composite scores of nurse-sensitive adverse events. The inclusion of an HAI as an outcome measure is relatively new. Due to this, the more traditional outcome measures were included to broaden the review of literature. Organizational Context Variables Organizational Context. Organizational context, as an independent variable, has been positively associated with higher reports of research implementation by healthcare providers in Canadian pediatric settings (Cummings et al., 2010). Though the authors of the ACT offer the instrument as a tool to measure the relationship between context, research utilization and outcomes for patients (Estabrooks, Squires, Hutchinson, et al., 2011), this work has yet to be completed. Krein et al. (2010), in a qualitative study that examined quality improvement efforts and implementation of recommended practices to prevent CLABSIs in U.S. hospitals, revealed that hospitals with a positive emotional and cultural context appeared especially conducive for fostering and encouraging internally motivated patient safety initiatives. Though the link to patient outcomes was not
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evaluated, this study provides excellent grounding for ongoing studies of organizational context and HAIs. To summarize, no published studies that have directly studied the relationships between organizational context and HAIs were found. Due to this, the variable was operationalized in this study through the sub-concepts of structural characteristics, the work environment and organizational climate. Structural Characteristics. In two reviews of the literature, Ayanian and Weissman (2002) and Kupersmith (2005) reported that the most rigorous studies on the effect of teaching status in healthcare demonstrated a moderate to substantially better overall quality of care in major teaching hospitals than in non-teaching hospitals, but the findings varied by health conditions. A study by Wan (1992) suggested that adverse patient outcomes, specifically the trauma rate, unstable medical conditions, treatment problems, postoperative complications, and unexpected deaths, were less likely to be associated with metropolitan hospitals that had a greater number of licensed beds. In addition Medicare case-mix index, a measure of the severity of patient conditions, was an additional factor that influenced patient outcomes (Wan, 1992). Fine, Fine, Galusha, Petrillo, and Meehan (2002) determined that larger hospitals (>250 beds) and teaching status positively influenced timeliness of services for hospitalized Medicare patients with pneumonia. In turn, they suggest that this positively influenced patient outcomes, though this was not a measure included in the study (Fine et al., 2002). Mitchell and Shortell (1997) reported mixed relationships between morbidity, mortality, adverse effects, and hospital structural factors such as size, location, and teaching status. Mark, Salyer and Wan (2000) suggest that the technological sophistication of the hospital, the volume of services offered, and the percentage of
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patients covered by managed care payers act as indirect predictors of patient outcomes. Flood et al. (1984) in a retrospective chart review of 12,000 U.S. hospitals, determined that hospitals with a high volume of procedures demonstrated lower mortality rates than lower volume institutions. This has also been found in a study of patient outcomes postcoronary artery bypass surgery (Ritchie et al., 1999). Shortell, Zimmerman, et al. (1994) reported that the availability of technology such as monitoring and physiologic support equipment has an impact on ICU riskadjusted mortality and risk-adjusted average length of stay. Sales et al. (2011) reported that ICUs that have electronic interface between the hospital-level electronic medical record (EMR) and the Veteran Affairs system-level EMR was associated with lower mortality. In a review of the literature of hospital characteristics associated with improved performance by Brand et al. (2012), the strongest evidence for an association with improved patient outcomes (i.e. mortality, adverse events, satisfaction, and dissatisfaction) was the use of computerized physician order entry, though there was also moderate evidence for network membership and hospital volume. Kim and Black (2011), in a doctoral dissertation to determine the impact of public reporting of CLABSIs in Pennsylvania on CLABSI infection rates, found that the process of public reporting did lower infection rates, on average by 14% over the first four years of public reporting versus a 9% increase in a control group (Kim & Black, 2011). In a more recent study, Strichof, Van Antwerpen, Smith, and Birkhead (2013) reported that in the first three years of public reporting of CLABSI, colon SSI and coronary artery bypass graft chest SSI rates in New York state, significant decreases in the rates of these HAIs were observed. The authors do not explain the finding, nor if the decreases were
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statistically significant, so the relationship between public reporting and the decreased rates is, at this time, only an observation. Multiple research studies have indicated that Magnet hospitals are good places for nurses to work (Kelly, McHugh, & Aiken, 2011), have a positive organizational climate (Stone, Mooney-Kane, Larson, Pastor, et al., 2007), and are settings that have a positive influence on nurses, their decision making, their professional relationships (Hess, Desroches, Donelan, Norman, & Buerhaus, 2011), and nurse-reported quality of care (Stimpfel, Rosen, & McHugh, 2014). The relationship between Magnet certification and nurse-sensitive patient outcomes (i.e. pressure ulcers and failure to rescue) was studied by Mills and Gillespie (2013). They reported no significant differences between riskadjusted rates for pressure ulcers and failure to rescue between Magnet and non-Magnet hospitals. Stone, Mooney-Kane, Larson, Horan, et al. (2007), in a review of nurse working conditions and HAI outcomes did not find positive relationships between Magnet accreditation and any of the patient safety outcomes measured. Work Environment. Aspects of the nurse work environment have been studied in relation to varied nurse-sensitive patient outcomes, including HAIs. Aiken, Clarke, Sloane, Lake, and Cheney (2008) reported that hospitals with better care environments, defined as organizations with high levels of staff development, quality management, nurse manager ability, leadership, support, and collegial nurse-physician relations, have lower surgical mortality rates then hospitals with poor care environments. This study also suggests that the odds on patients dying in hospitals with an average workload of eight patients per nurse is 1.26 times great than in hospitals with average workloads of four patients per nurse. In addition, the odds of patients dying in hospitals in which 60% of the
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nurses held a Bachelor’s of Science in Nursing (BSN) versus hospitals in which 40% or fewer were BSN prepared would be lower by 15%. (Aiken et al., 2008). These findings were supported in a similar study that investigated the nurse work environment, nurse staffing, nurse education, and 30-day readmission for Medicare patients (McHugh & Ma, 2013). Nurse staffing, defined as nurse-patient ratios (Hugonnet, Uçkay, & Pittet, 2007; Manojlovich, Sidani, Covell, & Antonakos, 2011) and the number of hours of nursing care per patient-day (Cimiotti, Haas, Saiman, & Larson, 2006; Manojlovich et al., 2011; Stone, Mooney-Kane, Larson, Horan, et al., 2007) have been implicated in the spread of infection. In a systematic review and meta-analysis of nurse staffing levels, Kane, Shamliyan, Mueller, Duval, and Wilt (2007) reported positive relationships between higher staffing levels and lower hospital-related mortality and lower risk of adverse events such as hospital-acquired pneumonia and bloodstream infections, though not urinary tract infections (Kane et al., 2007). In a more recent study by Cimiotti, Aiken, Sloane, and Wu (2012), a significant association was noted between patient-to-nurse ratio and the incidence of urinary tract infections (p = .02) [UTI] and surgical site infections (p = .04), though when patient severity and nurse and hospital characteristics were controlled, only nurse burnout remained significantly associated with UTI (p = .03) and SSI (p = 95% adherence with infection prevention bundles. This could be achieved through an organizational context that sets rigid goals and provides real-time feedback and rapid cycle change programs when goals are not met. This engineering perspective would require system redesign so that operations could be practiced consistently under a range of conditions (Vincent, Burnett, & Carthey, 2014). The second shift would require a change of the metrics of interest at the executive level. Currently, HAI rates are routinely included in safety reports to hospital boards. Due to this, a poor safety outcome triggers immediate case reviews and root cause analysis events with monitored action plans led by high-level
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management and specialists in quality, safety, and infection prevention. The repercussions for low levels of adherence to infection prevention practices incur unit level meetings to brainstorm methods to gradually improve adherence. Moving the metric to performance on infection prevention practices would refocus the attention to the bedside, where care is occurring. The final shift would require increased attention to the methods for monitoring adherence to prevention practices. Currently, surveillance for adherence with prevention practices is an imprecise science that uses subjective methods such as secret shoppers, self-reporting by clinicians, or monitoring of product usage (Haas & Larson, 2007; Morgan et al., 2013; Urbach, Govindarajan, Saskin, Wilton, & Baxter, 2014). These methods have poor inter-rater reliability and are prone to over-reporting (Boyce, 2008; Gawande, 2014; Leape, 2014). Due to this, there has been work on the use of technology, such as remote video auditing, to monitor and provide real-time feedback on adherence to infection prevention practices (Armellino et al., 2012). Investments in readily available technology may decrease the burden of surveillance while increasing the reliability of the measurement. The findings of this study support a transformation in healthcare from focusing on HAI outcomes to a spotlight on >95% adherence to infection prevention practices. Moving forward, the goal of eliminating infections may be realized through the creation of organizational contexts that facilitate the adoption and sustainment of ideal adherence to infection prevention practices. Study Limitations There are a number of limitations to this study. The original P-NICER study was a cross-sectional survey, limiting the determination of causality. In addition, the P-
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NICER study had a 29% response rate, did not include organizational culture or client characteristic variables, and had a low incidence of CLABSI outcomes. Though this is considered a moderate response rate, the availability of complete data from 614 hospitals that were deemed representative of U.S. hospitals balanced the reported response rate. Future research will attempt to study the concepts longitudinally, using a database that includes all of the QHOM concepts, with the goal of testing the full model while observing trends that can determine predictors of HAI outcomes. The testing of only some of the QHOM concepts may have confounded the confirmation of the model, for it is unknown if the culture and client variables would have affected the outcome to a significant degree. That being said, at the time of this study, the P-NICER database was the only dataset available that contained HAI interventions, instruments that assessed organizational climate and the work environment, structural variables, and HAI outcome data. A second limitation was the use of intervention and outcome data that were selfreported by hospital staff. One issue that has been discussed with the implementation of HAI pay-for-performance initiatives and mandatory reporting is the intense pressure for hospitals to report high adherence with bundle practices and low HAI rates (Furuya et al., 2011; Talbot et al., 2013). This can lead to over-reporting of prevention practices and underreporting of outcomes, which compromises the integrity of HAI surveillance methods and the validity of HAI reporting. In addition, it challenges the ability to draw inferences as to the role of the QHOM concepts and may have influenced the nonsignificant finding between organizational context and CLABSI outcomes. However, respondents did report varying levels of adherence which each element of the CLABSI
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bundle, suggesting that it was unlikely respondents were universally inflating their adherence scores to make themselves look better. Furthermore, the CLABSI outcome rate was within the range reported in the most recent NHSN annual report (CDC, 2013a). Additional limitations included the selection of intervention and outcome data from a single ICU to represent the organization and the use of a non-risk adjusted weighted mean as the outcome variable. The procedure for matching was to determine the largest ICU, as indicated by NHSN bed size, and match that with the type of ICU that respondents reported in the P-NICER survey. Though the majority of hospitals only had one large, medical ICU, some hospitals had multiple, large medical ICUs. To maintain consistency, the largest medical ICU with the most variability in CLABSI outcomes was selected to provide more variance for statistical analysis. Though the risk that respondents were reporting adherence rates for a different ICU existed, the large sample size and goodness of fit statistics for the model minimizes the risk of this limitation. Lastly, the use of the weighted mean as the outcome variable has both benefits and limitations. A noted strength of this method was that it provided some adjustment for central line use versus a pure rate calculation. Obviously, a summary measure that would have allowed for direct comparison between ICUs that adjusts for patient risk factors would have been the preferred method. Unfortunately, the data necessary to calculate such a measure was not available in this dataset. Opportunities for Future Research This study is the first to confirm a middle-range theoretical model that investigates the relationships between adherence to CLABSI interventions, organizational context, and CLABSI outcomes, based on the QHOM. The findings provide guidance to
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the relationship organizational context plays on adherence to CLABSI interventions and CLABSI outcomes. In addition, the confirmation of the RCI and LCQ as valid measures of organizational context can help clinical leaders assess the context of their organizations in preparation for implementation of patient safety interventions. Next steps are to develop a prospective, longitudinal study that studies hospitals with varied levels of adherence to infection prevention practices, while assessing the full complement of organizational context concepts described in the QHOM. This would allow for further testing of the conceptual model and confirmation of the findings in this study. The call for the addition of contextual factors in patient safety research was made four years ago. Since that time, numerous instruments that measure organizational context, the work environment, and organizational climate have been created and tested for validity and reliability. To generate more knowledge in this area, researchers should include an assessment of their organizational context prior to and during implementation studies to facilitate a greater understanding of the role context plays on the success of patient safety programs. The following research questions are posed to expand on the study findings: 1) Organizational context as a mediator between adherence to HAI interventions and HAI outcomes: A longitudinal, prospective SEM study guided by the Quality Health Outcomes Model 2) Surveillance for adherence to infection prevention practices versus surveillance for HAI outcomes. Where should our energies lie?
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Conclusion The prevention of HAIs is a complex topic of research. Organizational context is believed to be a key factor in the success or failure of HAI initiatives. This study confirmed a middle-range theoretical model that identifies and explains the relationships between the concepts of adherence to CLABSI interventions, organizational context, and CLABSI outcomes. Though we have not completely answered the question of why some HAI studies have positive findings and others do not, we are able to offer that the context of an organization has a direct effect on adherence to CLABSI interventions. In addition we encourage a review of the process of measurement and reporting of HAI outcome measures, due to the challenges noted. In the future, HAI studies should include measures that assess organizational context while focusing on processes that facilitate >95% adherence to HAI prevention practices. The findings of this study are important for they support the recommendation that organizational context be measured in HAI prevention studies to determine the role of context in the success or failure of patient safety programs (Shekelle et al., 2010). Furthermore, this study supports the study of organizational context through current organizational climate and work environment instruments such as the RCI and LCQ. These findings suggest that unit or organizational leaders should feel confident in using psychometrically sound tools to assess organizational context prior to implementation of patient safety programs. This will help them determine if the organization is willing and able to foster high levels of adherence to prevention measures, or if components of the climate and work environment need to be altered. In addition, replication of this work
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using common, validated instruments would allow consumers of research to evaluate whether published results are applicable to their own settings (Shekelle et al., 2010). Future work will attempt to build on the relationships offered by the conceptual model, for the long-term goal is to develop a theory-based framework with which to describe and evaluate the concepts of interventions, organizational context, outcomes, and client characteristics to aid in the planning and interpretation of HAI research projects and the explanation of variations in outcomes.
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REFERENCES AHRQ (2012). Hospital survey on patient safety culture. Retrieved August 1, 2013, from http://www.ahrq.gov/qual/patientsafetyculture/hospsurvindex.htm Aiken, L.H., Cimiotti, J.P., Sloane, D.M., Smith, H.L., Flynn, L., & Neff, D.F. (2012). Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Journal of Nursing Administration, 42(10 Suppl), S10-16. doi: http://dx.doi.org/10.1097/MLR.0b013e3182330b6e Aiken, L.H., Clarke, S.P., Silber, J.H., & Sloane, D. (2002). Hospital nurse staffing, and patient mortality, nurse burnout, and job satisfaction. Journal of the American Medical Association, 288, 1987-1993. Aiken, L.H., Clarke, S.P., Sloane, D.M., Lake, E.T., & Cheney, T. (2008). Effects of hospital care environment on patient mortality and nurse outcomes. Journal of Nursing Administration, 38(5), 223-229. doi: http://dx.doi.org/10.1097/01.NNA.0000312773.42352.d7
Aiken, L.H., & Patrician, P.A. (2000). Measuring organizational traits of hospitals: The Revised Nursing Work Index. Nursing Research, 49(3), 146-153. doi: http://dx.doi.org/10.1097/00006199-200005000-00006
Aiken, L.H., Sochalski, J., & Lake, E.T. (1997). Studying outcomes of organizational change in health services. Medical Care, 35(11 Suppl), NS6-18. doi: http://dx.doi.org/10.1097/00005650-199711001-00002
Alison, S.A., McLaughlin, K., & Walker, D. (1991). Nursing theory: A tool to put nursing back into nursing administration. Nursing Administration Quarterly, 15(3), 72-78. doi: http://dx.doi.org/10.1097/00006216-199101530-00012 Altman, D.E., Clancy, C., & Blendon, R.J. (2004). Improving patient safety: Five years after the IOM report. New England Journal of Medicine, 351(20), 2041-2043. doi: http://dx.doi.org/10.1056/NEJMp048243
Anderson, D.J., Kaye, K.S., Classen, D., Arias, K.M., Podgorny, K., Burstin, H., . . . Yokoe, D.S. (2008). Strategies to prevent surgical site infections in acute care hospitals. Infection Control and Hospital Epidemiology, 29(suppl 1), S51-S61. doi: http://dx.doi.org/10.1086/591064 Armellino, D., Hussain, E., Schilling, M., Senicola, W., Eichorn, A., Dlugacz, Y., & Farber, B.F. (2012). Using high-technology to enforce low-technology safety measures: The use of third-party remote video auditing and real-time feedback in healthcare. Clinical Infectious Diseases, 54(1), 1-7. doi: http://dx.doi.org/10.1093/cid/cir773
133
Arnold, K.E., & Thompson, N.D. (2012). Building data quality and confidence in data reported to the National Health Safety Network. Infection Control and Hospital Epidemiology, 33(5), 446-448. Ashkanasy, N.M, Wilderom, C.P., & Peterson, M.F. (2000). Handbook of organizational culture and climate. London: Sage Publications. Ayanian, J.Z., & Weissman, J.S. (2002). Teaching hospitals and quality of care: A review of the literature. Milbank Quarterly, 80(3), 569-593. doi: http://dx.doi.org/10.1111/1468-0009.00023
Backman, L.A., Melchreit, R., & Rodriguez, R. (2010). Validation of the surveillance and reporting of central line-associated bloodstream infection data to a state health department. American Journal of Infection Control, 38(10), 832-838. doi: http://dx.doi.org/10.1016/j.ajic.2010.05.016
Bae, S.H., Mark, B., & Fried, B. (2010). Impact of nursing unit turnover on patient outcomes in hospitals. Journal of Nursing Scholarship, 42(1), 40-49. doi: http://dx.doi.org/10.1111/j.1547-5069.2009.01319.x
Banaszak-Holl, J., Castle, N.G., Lin, M.K., Shrivastwa, N., & Spreitzer, G. (2013). The role of organizational culture in retaining nursing workforce. The Gerontologist, 1-11. doi: http://dx.doi.org/10.1093/geront/gnt129 Beekmann, S.E., Diekema, D. J., Huskins, W.C., Herwaldt, L., Boyce, J.M., Sherertz, R.J., . . . Network, Infectious Diseases Society of America Emerging Infections. (2012). Diagnosing and reporting of central line–associated bloodstream infections. Infection Control and Hospital Epidemiology, 33(9), 875-882. doi: http://dx.doi.org/10.1086/667379
Blegen, M.A., Gearhart, S., O'Brien, R., Sehgal, N.L., & Allredge, B.K. (2009). AHRQ's hospital survey on patient safety culture: Psychometric analyses. Journal of Patient Safety, 5, 139-144. doi: http://dx.doi.org/10.1097/PTS.0b013e3181b53f6e Blegen, M.Q, Goode, C.J., & Reed, L. (1998). Nurse staffing and patient outcomes. Nursing Research, 47(1), 43-50. doi: http://dx.doi.org/10.1097/00006199-19980100000008
Boyce, J.M. (2008). Hand hygiene compliance monitoring: Current perspectives from the USA. Journal of Hospital Infection, 70, 2-7. doi: http://dx.doi.org/10.1016/S01956701(08)60003-1
Brand, C.A., Barker, A.L., Morello, R.T., Vitale, M.R., Evans, S.M., Scott, I.A., . . . Cameron, P.A. (2012). A review of hospital characteristics associated with improved performance. International Journal for Quality in Health Care, 24(5), 483-494. doi: http://dx.doi.org/10.1093/intqhc/mzs044
134
Braun, B.I., Harris, A.D., Richards, C.L., Belton, B.M., Dembry, L., Morton, D.J., & Xiao, Y. (2013). Does healthcare role and experience influence perception of safety culture related to preventing infections? American Journal of Infection Control, 41, 638-641. doi: http://dx.doi.org/10.1016/j.ajic.2012.09.006 Brewer, B.B., Verran, J., & Stichler, J.F. (2008). The Systems Research Organizing Model: A conceptual perspective for facilities design. Herd, 1(4), 7-19. Burnett, E., Lee, K., Rushmer, R., Ellis, M., Noble, M., & Davey, P. (2010). Healthcareassociated infection and the patient experience: A qualitative study using patient interviews. Journal of Hospital Infection, 74(1), 42-47. doi: http://dx.doi.org/10.1016/j.jhin.2009.07.027
Cardo, D., Dennehy, P.H., Halverson, P., Fishman, N.O., Kohn, M., Murphy, C.L., & Whitley, R.J. (2010). Moving toward elimination of healthcare-associated infections: A call to action. Infection Control and Hospital Epidemiology, 31(11), 1101-1105. doi: http://dx.doi.org/10.1086/656912 CDC (2013a). National Healthcare Safety Network. Retrieved June 13, 2013, from http://www.cdc.gov/nhsn/about.html. CDC (2013b). Patient Safety Component - Annual Hospital Survey. Retrieved June 13, 2013, from http://www.cdc.gov/nhsn/forms/57.103_phospsurv_blank.pdf CDC (2014). National and state healthcare associated infections progress report (pp. 1124). Atlanta, GA: Centers for Disease Control and Prevention. Cho, S.H., Ketefian, S., Barkauskas, V.H., & Smith, D.G. (2003). The effects of nurse staffing on adverse events, morbidity, mortality, and medical costs. Nursing Research, 52(2), 71-79. doi: http://dx.doi.org/10.1097/00006199-200303000-00003 Chopra, V., & Shojania, K.G. (2013). Recipes for checklists and bundles: One part active ingredient, two parts measurement. BMJ Quality & Safety, 22(2), 93-96. doi: http://dx.doi.org/10.1136/bmjqs-2012-001480
Christine, K., Cheryl, J., Chunliu, Z., Peter, J.G., & Jayasree, B. (2008). Nurse staffing and postsurgical adverse events: An analysis of administrative data from a sample of US hospitals, 1990–1996. Health Services Research, 37(3), 611-629. doi: https://dx.doi.org/10.1111/1475-6773.00040 Cimiotti, J.P., Aiken, L.H., Sloane, D.M., & Wu, E.S. (2012). Nurse staffing, burnout, and health care-associated infection. American Journal of Infection Control, 40(6), 486-490. doi: http://dx.doi.org/10.1016/j.ajic.2012.02.029
135
Cimiotti, J.P., Haas, J.P., Saiman, L., & Larson, E.L. (2006). Impact of staffing on bloodstream infections in the neonatal intensive care unit. Archives of Pediatric Adolescent Medicine, 160(832-836). doi: http://dx.doi.org/10.1001/archpedi.160.8.832 Coffin, S.E., Klompas, M., Classen, D.C., Arias, K.M., Podgorny, K., Anderson, D.J., . . . Yokoe, D.S. (2008). Strategies to prevent ventilator-associated pneumonia in acute care hospitals. Infection Control and Hospital Epidemiology, 29(S1), S31S40. doi: http://dx.doi.org/10.1086/591062 Coopersmith, C.M, Rebmann, T.L., Zack, J.E., Ward, M.R., Corcoran, R.M., Schallom, M.E., . . . Fraser, V.J. (2002). Effect of an education program on decreasing catheter-related bloodstream infections in the surgical intensive care unit. Critical Care Medicine, 30(1), 59-64. doi: http://dx.doi.org/10.1097/00003246-20020100000009
Cummings, G.G., Hayduk, L., & Estabrooks, C.A. (2006). Is the nursing work index measuring up? Moving beyond estimating reliability to testing validity. Nursing Research, 55(2), 82 - 93. doi: http://dx.doi.org/10.1097/00006199-200603000-00003 Cummings, G.G., Hutchinson, A.M., Scott, S.D., Norton, P.G., & Estabrooks, C.A. (2010). The relationship between characteristics of context and research utilization in a pediatric setting. BMC Health Services Research, 10(168), 1-10. doi: http://dx.doi.org/10.1186/1472-6963-10-168 Denison, D.R. (1996). What is the difference between organizational culture and organizational climate? A native's point of view on a decade of paradigm wars. Academy of Management review, 21(3), 619-654. doi: http://dx.doi.org/10.2307/258997
Dillman, D.A. (1978). Mail and telephone surveys - the total design method. New York, NY: Wiley-Interscience. Dixon-Woods, M., Leslie, M., Bion, J., & Tarrant, C. (2012). What counts? An ethnographic study of infection data reported to a patient safety program. Milbank Quarterly, 90(3), 548-591. doi: http://dx.doi.org/10.1111/j.1468-0009.2012.00674.x Donabedian, A. (1980). Explorations in quality assessment and monitoring: The definition of quality and approaches to its assessment (Vol. 1). Ann Arbor, MI: Health Administration Press. Duffy, M.E. (2002). Methodological issues in web-based research. Journal of Nursing Scholarship, 34(1), 83-88. doi: http://dx.doi.org/10.1111/j.1547-5069.2002.00083.x Encinosa, W.E., & Bernard, D.M. (2005). Hospital finances and patient safety outcomes. Inquiry, 42(1), 60-72. doi: http://dx.doi.org/10.5034/inquiryjrnl_42.1.60
136
Estabrooks, C., Squires, J.E., Hayduk, L.A., Cummings, G.G., & Norton, P.G. (2011). Advancing the argument for validity of the Alberta Context Tool with healthcare aides in residential long-term care. BMC Medical Research Methodology, 11(107), 1-14. doi: http://dx.doi.org/10.1186/1471-2288-11-107 Estabrooks, C., Squires, J.E., Hutchinson, A.M., Scott, S., Cummings, G.G., Kang, S.H., . . . Stevens, B. (2011). Assessment of variation in Alberta Context Tool: The contribution of unit level contextual factors and speciality in Canadian pediatric acute care settings. BMC Health Services Research, 11(251), 1-17. Estabrooks, C.A., Squires, J.E., Cummings, G.G., Birdsell, J.M, & Norton, P.G. (2009). Development and assessment of the Alberta Context Tool. BMC Health Services Research, 9(1), 234. doi: http://dx.doi.org/10.1186/1472-6963-9-234 Estabrooks, C.A., Tourangeau, A.E., Humphrey, C.K., Hesketh, K.L., Giovannetti, P., Thomson, L.D., . . . Shamian, J. (2002). Measuring the hospital practice environment: A Canadian perspective. Research in Nursing & Health, 25(4), 256268. doi: http://dx.doi.org/10.1002/nur.10043 Evans, L. (2013). State HAI Reporting Laws. Retrieved March 29, 2014, from (http://www.apic.org/Resource_/TinyMceFileManager/AdvocacyPDFs/Advocacy_Updates/Static_map_-_HAI_revised_7-6-11.gif). Fawcett, J. (2000). Analysis and evaluation of contemporary nursing knowledge: Nursing models and theories. Philadelphia: F.A. Davis. Field, A. (2009). Discovering Statistics Using SPSS (3rd ed.). Los Angeles: Sage. Fine, J.M., Fine, M.J., Galusha, D., Petrilo, M., & Meehan, T.P. (2002). Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: Results from the Medicare quality indicator system pneumonia module. Archives of Internal Medicine, 162(7), 827-833. doi: http://dx.doi.org/10.1001/archinte.162.7.827
Fink, E.L., & Chen, S.S. (1995). A galileo analysis of organizational climate. Human Communication Research, 21(4), 494-521. doi: http://dx.doi.org/10.1111/j.14682958.1995.tb00356.x
Fitzpatrick, J.J. (1997). Nursing theory and metatheory. In I. M. King & J. Fawcett (Eds.), The Language of Nursing Theory and Metatheory (pp. 37-39). Indianapolis, IN: Sigma Theta Tau.
137
Flood, A.B., Scott, W.R., & Ewy, W. (1984). Does practice make perfect: Part II: The relation between volume and outcomes and other hospital characteristics. Medical Care, 22(2), 115-125. doi: http://dx.doi.org/10.1097/00005650-198402000-00003 Forster, A.J., Dervin, G., Martin, C., & Papp, S. (2012). Improving patient safety through the systematic evaluation of patient outcomes. Canadian Journal of Surgery, 55(6), 418-425. doi: http://dx.doi.org/10.1503/cjs.007811 French, B., Thomas, L.H., Baker, P., Burton, C.R., Pennington, L., & Roddam, H. (2009). What can managment theories offer evidence-based practice? A comparative analysis of measurment tools for organisational context. Implementation Science, 4(28), 1-15. doi: http://dx.doi.org/10.1186/1748-5908-4-28 Furuya, E.Y., Dick, A., Perencevich, E.N., Pogorzelska, M., Goldmann, D., & Stone, P. (2011). Central line bundle implementation in US intensive care units and impact on bloodstream infections. PLoS ONE [Electronic Resource], 6(1), 1-6. doi: http://dx.doi.org/10.1371/journal.pone.0015452
Gaba, D., & Park, D. (2000). Preliminary analysis of hospital safety culture surveys. Palo Alto, CA: Patient Safety Center of Inquiry, VA Palo Alto Health Care System. Gaba, D., Singer, S.J., Sinaiko, A.D., Bowen, J.D., & Ciavarelli, A.P. (2003). Differences in safety climate between hospital personnel and naval aviators. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(2), 173-185. doi: http://dx.doi.org/10.1518/hfes.45.2.175.27238
Gaba, D.M., & Cooper, J. (2000). Landmark report published on patient safety. Journal of Clinical Monitoring and Computing, 16, 231-232. Gagliardi, A.R., Majewski, C., Victor, J.C., & Baker, G.R. (2010). Quality improvment capacity: A survey of hospital quality managers. Quality & Safety in Health Care, 19(1), 27-30. doi: http://dx.doi.org/10.1136/qshc.2008.029967 Gawande, A.A. (2014). When checklists work and when they don't. Retrieved from http://theincidentaleconomist.com/wordpress/when-checklists-work-and-when-they-dont/
Gershon, R.R., Stone, P.W., Bakken, S., & Larson, E. (2004). Measurement of organizational culture and climate in healthcare. Journal of Nursing Administration, 34(1), 33-40. doi: http://dx.doi.org/10.1097/00005110-20040100000008
Gillespie, M.A., Denison, D.R., Haaland, S., Smerek, R., & Neale, W.S. (2008). Linking organizational culture and customer satisfaction: Results from two companies in different industries. European Journal of Work and Organizational Psychology, 17(1), 112-132. doi: http://dx.doi.org/10.1080/13594320701560820
138
Gittell, J.H. (2001). Supervisory span, relational coordination and flight departure performance: A reassessment of post-bureaucracy theory. Organizational Science, 12(4), 467-482. Gittell, J.H. (2002). Coordinating mechanisms in care provider groups: Relational coordination as a mediator and input uncertainty as a moderator of performance effects. Management Science, 48(11), 1408-1426. Gittell, J.H. (2006). Relational coordination: Coordination work through relationships of shared goals, shared knowledge and mutual respect. In O. Kyriakidou & M. Ozbilgin (Eds.), Relational perspectives in organizational studies: A research companion. Cheltenham: Edward Elgar Publishers. Gittell, J.H., Fairfield, K.M., Bierbaum, B., Head, W., Jackson, R., Kelly, M., . . . Zuckerman, J. (2000). Impact of relational coordination on quality of care, postoperative pain and functioning, and length of stay. Medical Care, 38(8), 807819. doi: http://dx.doi.org/10.1097/00005650-200008000-00005 Glisson, C., Landsverk, J., Schoenwald, S., Kelleher, K., Hoagwood, K.E., Mayberg, S., & Green, P. (2008). Assessing the organizational social context (OSC) of mental health services: Implications for research and practice. Administration Policy in Mental Health, 35(98-113). doi: http://dx.doi.org/10.1007/s10488-007-0148-5 Grimm, L.G., & Yarnold, P.R. (2010). Reading and understanding multivariate statistics. Washington, D.C.: American Psychological Association. Haas, J.P., & Larson, E.L. (2007). Measurement of compliance with hand hygiene. Journal of Hospital Infection, 66(1), 6-14. doi: http://dx.doi.org/10.1016/j.jhin.2006.11.013
Haley, R.W., Quade, D., Freeman, H.E., & Bennett, J.V. (1980). The SENIC project. Study on the efficacy of noscomial infection control (SENIC Project). Summary of study design. American Journal of Epidemiology, 111, 472-285. Halpin, H., Shortell, S.M., Milstein, A., & Vanneman, M. (2011). Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs. American Journal of Infection Control, 39(4), 270-276. doi: http://dx.doi.org/10.1016/j.ajic.2010.10.037 Hansen, L.O., Williams, M.V., & Singer, S.J. (2011). Perceptions of hospital safety climate and incidence of readmission. Health Services Research, 46(2), 596-616. doi: http://dx.doi.org/10.1111/j.1475-6773.2010.01204.x Hartmann, C.W., Meterko, M., Rosen, A.K., Zhao, S., Shokeen, P., Singer, S., & Gaba, D.M. (2009). Relationship of hospital organizational culture to patient safety climate in the veterans health administration. Medical Care Research and Review, 66(3), 320-338. doi: http://dx.doi.org/10.1177/1077558709331812
139
Hartmann, C.W., Rosen, A.K., Meterko, M., Shokeen, P., Zhao, S., Singer, S., . . . Gaba, D.M. (2008). An overview of patient safety climate in the VA. Health Services Research, 43(4), 1263-1284. doi: http://dx.doi.org/10.1111/j.1475-6773.2008.00839.x Hatler, C.W. (2004). Examination of the influences of hospital context on outcomes for patients undergoing cardiac catheterization procedures. (PhD), University of Arizona. Havens, D.S., Vasey, J., Gittel, J.H., & Lin, W. (2010). Relational coordination among nurses and other providers: Impact on the quality of care. Journal of Nursing Management, 18, 926-937. doi: http://dx.doi.org/10.1111/j.1365-2834.2010.01138.x He, J., Almenoff, P.L., Keighley, J., & Li, Y.F. (2013). Impact of patient-level risk adjustment on the findings about nurse staffing and 30-day mortality in Veterans Affairs acute care hospitals. Nursing Research, 62(4), 226-232. doi: http://dx.doi.org/10.1097/NNR.0b013e318295810c
Hearld, L.R., Alexander, J.A., Fraser, I., & Jiang, H.J. (2008). How do hospital organizational structure and processes affect quality of care? A critical review of research methods. Medical Care Research & Review, 65(3), 259-299. doi: http://dx.doi.org/10.1177/1077558707309613
Hess, R., Desroches, C., Donelan, K., Norman, L., & Buerhaus, P.I. (2011). Perceptions of nurses in magnet hospitals, non-magnet hospitals, and hospitals pursuing magnet status. Journal of Nursing Administration, 41(7-8), 315-323. doi: http://dx.doi.org/10.1097/NNA.0b013e31822509e2
HHS (2010). Healthy People 2020. Retrieved April 6, 2014, from http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=17
Hofmann, D., & Mark, B. (2006). An investigation of the relationship between safety climate and medication errors as well as other nurse and patient outcomes. Personal Psychology, 59(4), 847-869. doi: http://dx.doi.org/10.1111/j.17446570.2006.00056.x
Huckabay, L.M. (1991). The role of conceptual frameworks in nursing practice, administration, education, and research. Nursing Administration Quarterly, 15(3), 17-28. doi: http://dx.doi.org/10.1097/00006216-199101530-00005 Hugonnet, S., Uçkay, I., & Pittet, D. (2007). Staffing level: A determinant of late-onset ventilator-associated pneumonia. Critical Care, 11(4), 1-7. doi: http://dx.doi.org/10.1186/cc5974
Jacobs, R., Mannion, R., Davies, H.T., Harrison, S., Konteh, F., & Walshe, K. (2013). The relationship between organizational culture and performance in acute hospitals. Social Science & Medicine, 76(1), 115-125. doi: http://dx.doi.org/10.1016/j.socscimed.2012.10.014
140
James, L.R., Choi, C.C., Ko, C.E., McNeil, P.K., Minton, M.K., Wright, M., & Kim, K. (2008). Organizational and psychological climate: A review of theory and research. European Journal of Work and Organizational Psychology, 17(1), 5-32. doi: http://dx.doi.org/10.1080/13594320701662550 Jung, T., Scott, T., Davies, H., Bower, P., Whalley, D., McNally, R., & Mannion, R. (2009). Instruments for exploring organizational culture: A review of the literature. Public Administration Review, 69(6), 1087-1096. doi: http://dx.doi.org/10.1111/j.1540-6210.2009.02066.x
Kane, R.L, Shamliyan, T.A, Mueller, C., Duval, S., & Wilt, T.J. (2007). The association of registered nurse staffing levels and patient outcomes: Systematic review and meta-analysis. Medical Care, 45(12), 1195-1204. doi: http://dx.doi.org/10.1097/MLR.0b013e3181468ca3
Keller, S.C., Linkin, D.R., Fishman, N.O., & Lautenbach, E. (2013). Variations in identification of healthcare-associated infections. Infection Control and Hospital Epidemiology, 34(7), 678-686. doi: http://dx.doi.org/10.1086/670999 Kelly, L.A., McHugh, M.D., & Aiken, L.H. (2011). Nurse outcomes in Magnet and nonMagnet hospitals. Journal of Nursing Administration, 41(10), 428-433. doi: http://dx.doi.org/10.1097/NNA.0b013e31822eddbc
Kim, H., & Black, B. (2011). Does hospital infection reporting affect actual infection rates, reported rates, or both? A case study of Pennsylvania. (PhD Doctoral Dissertation), University of Texas at Austin. Retrieved from http://ssrn.com/abstract=1641459
Kirwan, M., Matthews, A., & Scott, P.A. (2013). The impact of the work environment of nurses on patient safety outcomes: A multi-level modeling approach. International Journal of Nursing Studies, 50(2), 253-263. doi: http://dx.doi.org/10.1016/j.ijnurstu.2012.08.020
Klevens, R.M., Edwards, J.R., Richards, C.L., Horan, T., Gaynes, R.P., Pollock, D.A., & Cardo, D.M. (2002). Estimating healthcare-associated infections and deaths in US hospitals, 2002. Public Health Reports, 122, 160-166. Kohn, K.T., Corrigan, J.M., & Donaldson, M.S. (1999). To err is human: Building a safer health system. Washington, D.C.: National Academy Press. Krein, S.L., Damschroder, L.J., Kowalski, C.P., Forman, J., Hofer, T.P., & Saint, S. (2010). The influence of organizational context on quality improvement and patient safety efforts in infection prevention: A multi-center qualitative study. Social Science & Medicine, 71(9), 1692-1701. doi: http://dx.doi.org/10.1016/j.socscimed.2010.07.041
141
Kupersmith, J. (2005). Quality of care in teaching hospitals: A literature review. Academic Medicine, 80(5), 458-466. doi: http://dx.doi.org/10.1097/00001888200505000-00012
Kutney-Lee, A., Sloane, D.M., & Aiken, L.H. (2013). An increase in the number of nurses with baccalaureate degrees is linked to lower rates of postsurgery mortality. Health Affairs, 32(3), 579-586. doi: http://dx.doi.org/10.1377/hlthaff.2012.0504
Kutney-Lee, A., Wu, E. S., Sloane, D.M., & Aiken, L.H. (2013). Changes in hospital nurse work environments and nurse job outcomes: An analysis of panel data. International Journal of Nursing Studies, 50(2), 195-201. doi: http://dx.doi.org/10.1016/j.ijnurstu.2012.07.014
Ladden, M.D. (2014). Ten years after keeping patients safe: Have nurses' work environments been transformed? Charting Nursing's Future: Reports on Policies That Can Transform Patient Care (March ed., pp. 1-8): Robert Wood Johnson Foundation. Lake, E.T. (2002). Development of the practice environment scale of the Nursing Work Index. Research in Nursing & Health, 25(3), 176-188. doi: http://dx.doi.org/10.1002/nur.10032
Larson, E.L., Early, E., Cloonan, P., Sugrue, S., & Parides, M. (2000). An organizational climate intervention associated with increased handwashing and decreased nosocomial infections. Behavioral Medicine, 26(1), 14-22. doi: http://dx.doi.org/10.1080/08964280009595749
Leape, L.L. (2014). The checklist conundrum. The New England Journal of Medicine, 370(11), 1063-1064. Lin, M.Y., Hota, B., Khan, Y.M., Woeltje, K.F., Borlawsky, T.B., Doherty, J.A., . . . Trick, W.E. (2010). Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. Journal of the American Medical Association, 304(18), 2035-2041. doi: http://dx.doi.org/10.1001/jama.2010.1637 Lo, E., Nicolle, L., Classen, D.C., Arias, K.M., Podgorny, K., Anderson, D.J., . . . Yokoe, D.S. (2008). Strategies to prevent catheter-associated urinary tract infections in acute care hospitals. Infection Control and Hospital Epidemiology, 29(S1), S41S50. doi: http://dx.doi.org/10.1086/591066 Lo, E., Nicolle, L.E., Coffin, S.E., Gould, C., Maragakis, L.L., Meddings, J., . . . Yokoe, D.S. (2014). Strategies to prevent catheter-associated urinary tract infections in acute care hospitals: 2014 update. Strategies, 35(5), 464-479. doi: http://dx.doi.org/10.1086/675718
142
Lohr, K.N. (1988). Outcome measurements: Concepts and questions. Inquiry, 25(1), 3750. MacCallum, R.C., Browne, M.W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypothesis. Psychological Methods, 11(1), 19-35. doi: http://dx.doi.org/10.1037/1082-989X.11.1.19 Manojlovich, M., Sidani, S., Covell, C.L, & Antonakos, C.L. (2011). Nurse dose: Linking staffing variables to adverse patient outcomes. Nursing Research, 60(214-220). doi: http://dx.doi.org/10.1097/NNR.0b013e31822228dc Mardon, R.E., Khanna, K., Sorra, J., Dyer, N., & Famolaro, T. (2010). Exploring relationships between hospital patient safety culture and adverse events. Journal of Patient Safety, 6, 226-232. doi: http://dx.doi.org/10.1097/PTS.0b013e3181fd1a00 Mark, B., Salyer, J., & Wan, T. (2000). Market, hospital, and unit characteristics as predictors of nursing unit skill mix: A contextual analysis. Journal of Nursing Administration, 30(11), 552-560. doi: http://dx.doi.org/10.1097/00005110200011000-00011
Mark, B.A. (1996). Organizational culture. Annual Review of Nursing Research, 14, 145166. Marschall, J., Mermel, L.A., Classen, D.C., Arias, K.M., Podgorny, K., Anderson, D.J., . Yokoe, D.S. (2008). Strategies to prevent central line-associated bloodstream infections in acute care hospitals. Infection Control and Hospital Epidemiology, 29(S1), S22-S30. doi: http://dx.doi.org/10.1086/591059 Mathews, S.C., & Pronovost, P.J. (2012). Establishing safety and quality as core values: A hospital road map. American Journal of Medical Quality, 27(4), 348-349. doi: http://dx.doi.org/10.1177/1062860611424332
Mayberry, L.J., & Gennaro, S. (2001). A quality of health outcomes model for guiding obstetrical practice. Image - the Journal of Nursing Scholarship, 33(2), 141-146. doi: http://dx.doi.org/10.1111/j.1547-5069.2001.00141.x Mayer, J., Greene, T., Howell, J.D., Ying, J., Rubin, M.A., Trick, W.E., & Samore, M.H. (2012). Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clinical Infectious Diseases, 55(3), 364-370. doi: http://dx.doi.org/10.1093/cid/cis410 McClure, M.L., Poulin, M.A., Sovie, M.D., & Wandelt, M.A. (1983). Magnet hospitals: Attraction and retention of professional nurses. Kansas City, MO: American Academy of Nursing.
143
McCormack, B., Kitson, A., Harvey, G., Rycroft-Malone, J., Titchen, A., & Seers, K. (2002). Getting evidence into practice: The meaning of "context". Journal of Advanced Nursing, 38(1), 94-104. doi: http://dx.doi.org/10.1046/j.13652648.2002.02150.x
McDonald, R.P., & Ringo Ho, M. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82. doi: http://dx.doi.org/10.137//1082-989X.7.1.64
McHugh, M.D., & Ma, C. (2013). Hospital nursing and 30-day readmissions among Medicare patients with heart failure, acute myocardial infarction, and pneumonia. Medical Care, 51(1), 52-59. doi: http://dx.doi.org/10.1097/MLR.0b013e3182763284 McKibben, L., Horan, T., Tokars, J.I., Fowler, G., Cardo, D.M., Pearson, M.L., & Brennan, P.J. (2005). Guidance on public reporting of healthcare-associated infections: Recommendations of the Healthcare Infection Control Practices Advisory Committee. American Journal of Infection Control, 33(4), 217-226. doi: http://dx.doi.org/10.1016/j.ajic.2005.04.001
Medicine, Institute of. (2001). Crossing the quality chasm: A new health system for the 21st century. (0309072808). Washington, DC: National Academy Press. Meier, B.M., Stone, P.W., & Gebbie, K.M. (2008). Public health law for the collection and reporting of health care–associated infections. American Journal of Infection Control, 36(8), 537-551. doi: http://dx.doi.org/10.1016/j.ajic.2008.01.015 Mills, A.C., & Gillespie, K.N. (2013). Effect of magnet hospital recognition on two patient outcomes. Journal of Nursing Care Quality, 28(1), 17-23. doi: http://dx.doi.org/10.1097/NCQ.0b013e318268a710
Misset, B., Timsit, J.F., Dumay, M.F., Garrouste, M., Chalfine, A., Flouriet, I., . . . Carlet, J. (2004). A continuous quality-improvement program reduces nosocomial infection rates in the ICU. Intensive Care Medicine, 30(395-400). doi: http://dx.doi.org/10.1007/s00134-003-2096-1
Mitchell, P.H., Ferketich, S. , Jennings, B.M., & Care, American Academy of Nursing Expert Panel on Quality Health. (1998). Quality health outcomes model. Image the Journal of Nursing Scholarship, 30(1), 43-46. doi: http://dx.doi.org/10.1111/j.1547-5069.1998.tb01234.x
Mitchell, P.H., & Lang, N.M. (2004). Framing the problem of measuring and improving healthcare quality. Has the quality health outcomes model been useful? Medical Care, 42(2S), II4-II11. doi: http://dx.doi.org/10.1097/01.mlr.0000109122.92479.fe Mitchell, P.H., & Shortell, S.M. (1997). Adverse outcomes and variations in organization of care delivery. Medical Care, 35(11), NS19-NS32. doi: http://dx.doi.org/10.1097/00005650-199711001-00003
144
Moran, E.T., & Volkwein, J.F. (1992). The cultural approach to the formation of organizational climate. Human Relations, 45(1), 19-47. doi: http://dx.doi.org/10.1177/001872679204500102
Morgan, D.J., Pineles, L., Shardell, M., Graham, M.M., Mohammadi, S., Forrest, G.N., . . . Perencevich, E.N. (2013). The effect of contact precautions on healthcare worker activity in acute care hospitals. Infection Control and Hospital Epidemiology, 34(1), 69-73. doi: http://dx.doi.org/10.1086/668775 Murphy, D.M. (2002). From expert data collectors to interventionists: Changing the focus for infection control professionals. American Journal of Infection Control, 30, I20-I32. doi: http://dx.doi.org/10.1067/mic.2002.120526 Murphy, D.M., Hanchett, M., Olmsted, R.N., Farber, M.R., Lee, T.B., Haas, J.P., & Streed, S.A. (2012). Competency in infection prevention: A conceptual approach to guide current and future practice. American Journal of Infection Control, 40, 296-303. doi: http://dx.doi.org/10.1016/j.ajic.2012.03.002 Neal, A., Griffin, M.A., & Hart, P.M. (2000). The impact of organizational climate on safety climate and individual behavior. Safety Science, 34(1), 99-109. doi: http://dx.doi.org/10.1016/S0925-7535(00)00008-4
Needleman, J., Buerhaus, P., Mattke, S., Stewart, M., & Zelevinsky, K. (2002). Nursestaffing levels and the quality of care in hospitals. New England Journal of Medicine, 346(22), 1715-1722. doi: http://dx.doi.org/10.1056/NEJMsa012247 Nelson, S. (2013). Organizational climate and hospital infection preventionists. (PhD Dissertation), Columbia University, New York. Nelson, S., Stone, P., Jordan, S., Pogorzelska, M., Halpin, H., Vanneman, M., & Larson, E. (2011). Patient safety climate: Variation in perceptions by infection preventionists and quality directors. Interdisciplinary Perspectives on Infectious Disease, 1-7. doi: http://dx.doi.org/10.1155/2011/357121 Network, National Health Safety. (2013). July 2013 CDC/NHSN Protocol Clarifications. Atlanta, GA: Centers for Disease Control and Prevention Retrieved from http://www.cdc.gov/nhsn/PDFs/pscManual/4PSC_CLABScurrent.pdf. NQF (2004). National voluntary consensus standards for nursing-sensitive care: An initial performance measure set. In N. Q. Forum (Ed.), (pp. 1-100). Washington, D.C. Oh, J.Y., Cunningham, M.C., Beldavs, Z.G., Tujo, J., Moore, S.W., Thomas, A.R., & Cieslak, P.R. (2012). Statewide validation of hospital-reported central lineassociated bloodstream infections: Oregon, 2009. Infection Control and Hospital Epidemiology, 33(5), 439-335. doi: http://dx.doi.org/10.1086/665317
145
Ostroff, C., Kinicki, A.J., & Tamkins, M.M. (2003). Organizational culture and climate. In W. C. Borman, D. R. Ilgen, R. J. Kilimoski & I. B. Weiner (Eds.), Handbook of psychology, Vol 12: Industrial and organizational psychology. Hoboken, NJ: Wiley. Page, A. (2004). Keeping patients safe: Transforming the work environment of nurses. Washington, DC: The National Academies Press. Peterson, S.J., & Bredow, T.S. (2009). Middle range theories: Application to nursing research (2 ed.). New York: Lippincott Williams & Wilkins. Pogorzelska, M., Stone, P.W., Furuya, E.Y., Perencevich, E.N., Larson, E.L., Godlmann, D., & Dick, A. (2011). Impact of the ventilator bundle on ventilator-associated pneumonia in intensive care unit. International Journal for Quality in Health Care, 1-7. doi: http://dx.doi.org/10.1093/intqhc/mzr049 Preacher, K.J., & Coffman, D.L. (2006). Computing power and minimum sample size for RMSEA. Retrieved from http://quantpsy.org Price, C.S., & Savitz, L.A. (2012). Improving the measurement of surgical site infection risk stratification/outcome detection Final Report (Prepared by Denver Health and its Partners under Contract No. 290-2006-00-20). Rockville, MD: Agency for Healthcare Research and Quality. Pronovost, P., Goeschel, C., Colantuoni, E., Watson, S., Lubomski, L.H., Berenholtz, S., . Needham, D. (2010). Sustaining reductions in catheter related bloodstream infections in Michigan intensive care units: Observational study. British Medical Journal, 340(c309), 1-6. doi: http://dx.doi.org/10.1136/bmj.c309 Pronovost, P., Holzmueller, C., Needham, D., Sexton, J.B., Miller, M., Berenholtz, S., . . . Morlock, L. (2006). How will we know patients are safer? An organization-wide approach to measuring and improving safety. Critical Care Medicine, 34(7), 1988-1995. doi: http://dx.doi.org/10.1097/01.CCM.0000226412.12612.B6 Pronovost, P., Miller, M.R., & Wachter, R.M. (2006). Tracking progress in patient safety: An elusive target. Journal of the American Medical Association, 296(6), 696-698. doi: http://dx.doi.org/10.1001/jama.296.6.696 Pronovost, P., Needham, D., Berenholtz, S., Sinopoli, D., Chu, H., Cosgrove, S., . . . Roth, G. (2006). An intervention to decrease catheter-related bloodstream infections in the ICU. New England Journal of Medicine, 355(26), 2725-2732. doi: http://dx.doi.org/10.1056/NEJMoa061115 Pronovost, P.J., Marsteller, J.A., & Goeschel, C.A. (2011). Preventing bloodstream infections: A measurable national success story in quality improvement. Health Affairs, 30(4), 628-634. doi: http://dx.doi.org/10.1377/hlthaff.2011.0047
146
Pronovost, P.J., & Sexton, B. (2005). Assessing safety culture: Guidelines and recommendations. Quality and Safety in Health Care, 14, 231-233. doi: http://dx.doi.org/10.1136/qshc.2005.015180
Radwin, L. (2000). Oncology patients' perceptions of quality nursing care. Research in Nursing & Health, 23, 179-190. doi: http://dx.doi.org/10.1002/1098240X(200006)23:3%3C179::AID-NUR2%3E3.3.CO;2-K
Raykov, T., & Marcoulides, G.A. (2006). A first course in structural equation modeling (2nd ed.). New Jersey: Lawrence Erlbaum Associates. Rich, K.L., Reese, S.M., Bol, K.A., Gilmartin, H., & Janosz, T. (2013). Assessment of the quality of publicly reported central-line associated bloodstream infection data in Colorado, 2010. American Journal of Infection Control, 41(19), 874-879. doi: http://dx.doi.org/10.1016/j.ajic.2012.12.014
Ritchie, J.L., Maynard, C., Chapko, M.K., Every, N.R., & Martin, D.C. (1999). Associations between percutaneous transluminal coronary angioplasty volumes and outcomes in the Healthcare Cost and Utilization Project. American Journal of Cardiology, 83(4), 493-497. doi: http://dx.doi.org/10.1016/S0002-9149(98)00901-1 Rosen, A.K., Singer, S., Shibei, Zhao, Shokeen, P., Meterko, M., & Gaba, D. (2010). Hospital safety climate and safety outcomes: Is there a relationship in the VA? Medical Care Research & Review, 67(5), 590-608. doi: http://dx.doi.org/10.1177/1077558709356703
Rostila, I., Suominen, T., Asikainen, P., & Green, P. (2011). Differentiation of organizational climate and culture in public health and social services in Finland. Journal of Public Health, 19, 39-47. doi: http://dx.doi.org/10.1007/s10389-010-03537
Rousseau, D.M. (1988). The construction of climate in organizational research. In C. L. Cooper & I. Robertson (Eds.), International review of industrial and organizational psychology. New York: John Wiley & Sons, Ltd. Sales, A.E., Lapham, G.G., Squires, J., Hutchinson, A., Almenoff, P., Sharp, N.D., . . . Li, Y. F. (2011). Organizational factors associated with decreased mortality among Veterans Affairs patients with an ICU stay. CIN: Computers, Informatics, Nursing, 29(9), 496-501. doi: http://dx.doi.org/10.1097/NCN.0b013e3182148c47 Salive, M.E., Mayfield, J.A., & Weissman, N.W. (1990). Patient outcomes research teams and the Agency for Health Care Policy and Research. Health Services Research, 25(5), 697-708. Sax, L.G., Gilmartin, S.K., & Bryant, A.N. (2003). Assessing response rates and nonresponse bias in web and paper surveys. Research in Higher Education, 44(4), 409-432.
147
Schein, E. (1997). Organizational culture and leadership. San Francisco, CA: JoseyBass. Schreiber, J.B., Nora, A., Stage, F.K., Barlow, E.A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338. doi: http://dx.doi.org/10.3200/JOER.99.6.323-338
Scott, R.D. (2009). The direct medical costs of healthcare-associated infections in US hospitals and the benefits of prevention. Atlanta, GA: Centers for Disease Control and Prevention. Scott-Findlay, S., & Golden-Biddle, K. (2005). Understanding how organizational culture shapes research use. Journal of Nursing Administration, 35(7-8), 359-365. doi: http://dx.doi.org/10.1097/00005110-200507000-00008
Septimus, E., Yokoe, D.S., Weinstein, R.A, Perl, T.M., Maragakis, L.L., & Berenholtz, S.M. (2014). Maintaining the momentum of change: The role of the 2014 updates to the Compendium in Preventing Healthcare-Associated Infections. Infection Control and Hospital Epidemiology, 35(5), 460-463. doi: http://dx.doi.org/10.1086/675820
Shekelle, P.G., Pronovost, P.J., Wachter, R.M., Taylor, S.L., Dy, S., Foy, R., . . . Rubenstein, L. (2010). Assessing the evidence for context-sensitive effectiveness and safety of patient safety practices: Developing criteria. Rockville, MD: Agency for Healthcare Research and Quality. Shortell, S.M., & Singer, S.J. (2008). Improving patient safety by taking systems seriously. Journal of the American Medical Association, 299(4), 445-447. doi: http://dx.doi.org/10.1001/jama.299.4.445
Shortell, S.M., Zimmerman, J.E., & Rousseau, D.M. (1994). The performance of intensive care units: Does good management make a difference? Medical Care, 32(5), 508-525. doi: http://dx.doi.org/10.1097/00005650-199405000-00009 Singer, S. (2003). Safety culture survey results from 12 patient safety consortium hospitals: 2001-2002. Paper presented at the Health Care Quality and Outcomes Conference, San Francisco, CA. Singer, S., Falwell, A., Gaba, D.M., & Baker, L.C. (2008). Patient safety climate in US hospitals: Variation by management level. Medical Care, 46(11), 1149-1156. doi: http://dx.doi.org/10.1097/MLR.0b013e31817925c1
Singer, S., Falwell, A., Gaba, D.M., Meterko, M., Rosen, A., Hartmann, C.W., & Baker, L. (2009). Identifying organizational cultures that promote patient safety. Health Care Management Review, 34(4), 300-311. doi: http://dx.doi.org/10.1097/HMR.0b013e3181afc10c
148
Singer, S., Gaba, D.M., Falwell, A., Lin, S., Hayes, J., & Baker, L. (2009). Patient safety climate in 92 U.S. hospitals: Differences by work area and discipline. Medical Care, 47(1), 23-31. Singer, S., Lin, S., Falwell, A., Gaba, D., & Baker, L. (2009). Relationship of safety climate and safety performance in hospitals. Health Services Research, 44(2P1), 399-421. doi: http://dx.doi.org/10.1111/j.1475-6773.2008.00918.x Singer, S., Meterko, M., Baker, L., Gaba, D., Falwell, A., & Rosen, A. (2007). Workforce perceptions of hospital safety culture: Development and validation of the patient safety climate in healthcare organizations survey. Health Services Research, 42(5), 1999-2021. doi: http://dx.doi.org/10.1111/j.1475-6773.2007.00706.x Sorra, J.S., & Dyer, N. (2010). Multilevel psychometric properties of the AHRQ hospital survey on patient safety culture. BMC Health Services Research, 10, 199. doi: http://dx.doi.org/10.1186/1472-6963-10-199
Sovie, M.D. (1984). The economics of magnetism. Nursing Economics, 2(2), 85-92. Stimpfel, A.W., Rosen, J.E., & McHugh, M.D. (2014). Understanding the role of the professional practice environment on quality of care in Magnet® and NonMagnet hospitals. Journal of Nursing Administration, 44(1), 10-16. doi: http://dx.doi.org/10.1097/NNA.0000000000000015
Stone, P. (2009). Economic burden of healthcare-associated infections: An American perspective. Expert Review of Pharmacoeconomics & Outcomes Research, 9(5), 417-422. doi: http://dx.doi.org/10.1586/erp.09.53 Stone, P., Braccia, D., & Larson, E. (2005). Systematic review of economic analyses of health care-associated infections. American Journal of Infection Control, 33(9), 501-509. doi: https://dx.doi.org/10.1016/j.ajic.2005.04.246 Stone, P., Dick, A., Pogorzelska, M., Horan, T., Furuya, E.Y., & Larson, E. (2009). Staffing and structure of infection prevention and control programs. American Journal of Infection Control, 27, 351-357. doi: http://dx.doi.org/10.1016/j.ajic.2008.11.001
Stone, P., Harrison, M.I., Feldman, P., Linzer, M., Peng, T., Roblin, D., . . . Williams, E.S. (2005). Organizational climate of staff working conditions and safety: An integrative model. Advances in Patient Safety, 2, 467-481. Stone, P., Pogorzelska-Maziarz, M., Herzig, C.T., Weiner, L.M., Furuya, E.Y., Dick, A., & Larson, E. (2014). State of infection prevention in US hospitals enrolled in the National Health and Safety Network. American Journal of Infection Control, 42(2), 94-99. doi: http://dx.doi.org/10.1016/j.ajic.2013.10.003
149
Stone, P.W., Mooney-Kane, C., Larson, E.L., Horan, T., Glance, L.G., Zwanziger, J., & Dick, A.W. (2007). Nurse working conditions and patient safety outcomes. Medical Care, 45(6), 571-578. doi: http://dx.doi.org/10.1097/MLR.0b013e3180383667
Stone, P.W., Mooney-Kane, C., Larson, E.L., Pastor, D.K., Zwanziger, J., & Dick, A.W. (2007). Nurse working conditions, organizational climate, and intent to leave in ICUs: An instrumental variable approach. Health Services Research, 42(3 Pt 1), 1085-1104. doi: http://dx.doi.org/10.1111/j.1475-6773.2006.00651.x Stone, P.W., Pogorzelska, M., Kunches, L., & Hirschhorn, L.R. (2008). Hospital staffing and health care–associated infections: A systematic review of the literature. Clinical Infectious Diseases, 47(7), 937-944. doi: http://dx.doi.org/10.1086/591696 Strichof, R.L., Van Antwerpen, C., Smith, P.F., & Birkhead, G.S. (2013). Lessons learned while implementing mandatory healthcare-associated infection reporting in New York State. Journal of Public Health Management & Practice, 19(4), 294-299. doi: http://dx.doi.10.1097/PHH.0b013e318278502b Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson. Talbot, T.R., Bratzler, D.W., Carrico, R.M., Diekema, D.J., Hayden, M.K., Huang, S.S., . . . Committee, for the Healthcare Infection Control Practices Advisory. (2013). Public reporting of health care-associated surveillance data: Recommendations from the Healthcare Infection Control Practices Advisory Committee. Annals of Internal Medicine, 159, 631-635. doi: http://dx.doi.org/10.7326/0003-4819-159-9201311050-00011
Thornlow, D.K., & Merwin, E. (2009). Managing to improve quality: The relationship between accreditation standards, safety practices, and patient outcomes. Health Care Management Review, 34(3), 262-272. doi: http://dx.doi.org/10.1097/HMR.0b013e3181a16bce
Trick, W.E. (2013). Decision making during healthcare-associated infection surveillance: A rationale for automation. Clinical Infectious Diseases, 57(3), 434-440. doi: https://dx.doi.org/10.1093/cid/cit249 Ullman, J.B. (2007). Structural Equation Modeling. In B. G. F. Tabachnick, L.S. (Ed.), Using multivariate statistics (5 ed., pp. 676-780). New York: Pearson. Urbach, D.R., Govindarajan, A., Saskin, R., Wilton, A.S., & Baxter, N.N. (2014). Introduction of Surgical Safety Checklists in Ontario, Canada. New England Journal of Medicine, 370(11), 1029-1038. doi: http://dx.doi.org/10.1056/NEJMsa1308261
150
Verbecke, W. (1998). Exploring the conceptual expansion with the field of organizational behavior: Organizational climate and organizational culture. Journal of Management Studies, 35(303-329). doi: http://dx.doi.org/10.1111/1467-6486.00095 Verbeke, W., Volgering, M., & Hessels, M. (1998). Exploring the conceptual expansion within the field of organizational behaviour: Organizational climate and organizational culture. Journal of Managment Studies, 35(3), 303-329. doi: http://dx.doi.org/10.1111/1467-6486.00095
Verran, J. (1997). The value of theory-driven (rather then problem-driven) research. Seminars for Nurse Managers, 5(4), 169-172. Vincent, C., Burnett, S., & Carthey, J. (2014). Safety measurement and monitoring in healthcare: A framework to guide clinical teams and healthcare organisations in maintaining safety. BMJ Quality & Safety. doi: http://dx.doi.10.1136/bmjqs-2013002757
Wachter, R.M., & Pronovost, P. (2006). The 100,000 Lives Campaign: A scientific and policy review. Joint Commission Journal on Quality & Patient Safety, 32(11), 621-627. Wan, T.T. (1992). Hospital variations in adverse patient outcomes. American Journal of Medical Quality, 7(2), 50-53. doi: http://dx.doi.org/10.1177/0885713x9200700203 Warren, D.K., Zack, J.E., Cox, M.J., Cohen, M.M., & Fraser, V.J. (2003). An educational intervention to prevent catheter-associated bloodstream infections in a nonteaching community medical center. Critical Care Medicine, 31(1959-1963). Wong, C.A., Cummings, G.G., & Ducharme, L. (2013). The relationship between nursing leadership and patient outcomes: A systematic review update. Journal of Nursing Management, 21, 709-724. doi: http://dx/doi.10.1111/jonm.12116 Yokoe, D.S., Anderson, D.J., Berenholtz, S., Calfee, D.P., Dubberke, E.R., Ellingson, K., . . . Maragakis, L. (2014). Introduction to "A compendium of strateiges to prevent healthcare-associated infections in acute-care hospitals: 2014 updates". Infection Control and Hospital Epidemiology, 35(5), 455-459. doi: http://dx.doi.org/10.1086/675819
Yokoe, D.S., Mermel, L.A., Anderson, D.J., Arias, K.M., Burstin, H., Calfee, D.P., . . . Gerding, D.n. (2008). A compendium of strategies to prevent healthcare-‐‑ associated infections in acute care hospitals. Infection Control and Hospital Epidemiology, 29(S1), S12-S21. doi: http://dx.doi.org/10.1086/591060 Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song, P., Yamin, C., . . . Bates, D.W. (2013). Health care–associated infections: A meta-analysis of costs and financial impact on the US health care system. JAMA Internal Medicine, 173(22), 2039-2046. doi: http://dx.doi.org/10.1001/jamanternmed.2013.9763
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APPENDIX A CONCEPT AND DATA TABLES Table A1: Organizational Context: Definition of Terms and Dimensions Concept Organizational Context
Nurse Practice Environment (i.e. Work Environment)
Definition The environment or setting in which people receive healthcare services (Estabrooks et al., 2009)
A system that supports registered nurse control over the delivery of nursing care the environment in which care is delivered (Aiken, & Patrician, 2000)
Instruments Alberta Context Tool (Estabrooks et al., 2009)
Dimensions Culture Leadership Evaluation
Organizational Social Context (Glisson et al., 2008)
Culture Climate Work Attitudes
Revised Nursing Work Index (Aiken & Patrician, 2000)
Autonomy Control over Practice Relationship with Physicians Organizational Support
Factors Culture Leadership Evaluation Social Capital Informal Interactions Formal Interactions Structural and Electronic Resources Organizational Slack (time, space, human resources) Rigidity Proficiency Resistance Stress Engagement Functionality Morale Support Physician/Nurse Relations Orientation Programs Supportive Supervisor Control over Practice Continuing Education Policies Creative Ideas Enough Staff Accessible Leader Flexible Schedules Staffing Freedom Praise Nurse Specialist Teamwork
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Concept
Organizational
Definition
Instruments
Dimensions
The organizational characteristics of a work setting that facilitate or constrain professional nursing practice (Lake, 2002)
Practice Environment Scale (Lake, 2002)
A set of workplace features that, when present, enable nurses to demonstrate professional practice characterized by decisionmaking, autonomy, clarity of mission, and organizational responsiveness (Estabrooks et al., 2002) The overall
Revised Nursing Work Index (Estabrooks et al., 2002)
Participation in Hospital Affairs Quality of Care Nurse Manager Ability Staffing and Resource NursePhysician Relations Composite – Overarching factor – Practice Environment Scale Practice Environment Index
Patient Safety
Organization
Factors Advancement Nursing Philosophy Control Costs Competent Nurses Quality Program Nursing Governance Joint Practice Preceptor Nursing Model See Aiken & Patrician, 2000 Factors
See Aiken & Patrician, 2000 Factors
Senior
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Concept Climate
Definition meaning derived from the aggregation of individual perceptions of a work environment and can be viewed as the outcome of aggregating individuals’ psychological climates (James et al., 2008)
Instruments Climate in Healthcare Organizations (Singer et al., 2007)
Dimensions Unit Individual Self-Report
AHRQ Hospital Survey on Patient Safety Culture
Safety Culture Outcomes
Factors Management Engagement Organizational Resources Overall Emphasis on Safety Unit Safety Norms Unit Recognition & Support for Safety Fear of Shame Fear of Blame Learning Provision of Safe Care Supervisor/ manager expectations and actions promoting patient safety Organizational learningcontinuous improvement Teamwork within units Communication openness Feedback and communication about error Non-punitive response to error Staffing Hospital management support for patient safety Teamwork across hospital units Hospital handoffs and transitions Overall perception of safety Frequency of event reporting
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Table A2: Hospital Structural Characteristics Variable Setting Urban (1) Suburban (2) Rural (3) Location Northeast (1) South (2) Midwest (3) West (4) Other (AK, PR, HI) [5] Bed Size 1,001 beds (4) Medical School Affiliation Yes (1) No (0) Facility Type Teaching (1) Oncology (2) Orthopedic (3) Surgical (4) Women & Children (5) Facility Ownership Not-for-profit, including church (1) For-profit (2) Government (3) Physician-owned (4) Magnet Status Yes (1) No (0) Mandatory Public Reporting Law Yes (1) No (0) Physician Staffing Hospitalists Yes (1) No (0) Intensivists
307 EFA (n-307) N (%) 67 (22) 106 (35) 131 (43) N (%) 161 (20) 102 (33) 96 (31) 45 (15) 3 (1) N (%) 158 (52) 106 (35) 32 (10) 5 (
