Literature Review in Research: An Annotated Bibliography

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PERCEIVED BARRIERS TO QUALITY IMPROVEMENT

AND REDUCED MEDICAL ERROR:

A QUANTITATIVE STUDY

Cynthia J. Bergs

A Dissertation Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Health Administration

UNIVERSITY OF PHOENIX

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Abstract

This quantitative, descriptive study explored the perceptions of hospital personnel with

regard to quality-improvement initiatives as a collective barrier potentially inhibiting the

reduction of medical error. A sample population of 162 personnel drawn from a

nonprofit hospital within central Florida participated in an online questionnaire known as

the Safety Climate Survey. The findings revealed a statistically significant relationship

between the perceptions of personnel implementing quality-improvement measures and

their positions within the hospital, the largest variance occurring between unit nurses and

hospital administration. No significant relationships were found among the demographic

variables of age, gender, ethnicity, specialty certification, educational level, years with

current employer, years within specialty area, title, shift, employment status, and job

satisfaction. The perception differences between unit nurses and hospital administration

was, overwhelmingly, the strongest indicator of impeded quality-improvement measures.

The findings hold leadership implications for nurse educators, preceptors, administrators,

recruiters, and managers. Recommendations for practice are presented for health-care

leaders to support decisions with the potential to reduce medical error.

Dedication

To my parents who provided the opportunity, strength, and guidance to reach this

life goal. Your own strength, kindness, and perseverance guided me to successful

completion. To my husband, who never questioned the long nights nor my ability.

To all those within health-care leadership who are striving to effectively reduce

medical error and improve the patient experience while acknowledging the perceptions of

those working under their authority. This work is offered as a contribution toward our

mutual goal of improved quality of care.

Acknowledgments

My deepest appreciation is expressed to my mentor, Dr. Mary Tan, who was

always available to provide a solid, clear perspective; answer questions, and offer strong

encouragement. Dr. Tan’s constructive feedback significantly contributed to the

guidance needed in the completion of this study. Committee members, Dr. Julia Aucoin

and Dr. Hans-Peter de Ruiter, also extended invaluable support, enrichment, direction,

and honest feedback. I am grateful for the guidance and support of Dr. Timothy DeGroot

who supported me through the last stages of my dissertation process.

Special thanks to my editor, Jill Eastwood, and my statistician, Fanchao Yi, both

of whom played an important role in my success through their provision of specialized

expertise and guidance.

Table of Contents

List of Tables ..................................................................................................................... ix

List of Figures ......................................................................................................................x

Chapter 1: Introduction……………………………………………………………………1

Background ..............................................................................................................3

Problem Statement ...................................................................................................4

Purpose of the Study ................................................................................................6

Significance of the Study .........................................................................................7

Nature of the Study ..................................................................................................8

Research Questions and Hypotheses .....................................................................11

Theoretical Framework ..........................................................................................12

Definitions …………….........................................................................................15

Assumptions ...........................................................................................................16

Scope, Limitations, and Delimitations ...................................................................17

Chapter Summary ..............................................................................................................19

Chapter 2: Literature Review .............................................................................................21

Historical Overview ...............................................................................................21

Health-Care Culture ...............................................................................................24

Health-Care Leadership .........................................................................................25

Outcomes of Medical Error ...................................................................................27

Perceptions .........................................................................................................................30

Patients and Hospital Personnel ...................................................................................30

Medical Professionals ..................................................................................................32

Improvement Initiatives .....................................................................................................34

Reduction Strategies ....................................................................................................37

Regulatory Oversight ...................................................................................................40

Local Mandates ............................................................................................................42

Disparities ..........................................................................................................................44

Culture of Safety ................................................................................................................46

Reporting............................................................................................................................49

Quality Improvement .........................................................................................................51

Chapter Summary ..............................................................................................................52

Chapter 3: Research Methods ............................................................................................54

Research Design…….............................................................................................54

Methodology Appropriateness…………………………………………………...55

Accomplishing the Study’s Goals………………………………………………..56

Informed Consent and Confidentiality.........................................................................58

Population and Sampling .............................................................................................59

Data Collection Methods .......................................................................................62

Instrument ….. .......................................................................................................63

Validity and Reliability ..........................................................................................64

Data Analysis .........................................................................................................66

Chapter Summary ..................................................................................................69

Chapter 4: Results ..............................................................................................................70

Instrument………………………………………………………………………..70

Demographic Data ………………………………………………………………71

Findings …….........................................................................................................73

Chapter Summary ..................................................................................................93

Chapter 5: Conclusions and Recommendations ................................................................95

Findings and Interpretations………………………………………………..……96

Limitations……………………………………………………………………….99

Implications and Recommendations……………………………………………100

Future Research……………………………………………………….………..102

Chapter Summary……………………………………………………………... 103

References ........................................................................................................................105

Appendix A: Study-Site Permission ................................................................................129

Appendix B: Informed Consent .......................................................................................130

Appendix C: Invitation to Participate ..............................................................................131

Appendix D: Permission to Use Survey ..........................................................................132

List of Tables

Table 1. Mean and Standard Deviations for Age and Experience of the Study

Participants ........................................................................................................................73

Table 2. Frequency Distribution for the Administrators Study Group (n = 44) ............77

Table 3. Frequency Distribution for the Nurse-Managers Study Group (n = 37).........79

Table 4. Frequency Distribution of the Nurses Study Group (n = 81) ..........................80

Table 5. Descriptive Statistics From Survey Responses ................................................81

Table 6. Spearman Product-Moment Correlation .........................................................83

Table 7. Kruskal-Wallis Test and Multiple Comparison ...............................................84

Table 8. Survey Responses According to the Age of the Participants ...........................86

Table 9. Survey Responses According to Years of Participant Experience

in Position ..........................................................................................................................86

Table 10. Survey Responses According to Participant Experience in Specialty .............86

Table 11. Survey Responses According to Years of Participant Experience

in Organization ..................................................................................................................87

Table 12. Gender of Study Participants ...........................................................................88

Table 13. Cronbach’s Alpha Results by Domain .............................................................89

Table 14. Basic Statistics .................................................................................................90

Table 15. Normality Check ..............................................................................................90

Table 16. Constant Variance Check ................................................................................92

List of Figures

Figure 1. Number and percentages of study participants by gender .................................72

Figure 2. Mean age of the study participants and mean years of experience

within the health-care field ................................................................................................73

Figure 3. Safety-climate domains based upon participant age .........................................75

Figure 4. Safety-climate domains based upon number of years participants

in specialty .........................................................................................................................76

Figure 5. Safety-climate domains based upon years of participant experience

in position...........................................................................................................................76

Chapter 1: Introduction

Medical errors and other human mistakes within the hospital setting can result in

unnecessary injury to patients or death (Kohn, Corrigan, & Donaldson, 2000). The

consequences have been identified by various studies (Berntsen, 2004; Young, 2005),

motivating multiple initiatives toward improved quality of medical care within hospitals.

Special-interest groups have taken action to encourage hospital administrators to make

related changes to reduce medical error. This action has included new patient-safety

standards, mandatory and voluntary event reporting, and public awareness through

hospital-performance ―scorecards‖ (Deavers, Pham, & Liu, 2004; Pawlson, 2002;

Weinberg, Hilborne, & Nguyen, 2005). This current study was conducted to determine

whether the perceptions of hospital personnel regarding barriers to implementing quality-

improvement measures contributed to either the reduction or elimination of medical error

within hospitals.

Hospital leaders have instituted strategies to reduce or eliminate medical error in

the interest of risk management, to adhere to new regulations, and to decrease the

potential loss of customers from public awareness of such error (Messner, 1998; Orser,

2000). A key factor to reducing patient injury from such error is public awareness

because it spurs implementation of the appropriate steps for institutional protection.

However, barriers can exist that impede quality-improvement efforts. Messner (1998)

cited organizational culture, restructuring, quality-control functions, and costs as

contributing to slowed progress with quality-improvement programs. According to

Walshe and Shortell (2004), the future success of such programs ―depends on cultural as

much as structural change in health care systems and organizations‖ (p. 103).

Steep authority hierarchies, lack of teamwork, an unwillingness to acknowledge

human fallibility, and the tendency to take punitive action rather than learn from error are

all prevailing aspects of the organizational and professional culture within the realm of

health care. These characteristics act as barriers to quality care improvement and patient

safety (Akins & Cole, 2005; Sexton, Pronovost, & Thomas, 2000; VanGeest & Cummins

2003). Scott (2003) found that highly skilled workers employed in hospital positions

may be resistant to formal organizational structure and controls. Such organizational

resistance can also serve as a barrier to accepting additional quality-improvement

controls and structure. Additional research on related change initiatives and barriers may

contribute to the improvement of care quality and reduce medical error while adding to

the existing body of knowledge within this area of study.

The aim behind this current quantitative study was to explore the perceptions of

hospital personnel related to quality-improvement initiatives as barriers potentially

inhibiting the reduction of medical error. Addressing such reduction is difficult. No

standardized system of measurement exists, resulting in various interpretations of data.

Additionally, the perceptions of both patients and medical personnel, in terms of existing

barriers, may impede progress toward the reduction of medical error. The perceptions of

nurses, hospital management, and administrative leaders may also affect quality-

improvement measures toward such reduction (Bognár et al., 2008). In the current study,

data were collected from a sample of nurses, as well as management and administrative

leaders within the health-care setting, with the aim of providing a clearer understanding

of how perceived barriers to the implementation of quality-improvement systems may

adversely affect the reduction of medical error. Existing literature was reviewed on the

impact of related regulations and quality systems in place, as well as research focused on

how patients and medical personnel perceive hospital cultures of safety.

Background

A number of patients are injured each year within the United States as a result of

unnecessary medical error within hospital settings (Bilawka & Craig, 2003; Kohn et al.,

2000). The Harvard Medical Practices Study reviewed patient records during 1984 at 51

hospitals located within the state of New York (Brennen et al., 1991). A substantial

amount of medical error was found to be due to negligence. The Institute of Medicine

(IOM) introduced the Quality of Healthcare in America Project during 1998 to

investigate the problem of medical error and develop improvement strategies (Kohn

et al., 2000). The project (IOM, 1999) identified medication procedural error and

diagnostic error as common problems within hospitals. Their prevention requires

systems improvement with a goal toward higher quality patient care.

The Harvard Medical Practices Study (Brennan et al., 1991) and the IOM (1999)

report became catalysts for the public awareness that brought not only public but also

media attention to the development of possible solutions for medical error. During 2003,

the Joint Commission on Accreditation of Healthcare Organizations (JCAHO; 2005)

issued new patient-safety standards that prompted multiple states to enact mandatory

medical-error reporting (Weinberg et al., 2005). Both published studies on medical error

and hospital-performance scorecards stimulated public awareness and demanded overall

improvement in health care (Collins, Block, Arnold, & Cristakis, 2009). However,

despite the subsequent initiatives, implementation of standards, and mandatory reporting,

evidence of these measures resulting in sufficient improvement in patient care and a

positive effect on reducing medical error is inconclusive. This is due to a lack of

consistent, comparable data, as well as a lack of protocol standardization in the

measurement and evaluation of such error (Berntsen, 2004). In fact, studies have

indicated an increased amount of medical error following initiative implementation

(Berntsen, 2004; Young, 2005). Barriers to progress in hospital-improvement systems

include (a) inconsistent definitions, (b) voluntary versus mandatory reporting, and

Problem Statement

A general problem for the health-care industry is the increased medical error that

has resulted in as many as 98,000 deaths per year and adds $29 billion to annual health-

care costs (Berntsen, 2004). The specific problem is the perceptions of hospital

personnel regarding quality-improvement initiatives, which may act as a collective

barrier inhibiting the reduction of medical error. Studies conducted after the related IOM

(1999) report revealed minimal hospital progress toward reducing the number and

significance of medical errors (Berntsen, 2004; Young, 2005). As a result, federal and

state mandates were imposed on health-care providers toward this end (Weinberg et al.,

2005).

Health-care organizations have instituted safer practice and agencies and

professional societies have issued safety guidelines and recommendations to address

medical error. Additionally, funding was dedicated to patient-safety research. The

JCAHO issued national patient-safety goals, and patient-safety legislation was introduced

(Burney, 2001; Deavers et al., 2004). Despite these efforts, health care is not measurably

safer than it was in 2000 (Weinburg et al., 2005). Existing data suggest an

underestimation of the magnitude of preventable health-care error. Many errors continue

to go unreported; consequently, accurate incidence rates are unknown (Berntsen, 2004).

The use and effectiveness of safety measures are also unknown due to unreliable outcome

measures.

Advances in patient safety include the increased use of technology to reduce

medical error, increased training toward improved teamwork, and full disclosure of

medical error (Leape & Berwick, 2005). Barriers to progress include the culture and

complexity of health care, continuing skepticism resulting in the perception of system

failures as the underlying cause of most health-care error, and the fear of malpractice

liability that inhibits a willingness to discuss or even admit such error. Research on

causal factors has been traditionally focused on clinical indicators. While this body of

study was centered in specific errors occurring within specific situations, the need

remains to find evidence of commonality among measures and related theory (Rathert,

Fleig-Palmer, & Palmer, 2006). Few empirical studies have asked frontline employees

for their perceptions of key contributing factors in the prevention of medical error.

Reason (2001) indicated that hospital employees can add valuable information in this

regard because they ―are at the sharp end of complex systems‖ (p. 14).

The current quantitative study was conducted to determine whether the

perceptions of hospital personnel created barriers to the implementation of quality-

improvement measures to reduce or eliminate medical error. The population sample

included nurses, health-care managers, and hospital administrators. The findings may

benefit hospital leaders seeking successful implementation of quality-improvement

strategies to reduce medical error through clearer communication of goals, increased

management and support, and planning that is adequate to effectively address the

problem under study (Messner, 1998).

Purpose of the Study

The purpose of this current quantitative study was to explore the perceptions of

hospital personnel regarding quality-improvement initiatives as a collective barrier

potentially inhibiting the reduction of medical error. The research method was suitable

for the study because it led to identifying possible solutions to ineffective quality-

improvement processes. The independent variable for this study was the perceptions of

barriers to quality care improvement measured by a Likert-type scale survey. The

dependent variable was the statistical data of the sample characteristics.

A simple descriptive survey design was appropriate for this study because it

supported a description of the characteristics or behaviors of a particular population in a

systematic and accurate fashion. A systematic sample was selected by obtaining a list of

employees meeting the following criteria: (a) employed a minimum of six months within

the study-site hospital; and (b) currently employed as a floor nurse, nurse manager, or

administrative employee. The participants were chosen through stratified, systematic

sampling. Every employee meeting the criteria was selected for study participation. The

goal was to obtain 110 total participants.

Discoveries are measurable in quantitative study, and data are presented from an

objective rather than subjective viewpoint (Balnaves & Caputi, 2001). Questions

surrounding the perceptions of the participants in the current study regarding the culture

of safety within the health-care environment were formulated to use quantifiable data for

explaining and predicting phenomena (Creswell, 2003). Ordinal data were collected

through a Likert-type, self-administered survey completed by hospital personnel. At the

ordinal level of measurement, the data were ranked in a manner resulting in an order to

the data but with no definite interval.

Significance of the Study

A survey on the safety climate of hospitals was expected to detect employee

concerns related to patient safety and help foster communication on this topic; however,

limited evidence existed to indicate that survey scores are related to patient-safety

outcomes (Colla, Bracken, Kinney, & Weeks, 2005). The findings contributed to

existing knowledge on the effectiveness of quality-improvement programs in reducing

medical error within hospital settings through clearer communication of organizational

goals, increased management and support, and planning that is adequate to effectively

address the problem under study (Messner, 1998; Rathert et al., 2006). Rathert et al.

(2006) recommended that further study address the perceptions of frontline hospital

employees in this regard. Such study was expected to validate the problems and lead to

positive change via a system approach. Increasing knowledge surrounding quality

improvement and perceptions contributing to medical error is important to both American

society and hospital administration because it addresses implementation of the proper

tools to reduce such error. This, in turn, will reduce the potential for patient injury or

death and the medical costs associated with related insurance coverage and lawsuits.

Responsibility for quality care must involve both clinicians and nonclinicians and

their effective interaction in response to conflict (Lagrosen & Lagrosen, 2006;

Reinertsen, 2005; Stanley, 2006). The working/professional relationship between

employees and leadership influences work attitudes (Tangirala, Green, & Ramanujam,

2007). Leadership is not static, and the influence of these relationships can be a predictor

of employee performance (Bauer, Erdogan, Linden, & Wayne, 2006; Tangirala et al.,

2007). The ability of management to acknowledge and understand employee

misconceptions surrounding quality improvement can result in improved methods of

training and deployment. Multidisciplinary health-care teams are reliant upon

information and tools they are provided to dispense quality care to patients.

Understanding differences in the perceptions of nurses, managers, and administrators

regarding quality-improvement processes was expected to provide a better opportunity

for the development of quality-improvement processes.

Nature of the Study

In the current study, a quantitative survey design was applied to collect data

facilitating description of the perceptions of medical personnel surrounding barriers to

implementing quality-improvement measures that may impede the reduction of medical

error. Data analysis addressed the research questions and hypotheses via simple

frequency distribution, central tendency, and variability. Several factors justify the

application of a quantitative design such as the type of data collected, analyzed, and

interpreted; identified variables; and verified theories or explanations supporting the

proposed hypothesis (Creswell & Clark, 2007).

The use of quantitative research designs may determine whether relationships

exist between variables while controlling certain occurrences (Leedey & Ormrod, 2001).

Researchers have used quantitative descriptive designs to question participants

surrounding their attitudes, opinions, and behaviors and to find relationships between

respondent characteristics and the behaviors they exhibit. The design also provides an

opportunity for participants to be autonomous with regard to their particular roles in

quality improvement because the survey allows for the collection of information through

confidential means. Survey responses were extended in an anonymous fashion with no

identifying information collected such as names or addresses. Each participant was

assigned a five-digit identification number to protect confidentiality.

Quantitative methods analyze variables, test hypotheses, measure numbers,

replicate findings, and generate statistics (Neuman, 2003). Quantitative data may

produce identifiable trends across a broad spectrum of participants, as well as identify

improvement processes, controls, and results (Creswell, 2003). Quantitative

methodologies focus on surveys and the statistics drawn from the data. A quantitative

research design was appropriate for the current study due to its potential for identifying

possible solutions for ineffective quality-improvement processes.

A descriptive, quantitative method was appropriate for this study because of its

logic of inquiry, which gains a greater amount of information on a particular

characteristic within a particular field of study. Barriers or problems to implementing

quality-improvement programs were identified (Creswell, 2003). There was no

manipulation of variables or attempt to establish causality occurred. Quantitative studies

produce measurable or testable data that are objective in nature and provide a general

conclusion from specific findings (Balnaves & Caputi, 2001).

The data collected in quantitative research provides information that can be

applied to a more generalized population through theories and/or hypotheses pertaining to

the phenomena under study. Such research is typically conducted from an approach that

views knowledge as acquired through direct observation and experimentation.

Consequently, in the current research, data were collected from a select group of

participants who were representative of a larger population. The transferability of data

refers to how findings can be generalized or transferred to other contexts or settings.

Thus, the survey administered in the current study included questions surrounding the

relationships among measured variables and using numeric data for purposes of

explaining phenomena.

A simple, systematic survey ensured the equal probability of participation in the

current study. Respondents were grouped into subsets sharing particular characteristics

(Creswell, 2003), which included their professional position as a general nurse, nurse

manager, or administrator within a central-Florida hospital. Demographic information

included gender; age; marital status; educational level; employment (i.e., full or part

time); and occupational status. The aim of sample selection was to obtain an unbiased

cross section of a hospital population. The simple descriptive survey design was

appropriate for the quantitative method to gain a general sense of the phenomenon under

study and to form theories that could be tested in future quantitative research (de la Torre,

2011; Polonsky & Waller, 2005).

A qualitative method was not deemed appropriate for the current study because

such research ―is typically used to answer questions about the complex nature of

phenomena, often with the purpose of describing and understanding the phenomena from

the participants’ point of view‖ (Leedy & Ormrod, 2001, p. 101). While qualitative

methods are effective in the appropriate research environment, ―qualitative researchers

construct interpretive narratives from their data and try to capture the complexity of the

phenomenon under study‖ (p. 103). The current study is quantitative in nature because

the research was conducted to gain a clearer understanding of intentionality or meaning.

Quantitative research is designed to quantify relationships between variables. This study

explored the perceptions of hospital personnel regarding quality-improvement initiatives

as a collective potential barrier inhibiting the reduction of medical error. The

identification of relationships and the measurement of variables was conducted through

descriptive statistics (Hopkins, 2000).

A self-administered, Likert-type survey provided a quantitative description of the

trends, attitudes, and opinions of the population sample in the current study (Creswell,

2005). Survey research enables generalization of the findings and inferences surrounding

particular characteristics or attitudes. A simple descriptive design allows the opportunity

to determine specific times for data collection and analysis (Polonsky & Waller, 2005), as

opposed to a longitudinal design that produces immediate results. A survey questionnaire

was appropriate for the statistical analysis of the current study because it generated

quantitative data with measurable findings (Balnaves & Caputi, 2001). Overall, the

simple descriptive design accomplished the goals of the research.

Research Questions and Hypotheses

The following research questions and corresponding hypotheses guided this

study:

R1.Do perceptions of barriers exist that influence quality care improvement within

hospitals and the reduction of medical error? H1A stated that significant barriers

exist that impede quality care improvement within hospitals and the reduction of

medical error. H10 stated that no significant barriers exist that impede quality

care improvement within hospitals and the reduction of medical error.

R2. Do the perceptions of barriers to quality care improvement within hospitals

differ among nurses and hospital managers and administrators? H2A stated that the

perceptions of nurses and hospital managers and administrators significantly

differ with regard to barriers to quality care improvement within the hospital

setting. H20 stated that the perceptions of nurses and hospital managers and

administrators do not significantly differ with regard to barriers to quality care

improvement within the hospital setting.

Theoretical Framework

Organizational-change theory explains the safety of patients and employees

within health-care environments and facilitates the advancement of health-care

institutions toward the successful implementation of quality improvement.

Organizational change identifies the commitment of organization members toward

change and the efficacy of change implementation. Specific habits, learned ―work-

arounds,‖ and organizational cultures can contribute to the stagnation of quality-

improvement programs (Weiner, 2009). Herscovitch and Meyer (2002) observed that

organizational members can commit to implementing organizational change because it is

their personal desire to do so (i.e., they value the change); because they are expected to

do so (i.e., they have little choice); or because they feel they must do so (i.e., they feel

obliged). True commitment is based upon personal motives that reflect the highest level

of commitment to organizational change.

Quality and quality improvement have been considered concepts of importance

for centuries; yet, they continue to lack universally accepted definitions and theoretical

basis. Early quality-improvement efforts within the realm of health care included the

standardization of nursing care and the development of medical-education standards in

1917 for hospital physicians by the American College of Physicians in 1917 (Bilawka &

Craig, 2003). One approach to the development of a related theoretical basis segregates

the broad concept of quality into two distinct areas—quality management practice and

quality performance (Fynes, 1999). The benefit of this approach is its separation of

theory and models into process and measurement. A second approach is to separate the

definitions of product and service quality (Bright & Cooper, 1993).

The diversity of quality definitions and lack of consensus have contributed to

scarce research into the development of quality theory and models (Bright & Cooper,

1993; Fynes, 1999). Quality-improvement theory is an integration of several

management theories including strategic planning, organizational-change processes, and

double-loop learning processes (Bilawka & Craig, 2003). Strategic planning not only

establishes a vision and goal for an organization, but also identifies the desired goals and

ultimate state of the organization. Change management is the process of changing the

organizational culture and behavior of the participants to progress toward the desired end

state. Double-loop learning is the process of implementing feedback from participants

and customers to identify changes or other factors affecting progress toward the desired

end state.

Quality controls are multilevel processes within complex organizations.

Institutional quality processes and controls are developed for specific operations

impacting the success of an organization. Institutional quality improvement within

hospital settings is focused on the interrelationships between policy, control, and

operational functions (Scott, 2003). Multiple feedback loops monitor and correct output

from the operational functions. Feedback loops are necessary to identify deviations from

the planned outcomes and potential weaknesses within the operations.

Contemporary theories and models for quality improvement began to emerge

following World War II, during the reconstruction of the Japanese economic system

(Landesberg, 1999). During the 1950s, both Deming and Juran participated in the

rebuilding of Japanese industry (Dotchin & Oakland, 1992). The Deming (as cited in

Ravichandran & Rai, 2000) quality-improvement theory suggests that a systems view of

quality is necessary to address the interrelationships between stakeholders. The Juran

(1986) theory focuses on a management approach to quality and quality-improvement

processes. Together, the success of Deming and Juran in the recovery of Japanese

industry gave rise to the interest of U.S. organizations in quality and quality-improvement

models (Walton, 1986). Accredited hospitals receive certification through compliance

with the patient-safety standards developed by the JCAHO. These standards are

consistent with the major elements of contemporary quality-improvement theories and

models.

The theory of planned behavior evaluates the manner in which human action is

guided (Eccles, Hrisos, Steen, Bosch, & Johnston, 2009). Such a theory-based approach

identifies the potential to generate a framework within which to consider factors

influencing behavior and the development of interventions toward their modification.

The model hypothesizes that three cognitive variables will predict the intention to

perform a behavior. The intention is the major precursor of the behavior; however,

perceived behavior control is also a predictor. The application of this theory in the

current study to identify perceived barriers of quality improvement toward reduced

medical error was expected to facilitate determination as to whether good intentions can

be prevented from becoming actions due to a perceived internal or external barrier.

Definition of Terms

To ensure shared meaning, definitions of operational terms are necessary

(Creswell, 2005). The following terms are used throughout the current study and are

defined for purposes of the research:

Barriers are factors impeding the implementation of error-reduction techniques

(McFadden, Stock, & Gowen, 2006, p. 127).

Employee perception is the employee interpretation of the impact of quality-

improvement programs on work responsibilities. Walz-Feher, Strickland, and Lenz

(1991) indicated that the theoretical definition includes ―critical attributes of the

concept’s meaning that differentiate it from other terms‖ (p. 39). Three key steps in the

process of developing a theoretical definition are (a) the extent to which assistance is

needed, (b) the type of research information obtained, and (c) the level of satisfaction.

The process of identifying individual perceptions reveals differences in the manner in

which expectations are set. The operational definition of perception is the means used to

measure the variables of interest. A Likert-type survey measured the perceptions of

selected frontline personnel in the current study.

Medical error refers to ―the failure of a planned action to be completed as

intended or the use of a wrong plan to achieve an aim‖ (Kohn et al., 2000, p. 54).

Quality is ―the extent to which the health care provided is expected to achieve the

most favorable balance of risks and benefits‖ (De Leon, 2004, p. 1).

Quality assessment is ―a process for measuring quality of care. It consists of

numerous approaches which define quality of care, select indicators for measurement,

collect data, [and] analyze and interpret results‖ (Larson & Muller, 2003, p. 2).

Quality assurance is ―an effort to change or improve the level of health care based

upon measures of quality‖ (Larson & Muller, 2003, p. 2).

Quality of care is ―the minimum acceptable level of performance or results, what

constitutes excellent performance or results, and the range in between‖ (Kinney, 2001,

p. 2).

Quality improvement refers to ―the ongoing systematic process of using quality

measurements to identify problems and to implement strategies to improve the quality of

care‖ (Weissman et al., 2005, p. 3).

Transferability refers to ―generalizability of the findings and results of the study

to other settings, situations, populations, or circumstances‖ (Lincoln & Guba, 1990,

p. 56).

Assumptions

An assumption is an accepted belief in the absence of evidence to the contrary

(Ramsey, 2005). The current study was subject to several assumptions. Nurses and

hospital managers and administrators were assumed to be willing to participate in

research that identifies their perceptions surrounding why quality improvement is not

more effective within their employing institutions. Medical error can have significant

legal and financial implications to the institution and negative consequences for

employees. It was also assumed in this study that health-care leaders may be hesitant to

participate in research that may result in negative findings surrounding their employing

organizations. However, survey distribution through internal mail generated reversal of

this concern. Unlike self-administered surveys, the distribution of questionnaires through

an internal mail system provides sufficient privacy for participants to respond with

confidence in terms of confidentially (Rea & Parker, 2005). Additionally, use of a

secured, advanced tracking Web site allowed the exclusion of any information identifying

participants.

Another assumption of the current study was that the participants would be honest

in their survey responses. With self-reported data, respondents can be prone to memory

error or harbor an unwillingness to disclose accurate information (Rea & Parker, 2005).

Hospital personnel may desire to project a positive image regarding their institutions,

particularly when dealing with a potentially controversial topic such as medical error.

Consequently, they may not reply candidly to all questions (Singleton & Straits, 1999).

The number and cross section of respondents in this study was assumed to adequately

represent the target population. Surveys were distributed across the hospital system to

nurses and hospital managers and administrative personnel; however, data collection was

reliant upon voluntary participation. Therefore, the risk was present of data inaccurately

reflecting the diversity of hospital personnel.

Scope, Limitations, and Delimitations

Data were collected in the current study through a survey to evaluate if perceived

barriers to quality improvement impact the reduction of medical error. A sample of 162

clinical and administrative personnel employed within a central-Florida hospital provided

a representative sample of this population within a hospital setting. The data collected in

the research provided a clearer understanding of how the perceptions of hospital

personnel related to quality-improvement barriers affect the reduction or elimination of

medical error. Many confounding variables associated to hospital management systems

exist that are related to medical error including hospital size, resources, and leadership.

These variables could have affected the data collection and/or findings of this current

study. This potential limitation and possible lack of honesty with the survey responses

could impact both the data and generalization of the findings.

This investigation involved variables describing quality-improvement practice

within hospital settings or other professional medical environments. Survey distribution

included personnel across the hospital setting. A letter of introduction instructed

participants to answer all questions and to answer them honestly to reduce response bias.

According to Creswell and Clark (2007), ―Wave analysis is a procedure that monitors

response bias. Surveys are checked at regular intervals to see if responses are consistent

during the survey collection process‖ (p. 411). Although surveys tend to produce weak

validity and strong reliability, such research presents all subjects with a standardized

stimulus and facilitates the elimination of unreliability in the observations of the

respective study. A small sample size and low response rate can be problematic (Sivo,

Saunders, Chang, & Jiang, 2006); hence, a response rate of 60% was targeted in the

current research. Literature on the use of surveys for data collection has indicated that an

acceptable return rate is between 50% and 60% (Kaplowitz, Hadlock, & Levine, 2004;

Sills & Song, 2002).

Delimitations imply deliberately imposed limitations on a research design

(Russell, 2004). A delimitation in the current study involved other hospital personnel

possibly differing significantly from the characteristics of the hospital personnel within

the research sample. The focus of the study was to determine how various personnel

perceptions of barriers to the implementation of quality improvement impact the

reduction of medical error. Differences between medical-error management systems

within the study-site hospital, or differences that may exist between adverse-event

reporting systems, are beyond the scope of the research. Finally, leadership and

organizational structures could impact the implementation and effectiveness of quality-

improvement strategies; however, these variables were also excluded from the research.

Summary

Patient care within hospitals has been affected by the high rate of medical error

adversely impacting patient safety (Kohn et al., 2000). Accredited hospitals have

implemented peer-reviewed medical-error management systems to comply with federal

and state standards. Understanding why efforts have not resulted in a higher reduction of

medical error is necessary to reduce the potential risk of harm to patients including death.

Quality-improvement systems and controls may not be the most effective because of the

perception of direct-care personnel and hospital management and administration.

Collecting the perceptions of various hospital personnel was expected to assist in

identifying causal factors for the lack of progress in quality improvement. Hospital

leadership will benefit from the findings of this study and the data related to personal

beliefs associated with implementation of quality-improvement processes potentially

leading to the reduction of medical error. The results will contribute to improving the

manner in which hospital personnel respond to external and internal pressures connected

to the implementation of change and the effectiveness of those efforts. An extensive

literature review was conducted to guide this quantitative study.

Chapter 2: Literature Review

The United States is renowned for one of the most innovative health-care systems

in the world. However, with the increased focus on quality improvement and medical

error, this status is declining (Kohn et al., 2000). National programs and supporting

federal agencies are in place, poised to develop quality measurement standards for

hospitals toward minimizing medical error (Harrington, 2005). Despite efforts toward

quality metric standardization and acknowledgement of medical error within hospitals,

the cost associated with these errors remains a major challenge for hospitals (Mello,

Studdert, Thomas, Yoon, & Brennan, 2007).

An analysis of literature pertaining to medical error, quality improvement, and

barriers to the reduction of medical error reveals the need to examine the perceptions of

medical personnel, as they relate to causal factors for medical error, toward the

development of more effective quality-improvement systems. A historical overview lays

the foundation for an examination of how medical error impacts the health-care industry

financially and structurally. A culture of health-care safety can motivate employee

commitment toward quality improvement and set a tone toward success with the

reduction of medical error. Through an investigation of perspectives on medical error,

the impact of health-care leadership, and the outcomes of medical-error strategies and

initiatives toward its reduction and improved regulatory oversight, are likely to be more

consistent and effective.

Historical Overview

The IOM (1999) published a report that served as a catalyst to raising public

concern and focusing attention on the problem of medical error and potential solutions.

The report estimated that medical error has caused up to 98,000 deaths per year within

the United States. A key recommendation was to reduce such error by 50% over the

following 5 years. During 2003, the JCAHO issued new patient-safety standards

(Deavers et al., 2004). As a result, 24 states enacted mandatory medical-error reporting

(Weinberg et al., 2005). Public awareness of medical error within hospitals increased due

to the publication of related studies and increased availability of hospital-quality report

cards (Pawlson, 2002). Despite these initiatives, investigation into the progress made by

hospitals in reducing medical error during the 5 years following the IOM (2006) report

has been inconclusive due to a lack of consistent methods of measurement and evaluation

(Berntsen, 2004; Young, 2005).

Prior to the 1999 IOM publication, health-care organizations engaged in

investigations of events that caused harm to patients; however, this was a systems-based

approach to the problem. The focus was on individuals and mistakes, rather than on the

events that combined to cause the incidents. Based upon a ―name and blame‖ culture, the

emphasis of such investigations was on punishment rather than prevention (Department

of Veteran Affairs, 2009). The tendency to ―play the blame game‖ is an unfortunate

aspect of human nature (Simpson, 2002). The increasing number of malpractice suits

filed each year reinforces the practice of assigning fault, which is automatically paired

with the search for monetary retribution. However, studies have indicated that only a

fraction of medical errors are caused by individual actions, and over one third of the cases

studied were unable to assign blame to a single individual (Krizek, 2000).

Blame is often interpreted as punishment and remains a major concern of medical

professionals. Wolf and Serembus (2004) surveyed 400 medical professionals using

open- and closed-ended questions related to actions following medical error. Clinicians

who made mistakes and reported them in good faith typically endured humiliation and

reprisal. Fear of administrative response was a common theme in examinations seeking

causal factors for unreported medical error. An assumption remains that errors are

actions with intention that failed to achieve desired results. Paget (1998) identified

medical error linked to the intentions of medical personnel to establish a culture of blame

and provide an opportunity for others to criticize those who made the mistakes.

Waring (2005) conducted a qualitative case study with particular focus on factors

inhibiting medical-error reporting. Interviews were conducted with 42 medical and

management staff within a medium-sized hospital located within the English midlands.

The interviews gathered information related to changes in the management of safety,

incident reporting systems, attitudes and practice regarding incident reporting, and issues

surrounding the management of medical performance. The transcribed interview data

were analyzed through a qualitative data-analysis computer package, which identified

several findings; one was related to both the fear of blame and the fear of reporting.

All of the physicians interviewed in the Waring (2005) study made reference to

the ―blame thing‖ or ―blame culture‖ when discussing apprehension surrounding incident

reporting. Blame equated to poor performance and the potential for punishment. The

most common source of blame was associated with the public and the press.

Contributing to this external source of blame was increased litigation and questioned

professional competence, leading to poor references and tarnished reputations.

Health-Care Culture

Recent changes in health-care approaches to patient safety have dissipated the

culture of blame because medical error is primarily attributed to systems rather than

individuals (Collins et al., 2009). Observation supported by interview data collected

from 163 physicians in a study conducted by Collins et al. (2009) revealed three forms of

blame—self-blame, blame targeting impersonal forces or the system, and blame targeting

other individuals. Physicians were primarily blamed for perceived error and bad

outcomes spurring scrutiny of their actions and their inability to foresee impending

problems. Physician self-blame extended beyond minor errors to those causing patient

deaths.

Second to physician self-blame was placing the responsibility of error on forces

such as lack of time, difficult diagnoses, and the transfer of care, indicating passive

acceptance of the manner in which the health-care system operates. Data have also

revealed physicians assigning blame to colleagues as a last resort, which often resulted in

questioning this logic. Adjusting to a blame-free culture is challenging due to the

prevalence of physician self-blame (Engel, Rosenthal, & Sutcliffe, 2006). The shift

requires a systems perspective (Collins et al., 2009).

A prevailing blame culture points to this environment as a major source of

medical error (Khatri, Brown, & Hicks, 2009). The movement to improve quality of care

is imperative as informed patients are actively engaging in health-care choices. However,

health-care organizations are finding it difficult to move from a culture of blame to a

more just culture without establishing a balance between control-based and commitment-

based management. Medical error has always been present and is likely to continue

because it involves human behavior.

Smith (2005) suggested that human behavior contributes to medical error because

of heuristics, fixation, pattern thinking, and overconfidence. Heuristics enables

individuals to judge situations based upon first impression rather than logical

examination of all involved factors. Under stress, the focus of attention may lead to

creative solutions that can miss obvious factors. Fixation error is the persistent failure to

revise diagnoses, even in the face of available evidence. Pattern thinking results in

individuals seeing what they expect rather than what exists, increasing the number of

misdiagnoses. Overconfident behavior in medical personnel can lead to believing

personal knowledge is greater than is demonstrated, adversely affecting patient

perspectives of care quality.

Health-Care Leadership

Many studies on hospital quality of care have focused on the patient perspective

(Gonzalez-Valetin & Padin-Lopez, 2005; Juwaheer & Kassean, 2006; Pakdil &

Harwood, 2005; Wann-Yih, Shih-Wen, & Hsin-Ping, 2004). This patient-centered

approach operates from the assumption that leaders are unaware of the quality

perceptions of patients, resulting in low service performance (Caruana & Pitt, 1997; Frost

& Kumar, 2001; Parasuraman, Zeithaml, & Berry, 1988; Rohini & Mahadevappa, 2006;

Wann-Yih et al., 2004). Fewer health-care studies have discussed the perceptions of

executive leaders, as they pertain to quality care (Frost & Kumar, 2001; Ovretveit, 2005).

Ovretveit (2005) indicated that further study is necessary to examine the

theoretical gap between the provider perspective of care quality and that of executive

leaders who influence the quality-management process. Research on medical error has

identified that, in addition to poor resource development for improvement systems,

organizational culture and personnel behavior contribute to the progress of quality

improvement (Jiang, Lockee, Bass, & Fraser, 2008; Ovretveit, 2005). The perceptions of

hospital personnel, as they pertain to barriers, impact the implementation of quality-

improvement measures targeting the reduction of medical error (Jiang et al., 2008).

Studies have also shown the importance of hospital leadership to the success of quality

initiatives (Jiang et al., 2008; Meyers, 2004; Sandrick, 2005).

The improvement of health-care service begins with recognition of the need for

effective, empowering leaders at all organizational levels (Janes & Mullan, 2007). The

success of hospital leadership affects quality improvement and the reduction of medical

error. These leaders understand the practice of care, but may lack the confidence,

knowledge, and skill to take action on needed improvements. Health-care leaders

maintain that medical health care is unique, and solutions for quality improvement must

be sought from the inside out (Krause & Hidley, 2008).

Quality-improvement leadership requires the implementation of reliable systems

that support the organization as a whole and serve as a collaborative tool across the

culture (Krause & Hidley, 2008). Leadership drives the organizational shift to a culture

of safety. Krause and Hidley (2008) suggested the following components to sustain such

a culture: (a) alignment among leadership across the health-care system with the

objective of patient safety to create an effective coalition, and (b) analysis of system

performance versus individual performance for an inclusive culture. Medical error

typically results from complex processes across the organization. To sustain improved

patient safety, all personnel must be included in communication via organizational

leadership.

The IOM (1999) described the aims and components of an ideal health-care

system, but did not provide a template for the organizational leadership needed for its

achievement. Medical staff, nursing staff, ancillary services, and executive management

all play a role in the organizational leadership that sustains patient care. Successful

programs characterize improvement teams and leadership support (Cowen et al., 2008).

Health-care systems must determine how to deploy and manage patient-focused teams

and maintain a connection with resources and the priorities of organizational leadership.

In response to the need for leadership competencies, the National Center for

Healthcare Leadership developed an empirically derived model focused on leadership.

This common model provides the framework to guide health-care leadership and the

improved performance of individuals and organizations (Calhoun et al., 2008). The

health-leadership competency model includes three domains—transformation, execution,

and people. Competencies reflect individual performance and provide indications for

additional development opportunities. Organizational leadership shapes a safety culture,

contributing to a reduction in medical error.

Outcomes of Medical Error

According to the Agency for Healthcare Research and Quality (AHRQ; 2005),

thousands of patients die each year as a result of medical error. Common mistakes

include minor infractions such as medication error resulting in minimal to no harm to

patients. However, other errors result in serious harm including death (McFadden et al.,

2006). Such events are unexpected and, if not death, involve serious physical or

psychological injury (JCAHO, 2005). Of all medical errors reported to the JCAHO,

13.4% were surgeries performed on inaccurate patient sites; 10.8% caused postoperation

complications. Those medical errors resulting in patient suicide totaled 11.9%. With the

exception of the suicides, the reporting of these events has been 64.6% self-report and

15% via patient complaint.

Researchers have suggested that conflicting and dysfunctional improvement

programs complicate the delivery of quality within American health-care environments

(McFadden et al., 2006; Sahney, 2003). The IOM (1999) confirmed that the cost of

medical error within the United States was $29 billion. However, additional psychosocial

variables include loss of morale among providers, diminished patient satisfaction,

increased patient discomfort, and loss of trust within the health-care system. These

factors take their toll in the form of decreased worker productivity, reduced school

attendance by children, and lower levels of population health status (Wexler, 2007).

The IOM (1999) reported that between 44,000 and 98,000 patients are injured on

an annual basis (Kohn et al., 2000). Crane and Crane (2006) similarly concluded that

medical error is widespread within contemporary hospitals. Health Grades Inc. (2005)

reported that 195,000 Americans die each year due to preventable errors, ranking hospital

error between the fifth and eighth leading cause of death. As a result, increased pressure

is placed on hospital leaders to focus on quality-improvement programs and provide a

safe environment for all stakeholders (Bradley et al., 2003; Crane & Crane, 2006; Kohn

et al., 2000). McFadden et al. (2006) described the critical nature of improved

understanding of the linkage between quality-improvement processes and human lives

when addressing the issue of medical error.

The magnitude of injury and death resulting from medical error was researched in

an extensive Harvard medical-practice study that reviewed the patient records of 51

hospitals across the state of New York (Brennan et al., 1991). Using weighted totals, the

researchers estimated that, among 2,671,863 patients discharged from New York

hospitals during 1984, 98,609 experienced adverse events (3.6%) and 27,179 (1%)

involved negligence. Adverse events of any kind occurred in 4% to 14% of all

admissions; 50% to 70% were due to preventable error (Wu, Huang, Stokes, &

Pronovost, 2009). The IOM investigated the problem of medical error to develop

improvement strategies (Kohn et al., 2000). The researchers compiled a comprehensive

literature review of related studies published within the decade following the Harvard

Medical Practices Study (Brennan et al., 1991; Kohn et al., 2000). A subsequent IOM

(2006) report concluded that medical error remains a pervasive and widespread problem

and additional research was indicated.

Health Grades Inc. (2005) conducted a study focused on hospital patient safety,

sampling the records of nearly 40 million Medicare patients to assess the mortality and

economic impact of medical error and related injury. Records corresponding to

nationwide hospital admissions from 2002 through 2004 were analyzed. The findings

indicated that 250,246 individuals within the United States died as a result of potentially

preventable, in-hospital medical error during the study period. Additionally, the

incidence of errors from a lack of patient safety increased from 1.18 million to 1.24

million.

The IOM (2006) reported that 1.5 million preventable, adverse events related to

medication error occur within the United States each year, costing as much as $3.5 billion

annually. The report noted that, of the five steps involved in medication administration—

procuring, prescribing, dispensing, administering, and monitoring a drug—errors occur

most often during the prescribing and administration phases. Medication-related error

occurs frequently within hospitals. Although not all medical errors result in actual harm,

those that do, can be costly. A single adverse drug event adds, on average, more than

$2,000 in hospitalization costs. This translates to $2 billion per year, nationwide, in

hospital costs alone (Birkmeyer & Diminck, 2004).

Perceptions

Patients and hospital personnel. A search for existing literature related to

quality-improvement systems, medical error, and barriers to progress was conducted for

the current study using the following databases: EBSCO host, Medline, Gale

PowerSearch, and ProQuest. The search revealed multiple studies on the perceptions of

patients and hospital personnel regarding barriers to quality improvement, as well as the

impact of these barriers on the implementation of improvement measures to reduce

avoidable medical error. Mazor, Goff, Dodd, and Alper (2008) conducted a qualitative

study to understand patient perceptions of medical error and their perceptions

surrounding how providers respond to such error. The manner in which provider

response or nonresponse influences patient reaction was also examined. During the 17

interviews of the Mazor et al. study, 23 incidents of medical error were identified through

data collected from patients or their families. Provider responses to the error varied from

not meeting family needs to neglecting the establishment of a trust of care. Mazor et al.

concluded that lack of disclosure will not ensure against patients or their families learning

of error. In fact, this will often lead patients to suspect error where none exists.

Dean, Farooqi, and McKinley (2004) described the perceptions and attitudes of

primary health-care team members with regard to quality-improvement initiatives to

identify any potential obstacles. The researchers found two major challenges in

implementing quality improvement. First, a perceived gap exists between the potential of

health-care workers and what they can actually achieve. Effective implementation of

quality-improvement systems is dependent upon organizational staff. Effective

teamwork can be accomplished when each participant engages in the process and the

process engages each participant.

Systems aimed at reducing medical error are most beneficial when the process not

only meets the needs of the organization, but also meets the skill level of the frontline

workers. The second challenge to implementing quality improvement is the need to

promote team understanding and involvement. Utilization of quality-improvement

initiatives toward reduced medical error must uniformly address each employee, fully

employing the skills each worker has developed. The role of each employee must be

understood and each must be actively engaged in the change process.

Griffin and Neal (2000) combined theories of individual performance with those

of organizational climate to develop a framework for investigating perceptions of safety

within organizations. The framework provides a link between perceptions of the work

environment and individual behavior within that environment. The measurement of

employee perceptions and measurable indicators of improvement within the work

environment is important to understanding the impact perceptions have on workplace

outcomes. Understanding the framework for identifying the type of quality-improvement

interventions that will best meet the needs of the organization provides an environment

most conducive to reducing medical error through engaged employee participation.

Exploration of the perceptions of health-care workers with regard to quality

improvement can facilitate the implementation of effective quality-improvement

processes. Clinical personnel play a pivotal role in the success of such initiatives.

Understanding and participating in the quality-improvement process increases ownership

and success (Ashley, 2000; Bolton & Goodenough, 2003; Koch & Fairly, 1993; Packer,

1998). A management gap exists between theory and practice when it comes to the role

of management. This, in turn, leads to ineffective processes (Williams, Pladevall, &

Fendrick, 2003). Further, policies and required data collection can lead to a failure in

personnel participation in quality improvement.

Price, Fitzgerald, and Kinsman (2007) explored the perceptions of nurse managers

and clinical nurses with regard to quality improvement within their respective practices.

A descriptive, qualitative research method was employed by collecting data through

semistructured interviews and constant comparative analysis. The findings confirmed

variant understanding of quality improvement by clinical personnel including how it

applies to practice. Participating nurse managers and clinical nurses blamed each other

for not recognizing the potential benefits. Integral to any quality-improvement process is

the need to generate participation and commitment from each employee engaged in the

initiative.

Medical professionals. Historically, considerable reluctance has been evident

among medical professionals to openly discussing medical error for fear of legal recourse

or a compromised reputation. Deterrents to such discussion include the threat of

malpractice suits, high expectations of patient families and/or society, disciplinary action

by licensing boards, and threats to job security. However, experts believe that individual

providers are not the underlying cause of medical error. The high level of stress

associated with delivering medical care, the similarities in spelling and pronunciation of

many drug names, the nationwide shortage of health-care workers, and the lagging

attention to safety within the health-care industry contribute to the nondisclosure of

medical error.

Increased awareness of medical error has increased interest in how patients,

families, and providers respond to such error (Mazor et al., 2008). It is therefore

important to study how perceived conclusions are generalized with patients experiencing

medical error. Past studies of these patients included only those who considered taking

legal action or who participated in a formal disclosure program. Mazor et al. (2008)

interviewed a sample of patients and family members to describe events they perceived

as medical error and the subsequent consequences. During the 17 interviews, 23

incidents of perceived medical error were recounted. A variety of factors led the patients

and family members to conclude that an error had occurred. These included the health-

care provider who administered care informing the patient or family of an error, or the

health-care provider informing the patient or family that the outcome of care resulted in

an error.

Physician perspectives on quality care and medical error within the health-care

setting are both negative and positive. Manwell, Williams, Babbott, Rabatin, and Linzer

(2009) conducted a study with a sample of 32 family physicians and internists located in

the upper midwest region of the United States and New York City. The study was

conducted to determine how medical error can be minimized while maximizing medical

outcomes. Areas targeted for improvement toward reduced medical error included

teamwork, aligned leadership values, diversity, collegiality, and respect among workers.

Factors identified by the physicians as adversely affecting quality care were related to

practice management and an inability to participate in decision making. Inadequate

resources and time, as well as a lack of necessary equipment, were additional factors.

Despite strong motivation to minimize medical error, several barriers to the

implementation of positive patient-safety practice are evident within existing literature.

Kalisch and Aebersold (2006) suggested that lack of leadership support is a significant

barrier to eliminating adverse care events. If hospital employees perceive that the

reduction of medical error is not a priority of the executive leadership of their

organizations, the staff will tend to adopt a similar viewpoint, regardless of their personal

perspectives. Fear of blame or punishment is a significant barrier to reform. Kalisch and

Aebersold reported that many nurses believe retribution is an inherent aspect of a plan

targeting the reduction of medical error. Administrators operate on the assumption that

fear of punishment motivates hospital staff to act more responsibly. As indicated earlier,

the opposite is typically true. When a medical error occurs, fearful employees may

intentionally neglect to report the incident (Crane & Crane, 2006; Gawande, Studdert,

Orav, Brennan, & Zinner, 2003; Kalisch & Aebersold, 2006).

Improvement Initiatives

Existing literature has suggested that quality-improvement initiatives have failed

to engage health-care workers (Davies, Powell, & Rushmer, 2007), especially physicians.

This has been attributed to the lack of time and resources necessary to enable physicians

to participate in improvement efforts. However, Davies et al. (2007) concluded that

causal factors go deeper than these issues to involve differing definitions of quality care,

conflicting views on responsibility, concern over the impact of medical error, and the

belief that a high quality of care is already being delivered. The perceptions of health-

care workers also indicate that quality initiatives are ineffective, a waste of resources, and

hold the potential to adversely impact patient care (Reason & Hobbs, 2003). Engaging

health-care workers in quality-improvement measures to reduce medical error will

require addressing these perceptions and utilizing skills and experience to harness

appropriate processes toward positive change.

Hospital leaders have an incentive to reduce or eliminate medical error due to

financial risk from lawsuits, the potential for increased regulation, and loss of customers

from greater public awareness (Savey, 2003). Important barriers exist within hospitals,

impeding quality-improvement efforts. Scott (2003) found that highly skilled workers

employed in various hospital positions are resistant to formal organizational structure and

controls. Organizational resistance can be a barrier to accepting additional quality-

improvement controls. Gaining related knowledge was the cornerstone of the current

research study. Understanding how hospital organizations have responded to change

initiatives targeting improved quality will benefit future study of organizational and

leadership theory and models.

The development and implementation of initiatives implemented to improve

quality rely upon successful execution (McAlearney, 2008; Singla, Kitch, Weissman, &

Campbell, 2006; Wachter & Pronovost, 2006). Health-care leaders overwhelmed with

the daily demands of addressing clinical, managerial, and community issues may not

sufficiently prepare for leadership (Hoffmann & Perry, 2005; Ramanujam & Rousseau,

2004; Russell & Greenspan, 2005). McAlearney (2008) suggested using leadership-

development programs to improve quality and efficiency within realms of health care

impacting medical error. He conducted qualitative research employing standard,

semistructured interviews with more than 200 experts filling health-care leadership

positions. The objective of the McAlearney study was to investigate the perspectives of

various health-care leaders on quality care, patient safety, and their organizational impact.

The identified perceptions provided opportunities to improve quality and efficiency

within health-care environments. These opportunities included increasing the caliber and

care quality of the workforce, improving education, reducing turnover, and focusing on

specific strategic priorities related to quality and efficacy.

As consumers, patients have a high degree of interest in hospital quality (Sofaer,

Crofton, Goldstein, Hoy, & Crabb, 2005). The Centers for Medicare and Medicaid

Services (2005) engaged in several initiatives to publicly report the results of measures of

care-provider performance to help consumers make more informed decisions and hold

hospitals accountable. During the mid 1990s, the AHRQ (2005) developed a survey

entitled Consumer Assessments of Healthcare Providers and Systems. The instrument is

focused on a national effort to measure, report, and improve the quality of health care

based upon the perspectives of patients and care providers.

Sofaer et al. (2005) conducted a qualitative study using 16 patient focus groups

across Baltimore, Los Angeles, Phoenix, and Orlando. These markets offered the best

opportunity to recruit a diversity of participants due to the wide range of regional hospital

facilities and available health-care coverage options. The participants indicated which

safety domains of the Consumer Assessments of Healthcare Providers and Systems were

the most important to them, which did not impact their choice of hospitals, and those

domains they perceived as unimportant. The results of the study indicated that both

consumers and patients have a high degree of interest in hospital quality and considered a

high proportion of safety-domain items on the survey as sufficiently important to force

them to change hospitals. The important areas highlighted by the patients were physician

communication, nurse and hospital-staff communication, staff responsiveness to patient

needs, and the manner in which problems were avoided with medications or care

following hospital discharge.

Reduction strategies. Although past literature has suggested various approaches

to the reduction of medical error, research focused on one common approach seems to

present a panacea to such reduction—standardization (Hoffman & Mark, 2006).

Processes, tools, technology, and equipment are standardized because variation increases

complexity and the risk of error (Griffin & Haraden, 2005; Hosford, 2008; Ransom, Kini,

Jones, & Ransom, 2005; Williams, Schmalt, Morton, Koss, & Loeb, 2005). Kohn et al.

(2000) opined that many errors are prevented by designing standardized processes that

make it difficult for incorrect actions to manifest. Leape (2002) indicated that research

has been inconsistent in determining the extent of processes, tools, and technology

methods that reduces medical error. Naveh, Katz-Navron, and Stern (2005) disagreed

with the view of traditional methods of developing and implementing safety procedures

providing definitive results.

Kohn et al. (2000) defined a medical error as ―the failure of a planned action to be

completed as intended or the use of a wrong plan to achieve an aim‖ (p. 28). McFadden

et al. (2006) explored current strategies for reducing medical error within hospitals. Past

research approaches to medical error are limited to a small subset of systems-oriented

solutions (Bright & Cooper, 1993). Studies have indicated that hospitals are making

progress toward implementing improvement measures to successfully reduce medical

error (Clarke, Krause, & Hidley, 2006). However, findings have also identified an

ongoing gap between hospital practice and the perceived importance of such measures.

McFadden et al. sampled 21 medical units within a general hospital and cross-validated

the data collected to 15 units in another hospital. The findings demonstrated that

perceived safety procedures and clear communication flow reduce medical error only

when managers practice safety to demonstrate its priority within the respective hospital

unit.

Health-care providers are motivated to report medical error. Clarke et al. (2006)

opined that this motivation is developed from professionalism, regular feedback,

addressing system problems to avoid work-arounds, and developing a nonpunitive

workplace culture. The development of a culture of safety must make reporting easy,

communicate the benefits of reporting, ensure confidentiality, and commit to changing

the system rather than the individual. Crane and Crane (2006) proposed that the use of

failure mode affects analysis as a solution to medical error. This process identifies

potential failures, identifies actions that could eliminate the failures, and actively

documents the process for review and training.

Mandatory or nonmandatory medical-error reporting continues to challenge

organizations attempting to obtain a realistic measure of organizational performance.

Mehta and Gogtay (2005) indicated that health-care organizations are responsible for

acknowledging medical error and expressing concern to the involved staff, physicians,

patients, families, and community. The effectiveness of mandatory error reporting is

measureable. Bhattacharya and Catherine (2004) conducted a survey study with

physicians and hospital administrators that revealed the reporting of medical error as

causal to improvements in health care. Weissman et al. (2005) concluded that more than

two thirds of hospital executives are opposed to mandatory medical-error reporting when

the information is made public. This indicates that public disclosure of medical-error

information discourages the internal reporting of such error.

Existing literature on improvement strategies and their implementation has

revealed that little is known surrounding the design of effective quality-improvement

interventions (Bosch, van der Weijden, Wensing, & Grol, 2006). Rather than analyzing

perspective barriers to the development and content of effective interventions, most

literature has analyzed solely obstacles to their implementation. Bosch et al. (2006)

conducted a qualitative analysis of a sample of 20 quality-improvement studies reporting

barriers to both educational and organizational quality interventions. Their findings

indicated that the design of quality-improvement interventions remains within an infancy

stage due to a continued mismatch between the level of identified barriers and the type of

interventions selected for use.

Quality-improvement initiatives effectively implemented within health-care

organizations perform as operational and strategical responses to system challenges

(Alavi & Yasin, 2008). Alavi and Yasin (2008) conducted a qualitative study to

determine if the utilization of quality-improvement initiatives affect the operational

environment of health-care facilities. Instrumentation consisted of four open-ended

questions and 80 items focused on environmental-change factors, response factors, and

the effectiveness of quality-improvement initiatives with a Likert-type response scale.

The population sample consisted of 39 health-care organizations—hospitals, outpatient

clinics, laboratories, and pharmaceutical firms. The findings indicated that the

participating health-care organizations were aware of the challenges associated with

quality improvement and medical error and were actively engaged in safety initiatives to

address those challenges. However, the effectiveness of safety initiatives depends largely

upon their interpretation by health-care personnel.

Regulatory oversight. Numerous initiatives implemented to improve the quality

of health care aim to establish standards for the management of medical error within

hospitals. During 1996, the JCAHO created a sentinel event policy for the management

of such error (Schyve, 2000). The federal government established the AHRQ in 1999 as

an effort to coordinate federal quality-improvement efforts. National patient-safety goals

introduced during 2003 by the JCAHO focused on health-care improvement efforts

targeting a set of high-priority problem areas (Hyman, 2006). Accreditation standards

developed by the JCAHO were providing hospitals with latitude on the manner in which

they complied with the standards.

During the nine years between publication of the Harvard Medical Practices

Study and the 2001 IOM report, various initiatives toward improving the quality of health

care within hospitals were implemented. Legislation was enacted in 20 states for

voluntary and mandatory reporting of medical error. In 1996, the JCAHO instituted the

sentinel event policy that established standards for the management of medical error

within accredited hospitals and the federal government established the AHRQ. Despite

these initiatives and increased public awareness, the 2000 IOM identified ―few tangible

actions to improve patient safety‖ (as cited in Kohn et al., 2000, p. 5).

On July 29, 2005, the Patient Safety and Quality Improvement Act of 2005 was

enacted to improve patient safety by encouraging voluntary and confidential reporting of

medical-error events adversely affecting patients. The Act signified the commitment of

the federal government to fostering a culture of safety (AHRQ, 2005). It established a

national voluntary reporting system for medical error and public disclosure of the

reported information was prohibited (Kinnaman, 2007).

The Patient Safety and Quality Improvement Act of 2005 achieved one of the

IOM goals of establishing an environment that encourages voluntary reporting while

protecting information from public disclosure. Private entities known as patient-safety

organizations were created to collect confidential information related to medical error,

analyze it, and provide recommendations toward improved patient safety, which began to

transform the reporting system. The Patient Safety and Quality Improvement Act served

to improve patient safety through confidentiality and established reporting standards that

eliminated patient and provider identity. The Act embraced a culture of nonpunitive

support (Mewshaw, White, & Walrath, 2006).

In December 2005, the American Medical Association (2005) developed

approximately 140 performance measures covering 34 clinical areas. An agreement with

Congress called for physicians to voluntarily report their performance on these measures

as part of a national quality-improvement program. The measures were collectively

considered best practice and consisted of diagnostic tests and treatments that

demonstrated the ability to improve clinical outcomes. The American Medical

Association also developed measures to assess physician performance and compensation.

Local mandates. In addition to federal legislation mandating the measurement

and monitoring of medical error, individual states implemented additional oversight

measures. In 2000, the Florida legislature appointed the Florida Commission on

Excellence in Healthcare, which focuses on issues of quality health care, patient safety,

and the reduction of medical error. In September 2003, the Medical Incidents Law went

into effect within the state of Florida, which impacted the responsibilities of licensed

health-care providers. Some of these requirements include recognition of error-prone

situations, process improvement for patient outcomes, reporting responsibilities, and

public education.

Patient-safety organizations were deployed in compliance with the Patient Safety

and Quality Improvement Act of 2005. They worked with clinicians and health-care

organizations to identify, analyze, and reduce the risks and hazards associated with

patient care. Florida was one of the first states to recognize the link between medical

malpractice, medical error, and patient safety. Primarily in response to the 1999 IOM

report, the Florida legislature established the Florida Patient Safety Corporation (FPSC)

with the purpose of monitoring patient safety throughout the state. In 2005, the Florida

legislature provided funding for the FPSC to establish the voluntary Near Miss Reporting

System, based upon a successful system used within the commercial-aviation industry.

The objective of the program was to establish a statewide reporting system that was

timely, anonymous, standardized, and easy to use. An important aspect of the system

was the provision of immunity from legal penalties and sanctions (FPSC, 2008).

Unfortunately, in response to an ongoing budget crisis, funding for the FPSC was

discontinued in 2008 and indefinitely suspended.

In 2004, two state amendments were passed in Florida—the Patients’ Right-to-

Know About Adverse Medical Incidents Act of 2004, known as Amendment 7, and the

Three Strikes and You Are Out Act of 2004, known as Amendment 8 (as cited in Yaeger,

2009). Collectively, this legislation was aggressively promoted by Florida trial attorneys

and their efforts reversed many of the patient-safety gains of the FPSC mandates.

Amendment 7 eliminated confidentiality provisions and allowed full access to all patient

records including all meetings, morbidity and mortality conferences, root-cause analysis,

and any other professional exchanges of information related to patient injury or death.

Upon first analysis, this appears to be a positive change; however, according to risk-

management professionals, Amendment 7 has done immense harm to the quality

assurance and peer-review protections developed over 2 decades and caused an

immediate decline in the reporting of adverse events throughout the state (Barach &

Small, 2005).

The Three Strikes You Are Out Act of 2004 (as cited in Yaeger, 2009) presented

an unintended adverse effect on the reporting of near misses and adverse medical events.

It directed the Florida Board of Medicine to revoke medical licenses from providers with

three ―adjudicated malpractice incidents‖ (p. 126). A strike is considered ―any

malpractice judgment, findings from disciplinary cases, decisions of binding arbitration

finding malpractice, and malpractice judgments from any other state‖ (Barach & Small,

2005, p. 762). It is hoped that the new federal regulations from the patient-safety

organizations will help resolve the Florida situation. They went into effect on January

19, 2009 and described a clear, legally-protected framework for how hospitals, clinicians,

and health-care organizations can work together to improve patient safety and nationwide

quality of care.

Thirty-nine states, including Florida, have mandatory or voluntary systems in

place for reporting medical error. Florida requires that all licensed health-care facilities

establish an internal risk-management program that includes (a) investigation and

analysis of the frequency and causes of general categories and specific types of adverse

patient incidents, and (b) the development of appropriate measures to minimize the risk

of adverse patient incidents (Kaiser Family Foundation, 2008). Health-care facilities

within the state of Florida must electronically report data on hospital-acquired infections

to the Agency for Healthcare Administration, as specified in federal regulations. Health-

care facilities must also submit annual reports to the Department of Health on adverse

sentinel events (Rosenthal & Takach, 2008).

Disparities

Hospitals are complex organizations requiring diverse technology and specialized

skills in personnel. They must manage data, consumer demands, market fluctuations, and

changing medical information while assimilating these elements into quality patient care.

The fallibility of human nature renders quality management difficult (Griffin & Haraden,

2005; Hughes & Clancy, 2005). As a result, when a significant medical error occurs,

hospitals must cope with a variety of adverse consequences. They must effectively

execute key processes related to patient access, service delivery, and revenue realization

to optimize the relationships among quality, efficiency, and cost (Orlikoff & Totten,

2010).

The U.S. Bureau of Labor Statistics (2006) reported that health care is the largest

U.S. industry, providing 14 million jobs. Between 2006 and 2016, this industry is

predicted to generate 3 million new wage and salary positions (DeGeetern, 2009). The

delivery of quality health care is accomplished through offering personal services. The

connection between the health-care provider and patient requires development to improve

the current delivery model. The IOM (2001) identified the need for a redesigned health-

care delivery system to improve patient safety. Areas were identified that contributed to

quality problems such as an increase in chronic disease, a poorly organized delivery

system, and constraints in deploying information technology.

The IOM (2001) set forth six aims for improvement and 10 rules for a redesigned

health-care system. The ten rules included care based upon continuous healing of

relationships, the patient as the source of control, a free flow of shared knowledge and

information, evidence-based decision making, safety as a system priority, transparency,

anticipated needs, decreased waste, and cooperation among clinicians. Health Grades

Inc. (2005), an independent health-care quality research organization that grades hospitals

based upon a range of criteria and provides hospital ratings to health plans and other

payers, issued its third update to the 1999 IOM report. The report found that, despite

widespread participation in patient-safety initiatives to reduce the frequency of medical

error, progress toward improved safety was slow during the 6 years since the IOM report.

According to Brady, Ho, and Clancey (2008), ―Quality improvement is, by

definition, an endeavor, never completely fulfilled. Quality improvement is marked by

the constant effort to raise performance and produce results that are consistently better‖

(p. 396). Realistically, 100% improvement cannot be achieved; however, continuous

improvement from one measurement period to the next must be evident. The AHRQ

(2005) has been documenting steady improvement in the quality of American health care

since it began publishing reports on the quality of U.S. health care and disparities in

2003. Improvement has occurred; however, at a modest annual rate of 1.5%. The Brady

et al. analysis draws on more than three dozen data sources to measure quality and

disparity in five areas—the effectiveness of care, patient safety, the timeliness of care,

patient centeredness, and the efficiency of care.

Culture of Safety

Time is a barrier to patient safety. Staff reduction, complex procedures, and

heavy patient loads contribute to the frequency of medical error (Gawande et al., 2003;

Kalisch & Aebersold, 2006; McFadden et al., 2006). When staff perceives an insufficient

amount of time to conduct a root-cause analysis of medical incidents, the tendency is to

dismiss the error so the provider can return to patient care (Kalisch & Aebersold, 2006;

McFadden et al., 2006; Weeks & Bagian, 2000). Similarly, daily workload interruptions

are a barrier to reducing medical error. Nurses frequently multitask while attending to

critically ill patients, carrying cell phones or pagers for both hospital and personal calls.

Kalisch and Aebersold (2006) found that nurses reported 84 to 120 interruptions during a

single shift. Ineffective teamwork and lack of accountability often prevent the

development of quality-improvement programs (Gawande et al., 2003; Kalisch &

Aebersold, 2006). Kalisch and Aebersold reported that poor teamwork increases the

number of medical errors.

Promoting a culture of safety within health-care organizations is an important

strategy toward improving patient safety. A positive culture recognizes errors will occur

and seeks opportunities to implement preventative strategies (Edwards et al., 2008). A

safe culture must move from a punitive to a blame-free environment. Edwards et al.

(2008) used the AHRQ hospital survey on patient safety to measure the safety culture

within various units of a hospital. The survey facilitated identification of common

dimensions of organizational climate and the assessment of staff perceptions of safety.

The instrument consists of questions measuring the dimensions of a safety culture and

patient-safety outcomes. Edwards et al. distributed the survey to various hospital

personnel employed within two Atlanta hospitals. Their findings identified key areas of

concern regarding perceptions of the frequency of reporting error, manager expectations

and actions, and teamwork. Understanding personnel perceptions allowed the

development of safety initiatives such as safety rounds, education in event reporting, and

a nonpunitive response to error.

Public awareness of medical error has increased since the 1990s. Information

sources, such as report cards comparing health-care providers, provide consumers access

to patient-outcome information. Access to the Internet, public notification, and media

focus transformed the consumer into an informed participant. Studies analyzing

evidence-based quality ratings motivated hospital administrators to improve patient

safety to retain customers. Access to health-care reports appears to be a positive

motivator for consumers and industry leaders. Werner and Asch (2005) investigated the

negative impact of health-care reports on patient-care services. These researchers found

that physicians may avoid certain patients or overly rely upon interventions to improve

their ratings.

Despite claims within existing literature that most medical error is due to system

problems (Collins et al., 2009), Menachemi, Shewchuk, O’Connor, Berner, and Allison

(2005) concluded that physicians tend to underestimate medical error and generally favor

remedies. The researchers examined perceptions potentially influencing physician

behavior with respect to medical error. Participants from three large academic medical

centers and one residency program completed a survey on 40 issues related to medical

error using a visual-analog rating scale. The findings revealed 10 physician perceptions

that impact medical error. These ranged from inaccurate interpretations of results and

lack of competent staff to working when fatigued or stressed and deviant-related actions

such as negligence. This information can be used to improve care quality and patient-

safety education.

Employee perceptions of safety procedures are viewed as suitable when the

procedures are not disruptive to daily life. Naveh et al. (2005) posited that safety

procedures ensure effective safety performance when they are practical, applicable, and

congruent with the demands of the work. MacIntosh-Murray and Choo (2005) conducted

a study of the beliefs, values, and practices surrounding patient safety and agreed that

behavior and safety measures must work congruently to create a culture of safety.

Factors requiring the attention of leaders are (a) the possibly latent nature of the

information needs of individuals and the active engagement required, (b) the ability of

information change agents to provide critical feedback to individuals, (c) awareness of

routines becoming the norm and resulting in negative adaptation, and (d) a gap possibly

existing between the expectations and level of thought and practice among workers.

The quality-improvement process is an integral component of health-care delivery

(Price et al., 2007). Price et al. (2007) conducted a study to identify and explore the

perceptions of clinical nurses with regard to quality improvement within their practices

for purposes of reducing medical error. Quality-improvement intervention is aimed at

improving clinical practice and patient outcomes. Clinical nurses understand its role;

however, they do not integrate quality improvement into their practice because it is

viewed as supplemental to their overall role (Naveh et al., 2005). An emphasis on issues

relating to the management and implementation of quality improvement contributes to

the execution of related interventions. Organizational culture, management structure

encouraging involvement, and ownership can be effective factors to their success.

Recognition of the need to assess patient safety within health-care settings, as

well as the impact of effective patient-safety measures on reducing medical error, leads to

the development of tools facilitating measurement of the perceptions of hospital workers

regarding the safety culture (Singer et al., 2007; Weiner et al., 2006). Singer et al. (2007)

listed key topics pertinent to a safety culture and randomly distributed the list among

health-care personnel within 105 hospitals to collect data conducive to the development

of tools to measure perceptions of safety. The findings indicated that organizational and

individual perceptions of, and engagement in, a safety culture play a critical role in

adequately addressing medical error.

Reporting

The Quality Interagency Coordination Task Force (2000), established in 1998 in

accordance with a presidential directive, ensured that all federal agencies involved in

purchasing, providing, studying, or regulating health-care services were working in a

coordinated manner toward the common goal of increasing care quality. The task force

was to evaluate the related findings of the IOM (1999) and provide a strategy to address

medical error and patient safety (Wahls, Chatterjee, Ting, & Clancy, 2002). This group

agreed with the findings of the IOM and developed a federal response using the four-tier

IOM recommendations. They also agreed with the IOM recommendation to implement a

single reporting system with voluntary and mandatory components. The Quality

Interagency Coordination Task Force advanced that successful expansion of the reporting

system could only be accomplished as a nonpunitive system, which was supported by

federal and state legislation.

The impact of quality reporting on hospital operations can best be assessed

through program modification (Pham, Coughlan, & O’Malley, 2006). Programs for

reporting quality performance and medical error have surged over the past decade from

local to broader initiatives (Simpson, 2005). Pham et al. (2006) examined the interaction

between various systems and their impact on operations within hospitals from the

perspective of hospital executives and frontline staff. Semistructured interviews included

open-ended questions on specific reporting programs in which the hospitals participated.

The interviews solicited perceptions of program improvement based upon reported

priorities, budgeting, staffing levels, data collection, feedback, and accountability

mechanisms. The findings revealed that hospital executives assign quality improvement

a higher priority when it is linked to patient payment, JCAHO accreditation, and peer

pressure from benchmarking. Clinical respondents viewed the impetus to participate in

programs of the JCAHO and the Centers for Medicare and Medicaid Services as forced

because they are mandatory public disclosures.

Quality Improvement

Organizational safety is the relationship between organizational culture,

leadership, and safety outcomes (Krause & Hidley, 2008). Organizational insight sheds

light on patient and employee safety and moves health-care institutions into a new

direction toward the successful implementation of quality improvement. Organizational

change can be difficult within any industry. Specific habits, learned work-arounds, and

organizational cultures can contribute to the stagnation of quality-improvement programs.

Krause and Hidely (2008) recommended five ways of viewing patient and

employee safety. Creating alignment among hospital leadership is the leading driver of

change toward a culture of safety. Root-cause analysis of incidents leads to advanced

training and appropriate system changes. Safety must be the underlying theme of every

component of organizational analysis. Leadership shapes the culture of the institution,

and effective safety leadership manifests through leader action. The active-engagement

approach to medical-error reporting leads to patient safety (Macrae, 2008). Active

engagement by employees in the management of medical error validates the importance

of involving individuals closest to risks.

Error management aims to develop work environments attentive to reducing the

possibility of medical error while managing the impact on the health-care system (Bauer,

Mulder, & Mulder, 2007). Additional research into how such error can contribute to

learning and the competence and performance of teams is important. Dominating

quality-improvement initiatives is the focus on reducing medical error through process or

environmental changes rather than on the recognition of human perceptions and

dispositions. Bauer et al. (2007) conducted an exploratory interview study with 10

experts in nursing to examine two specific areas. Errors occurring within the health-care

arena were first collected for the assessment of personnel understanding and learning.

The study subsequently identified the effectiveness of strategies in place to address the

medical error. Bauer and Mulder concluded that nurses only associate learning with

medical error when it is formal learning. Participation in safety initiatives is influenced

by the workplace environment. Buetow (2005) argued that medical error is potentially

desirable to produce beneficial consequences for patients and improved patient-safety

standards.

Medical error is a significant problem (Clarke et al., 2006; Wachter, 2004).

Process-improvement programs, standardized information reporting, and the

implementation of mandatory reporting systems indicate that the need for quality

improvement is indeed recognized. Despite good intentions, few efforts have reduced the

frequency of medical error within U.S. hospitals (McFadden et al., 2006). Hospital

leadership has become isolated within organizational silos that discourage leader

collaboration (Stone, 2004). Litigation and bad publicity discourages hospital leaders

from disclosing information on medical error and impedes employee reporting efforts

(Gawande et al., 2003). Various medical-personnel perceptions of barriers to effective

quality-improvement programs have been evaluated.

Summary

The need to understand barriers to the implementation of quality-improvement

systems to reduce medical error is clear. Standards of care, patient outcomes, and

consumer awareness are driving factors to reduce such error. Hospitals are responsible to

perform at a higher level. Following the Harvard Medical Practices Study and the 2001

IOM report identifying the widespread problem of medical error, initiatives were

implemented to address patient safety. Federal and state legislation have strengthened

the need for quality improvement and measurement activities. Competition among

hospitals has added to the drive to improve patient outcomes. Mandatory and voluntary

reporting systems have been implemented and consumer awareness of patient safety has

increased (Pronovost, Berenholz, & Needham, 2007). Despite improvements in

addressing medical error, research remains needed to determine how barriers perceived

by various health-care employees impact their reduction.

Health-care leadership and patient perceptions of medical error have been

significantly studied; however, additional research is needed on the gaps between the

perceptions of hospital leaders regarding quality improvement and those of other hospital

personnel. Research since the 1990s has focused on the causes or consequences of, or

intervention for, medical error (Menachemi et al., 2005). Existing literature has

addressed how medical error occurs, the cost of such error, and the implementation of

improvement strategies. Understanding the perceptions of health-care providers with

regard to this issue will facilitate improved interventions and strategies acceptable to

health-care practitioners.

Chapter 3: Research Methods

The purpose of the current quantitative study was to explore the perceptions of

hospital personnel regarding quality-improvement initiatives as a collective barrier

potentially inhibiting the reduction of medical error within a hospital setting. This

method enabled effective examination of the variable of interest (Balnaves & Caputi,

2001) and allowed generalization from the specific study sample to the larger target

population (Creswell, 2003).

Research Design

Mixed-method, qualitative, and quantitative research designs were considered for

use in this study. A mixed-method design requires ―collecting both quantitative and

qualitative data in a single study, and analyzing and reporting the data based on a priority

and sequence of information‖ (Creswell, 2003, p. 506). Such research depends upon the

quantitative data to enhance or explain the qualitative findings. A mixed- method design

was not chosen for this study because qualitative data collection was unnecessary. The

research does not include interviews nor a focus on words and images from within the

collected data. The study focused on measuring variables and statistics drawn from the

data. Descriptive research designs are aimed at determining ―what is‖; consequently,

observational and survey methods are frequently used to collect descriptive data. Such

research is unique in terms of the number of variables employed. Analyzing correlations

between multiple variables may be employed by using tests such as a Pearson product-

moment correlation, regression, or multiple-regression analysis.

This current research was conducted to explore the perceptions of hospital

personnel with regard to quality-improvement initiatives as a collective barrier potentially

inhibiting the reduction of medical error. The population sample included nurses, nurse

managers, and hospital administrators. The findings can benefit hospital leaders working

toward successful implementation of quality-improvement strategies to reduce medical

error through clearer goals, increased management and support, and adequate planning

(Messner, 1998). Several factors influenced the selection of the descriptive survey design

of this study. The design allowed a sample representative of the target population

(Singleton & Straits, 2005). Each participant received the same instrument with no

variation. The sampling method was vital to generalization of the findings and ensured

against researcher bias hindering this goal.

Methodology Appropriateness

A quasi-experimental design was deemed inappropriate for this study because

experimental designs are used to test the impact of a treatment or intervention.

Researchers manipulate the independent variable to isolate whether or not the outcome

was influenced by the intervention and not other factors (Creswell, 2009). An

experimental design was inappropriate because the target population is already

established in their given fields of work. The independent variable for this study was the

perceptions of barriers to quality care improvement measured by a Likert-type scale

survey. The dependent variable was the statistical data of the sample characteristics.The

employee attitudes toward quality improvement were already established and could not

be manipulated. Factors affecting beliefs and knowledge could lead to a change in

attitude and influence behavior (Fishbein & Ajzen, 2009); however, this aspect is beyond

the scope of this current study.

A survey research design was appropriate for this research because data can be

collected via a random sample. In quantitative study, discoveries are measurable and the

data presented are from an objective rather than subjective viewpoint (Balnaves &

Caputi, 2001). Questions surrounding the relationships among measured variables can be

addressed using quantifiable data collected for purposes of explaining and predicting

phenomena (Creswell, 2003). Therefore, objectivity, deductiveness, generalization, and

statistics are features often associated with quantitative research (Gelo, Braakmann, &

Benetka, 2008). A Likert-type, self-administered survey was administered to the sampled

hospital personnel to facilitate a measure of the perceptions of barriers to quality

improvement and how the measured results vary across categories of employees.

Accomplishing the Study’s Goals

The descriptive design of this study allowed the opportunity to determine the

behavior of the subjects without affecting the sample in any way (Balnaves & Caputi,

2001; Polonsky & Waller, 2005). The data collected facilitated an examination of the

independent and dependent variables to determine whether a relationship exists. In

quantitative research, the aim is to determine the relationship between one factor (i.e., the

independent variable) and another factor (i.e., a dependent or outcome variable) within a

given population. The design selected for this study was appropriate because the

quantifiable data led to objective findings (Balnaves & Caputi, 2001). This is in contrast

to a longitudinal or cross-sectional design, which allows collection and analysis of data in

a manner that secures immediate results (Polonsky & Waller, 2005).

Use of a survey was feasible for this study due to the easily accessible population

sample and minimum cost (Creswell, 2003). Questionnaires in quantitative research

provide credibility (Miyata & Kai, 2009). Bias is unlikely with a high participation rate

in a sample selected randomly from a target population. The objective of a text-based

questionnaire is to capture reality; therefore, a strong need may exist to employ a

credibility criterion for evaluation. A simple survey design was the optimal choice for

accomplishing the goals of this research, as opposed to a longitudinal design (Polonsky &

Waller, 2005). Longitudinal study can extend from 1 to 20 years or longer.

Consequently, the longitudinal design was inappropriate for this current research.

Quantitative study met the goals of this research, given the hospital study site and

population sample. The frontline health-care providers who participated in the study

provide direct patient care within the study-site hospital, and the management and

administrative personnel completed the hierarchy involved in process improvement. The

data-collection approach yields several elements to the data without revealing unique

personal opinions surrounding why medical error has not been reduced. Validity,

reliability, and generalization can be used as criteria in quantitative analysis (Miyata &

Kai, 2009). In such approaches, psychological and social phenomena have an objective

reality. The relationships between the phenomena are investigated in terms of general

causal effects (Gelo et al., 2008).

All research methods present unique problems and limitations; consequently, an

overreliance on any one method, as opposed to applying multiple methods to investigate

a phenomenon of interest, can lead to limited resources (Bowling, 2002). As Johnson

and Onwuegbuzie (2004) maintained, purists must be challenged by those willing to

expose the philosophical weaknesses of a paradigm. For example, quantitative methods

and data are noted for their objectivity; however, objectivity does not completely drive

quantitative methods. Subjectivity intervenes when deciding, for example, what to study

and how to develop research guidelines based upon researcher perception of the

construct. These arguments describe the natural tendencies of a paradigm and can hence

lead to a determination of the optimal research method.

A quantitative survey facilitated the collection of data in this study toward

identifying the perceptions of employees without changing their environment or

behavior. Data were analyzed through descriptive statistics to determine the impact of

the employee perceptions on the effectiveness of quality-improvement programs. A

volunteer sample of 162 clinical and administrative personnel at a central-Florida hospital

represented the target population. Data analysis was supported by SAS software that

methodically organizes, codes, and categorizes identified data. The findings provide a

clearer understanding of how the perceptions of hospital personnel regarding barriers to

the implementation of quality-improvement initiatives contribute to the reduction or

elimination of medical error.

Informed consent and confidentiality

Permission to use the study site in this research was obtained from a vice

president of the hospital (see Appendix A). Research conducted within a hospital setting

must be conducted in a manner that protects the rights and welfare of all participants by

adhering to ethical and legal guidelines (Collaborative Institutional Training Initiative,

2006). All policies designed for the protection of human subjects, mandated by both the

University of Phoenix and the Institutional Review Board of the participating hospital,

were honored. No risk to the participants was anticipated; however, a degree of

emotional discomfort was possible due to the disclosure of personal feelings related to

specific work tasks. The survey also did not introduce vulnerability for the study

participants. No risk was involved in its completion, and the results are reported in

aggregate with no specific respondents identified.

An informed-consent form inclusive of an explanation surrounding the nature and

goals of the study was executed by all individuals within the study sample. The form

also identified the researcher and described how all data will be collected. The right of

all participants to withdrawal from the study at any time with no adverse consequences

was also clearly stated (see Appendix B). A cover letter was provided, identifying the

name and contact information of the principal investigator, the affiliated organization, the

purpose of the research, the number of individuals needed for the sample, the risks and

benefits, and assurance that all survey responses will be held strictly confidential and

reported only in an anonymous fashion (see Appendix C).

Only the researcher had access to identifying data, which was password protected

and downloaded into a personal database. As noted earlier, no anticipated risk existed

with survey completion. Individual responses were submitted in an anonymous fashion

with no identifying name or address placed on the instrument. Survey participation had

no effect on the employment status of the respondents. As also noted earlier, all

participants were provided with contact information for the researcher should any

questions, comments, or concerns arise with regard to the study.

Population and Sampling

The sample for the proposed study consisted of employees involved in

performance improvement within a central-Florida nonprofit hospital. According to the

most current information obtained from the human-resource database of the participating

hospital, this care facility employees 6,674 individuals—5,078 female and 1,596 male.

Further breakdown identified 2, 584 nurses—245 directly involved in nursing

management—and 81 leadership personnel. Full-time employees numbered 5,447 and

part-time employees totaled 543. Employment status is either full time or part time (i.e.,

4 to 6 days per week). A full-time employee works a minimum of 36 hours per week,

whereas a part-time employee works less than 36 hours per week.

Other study-group characteristics collected in this research included gender, age,

occupational status, and tenure status. Inclusion criteria include a six month tenure with

the participating hospital, which is a tertiary-care institution within Orlando, Florida.

Orlando is a major city within the central region of Florida and is located at the center of

the greater Orlando metropolitan region. This area has a population of more than 2

million, while the city-proper population is 230,519 (U.S. Census Bureau, 2008).

The primary intent in sampling is to select study participants who are

representative of the larger target population. Results can then be generalized to the

target population, which represents external validity. The sampling can simplify the

complexity of studying an entire population (Singleton & Straits, 2005). The data

collected can allow for generalization with random sampling. According to Bartlett,

Kotrlik, and Higgins (2001), sample size is important in detecting significant differences,

relationships, or interactions among variables. The participants of the current study were

selected through stratified systematic sampling to ensure that each individual had an

equal probability of inclusion in the research (Creswell, 2003).

As noted earlier, the population sample of this study consisted of registered

nurses; nurse managers; assistant nurse managers; and hospital administration (i.e.,

directors, vice presidents, and chief nursing officers) of a central-Florida hospital. The

demographics of gender, age, occupational classification, and tenure status were drawn

from those selected for participation. The sample consisted of males and females who

spoke English as their primary language. They were employed with the study-site

hospital and were employed a minimum of six months. The sample was drawn by

identifying qualified individuals who matched the identified criteria from an employee

database and then sorted by every third record.

Bartlett et al. (2001) presented a formula to determine sample size that was

applicable to the current research. For continual data with an alpha of .05 and a margin

of error at .03, the required sample size for a target population of 300 is 85. Statistical

power may depend upon a number of factors. Some of these factors may only be relevant

to a particular testing situation, but at a minimum, power nearly always depends upon the

following three factors: (a) the criterion used for statistical significance in the test, (b) the

magnitude of the effect on the population, and (3) the sample size used to detect the

effect (Ellis, 2010). A confidence level of 95% is viewed as achievable with a sample

size of 110 individuals.

An interval sample size can be estimated using statistical methods based upon the

theory of sampling variation (Wright, 2008). The population value is estimated at the

chosen level of certainty. A 95% confidence interval is plausible for an unknown effect.

Armitage and Berry (2002) confirmed that the significance of the confidence level is

equated to a hypothesis test at a specified level for all possible values of the population

effect. While confidence interval levels identify no information surrounding the effect of

a relationship, they do identify the importance of the study findings (Wright, 2008).

Data Collection Methods

The target audience for the data collection was healthcare professionals at a non-

profit central Florida hospital. Data collection was accomplished using an online survey

via Survey Monkey. An e-mail with a link to the survey was sent to all eligble

participants within the hospital. A total of 162 surveys were returned.

Data were collected in the current study via electronic means managed by Survey

Monkey, an on-line third-party vendor. The survey was retrieved by participants via an

Internet link provided following their return of the informed-consent form. Those

potential participants who did not agree to informed consent were not allowed access to

the survey. Completed instruments were returned to the third-party vendor through an

online hyperlink. The participants were asked to complete the survey within one week of

their receipt of the Internet link for access to the questionnaire.

Three reminders were sent within a one month period to those failing to return the

survey within the allotted time until the target sample size was achieved. Nulry (2008)

found that e-mail reminders to nonresponders and incentives increase online response

rates with online surveys. Ballantyne (2005) reported that e-mail distribution of the

Internet link to the survey also yields higher response rates. Edsall (2007) indicated that

the return rate for electronic surveys has been slightly higher than that reported for

written surveys. Consequently, the choice to use an electronic survey in this study was in

the interest of a higher response rate.

An interval sample size was estimated using statistical methods based upon the

theory of sampling variation (Wright, 2008). Statistical power depends upon the

following three factors: (a) the criterion used for statistical significance in the test, (b) the

magnitude of the effect on the population, and (3) the sample size used to detect the

effect (Ellis, 2010). A confidence level of 95% is viewed as achievable with a sample

size of 110 individuals. A sample size of 162 respondents was obtained in this study.

Instrument

The survey administered in this study addresses dimensions of the patient-safety

climate through questions on how safety concerns are handled; the roles of nurses,

leadership, and physicians; and individual beliefs surrounding the effectiveness of safety

measures (Sexton et al., 2006; see Appendix D). A Likert-type rating scale measured the

strength of respondent agreement to all survey items (Delaney, 2004; Likert, 1932; Pell,

2005). The 6-point scale offered the following responses for each item: 1 = disagree

strongly, 2 = disagree slightly, 3 = neutral, 4 = agree slightly, 5 = agree strongly, 6 = not

applicable. Colon-Emeric et al. (2004) conducted a study measuring ten content areas

related to perceived barriers to the implementation of guidelines for patient safety. The

content areas were representative of a single theme and addressed major components of

the patient-safety culture within health-care organizations and its assessment (AHRQ,

2005).

Survey research employs face-to-face interviews, telephone surveys, or mailed

questionnaires (Creswell, 2003). Of these three data-collection techniques, the survey

was the most appropriate for the current study because it yields the optimal results from a

dispersed population sample. The survey technique also eliminates any potential for the

interjection of personal bias by the researcher into the responses (Asch, Jedrziewski, &

Christakis, 1997; Balnaves & Caputi, 2001; Creating effective surveys, 2006). The Safety

Attitudes Questionnaire (SAQ) facilitated the collection of the quantitative data needed

for this research. This instrument has been previously used as a tool for assessing the

effectiveness of quality-improvement programs and respondent attitudes. Compared to

other techniques considered for this study, the survey method of data collection also

introduced minimum cost (Creswell, 2003).

The survey technique was appropriate for this research because data can be

collected on a random basis that will subsequently answer the research questions via

statistical techniques (Balnaves & Caputi, 2001). Nulry (2008) reported that online

surveys not only provide an opportunity for the protection of participant identities

through later aggregate reporting, but also provide data in real time. In addition to

drawing quantitative data on the independent and dependent variables, the survey

provided data indicating the statistical relationship between the variables (Creswell,

2003). The inductive analysis allowed generalization of the study conclusions to the

larger target population (Ader & Mellenbergh, 1999).

Validity and Reliability

The validity of survey data is dependent upon a shared understanding of the

statements and response options, all of which must be phrased in a manner that is

understood by all respondents (Adamson, Gooberman-Hill, Woolhead, & Donovan,

2004). Maxwell (2005) contended that quantitative study can provide flexibility with

controls addressing both anticipated and unanticipated threats to validity. Creswell

(2003) asserted that a significant research study must ensure reliable measures and

results. A reliable instrument will result in consistent, error-free results. Internal

validity, as indicated by Leedy and Ormrod (2001) and Golafshani (2003), is based upon

the procurement of accurate data from the study participants. Proper selection of the

instrument, sample, and statistical analysis will ensure internal validity, which can also be

reduced by the backgrounds of the sample surveyed. This type of validity is measured by

how closely the conclusions match reality (Merriam, 1998).

The SAQ consists of 19 items with a 5-point Likert scale, ranging from disagree

strongly to agree strongly, to evaluate six key domains—teamwork climate, job

satisfaction, perceptions of management, safety climate, working conditions, and stress

recognition (Kao & Thomas, 2008). The instrument has been validated with pilot testing

and confirmatory factor analyses and has been modified for use within various health-

care arenas including general inpatient and ambulatory settings. Based upon extensive

testing across three countries, 203 clinical areas, and over 10,000 health-care providers,

the SAQ has been psychometrically validated for use within the medical field (Sexton

et al., 2006). The internal validity of the SAQ showerd test – retest reliability of .85-

.90 and Cronbach values of .75- .88. The survey instrument proved to be reliable and

was administered to the populatin for this study.

Ensuring a study sample is representative of the larger target population is crucial

in attaining external validity (Leedy & Ormrod, 2001). Study reliability is measured by

its replicability. A comparative analysis is conducted by examining factors identified

within the literature and the data collected, coded, and categorized from the survey. In

the current study, the validation of a safety-climate culture demonstrates that the medical

personnel are influenced by environmental, peer, and cultural demands (Bognár et al.,

2008). Bognár et al. (2008) suggested that safety attitudes among team members may

impact ultimate employee performance. They posited, ―Providers’ reluctance to share

safety events with others and perceived powerlessness to prevent events, must be

addressed as part of an overall strategy to improve patient care outcomes‖ (p. 1376). The

Bognár et al. study highlighted the need to address the team culture in efforts to improve

patient care.

Slow progress toward improved patient safety may be the result of poorly

articulated safety-improvement goals and measures (Pronovost et al., 2009). The validity

of a measure must be analyzed at two levels. In the current study, one level is the patient-

safety domain, which is measuring the extent to which improvement in safety

interventions will result in improved patient outcomes. The other level is minimized bias

through a framework designed to identify and prioritize effective safety interventions.

The establishment of external validity equates to results that can be applied or

generalized to a larger population. Leedy and Ormrod (2001) described an external-

validity interview as the selection of a real-life setting and posing the same questions to

all participants, ensuring consistency. External-validity methods examine how well the

research conclusions can be generalized from a sample to the target population and

ensure objective research procedures.

Data Analysis

The independent variable of this research was the perceptions of barriers to

quality care improvement measured by a Likert-type scale survey. Employee perception,

for purposes of this study, refers to the manner in which employees interpret the impact

of quality-improvement programs on their work responsibilities. The dependent variable

was the statistical data of the sample characteristics. According to Kohn et al. (2000),

―Medical errors are the failure of a planned action as intended or the use of a wrong plan

to achieve an aim‖ (p. 54). Employee perceptions of barriers to quality improvement are

influenced by their professional roles and their relationships with management personnel.

Raw data were entered into SAS software and descriptive statistics were used to

generate frequencies and percentages for the demographic information collected from the

nurses, managers, and administrators. The descriptive statistics and frequency

distributions facilitated determination as to whether the data were within the possible

range of values. No data were removed. The presence of outliers was tested by

examining standardized residuals.

This study used simple frequency distribution, central tendency, and variability.

Quantitative variables assume several forms, frequently referred to as levels of

measurement, which affect the type of data analysis considered appropriate. Attributes of

the sample population in this study were measured as ordinal. Although the ordinal level

of measurement yields a ranking of attributes, no assumptions were made surrounding the

distance between classifications (Hahn & Meeker, 1991), nor were assumptions made

surrounding the difference between individuals who greatly attribute or moderately

attribute perceived barriers to the effectiveness of quality improvement.

The survey responses were placed into SAS software program. The items of the

instrument were rated on a 6-point, Likert-type scale (i.e., 1= disagree strongly,

2 = disagree slightly, 3 = neutral, 4 = agree slightly, 5 = agree strongly, and 6 = not

applicable; Delaney, 2004; Likert, 1932; Pell, 2005. The data were coded in a manner

consistent with the survey items, reviewed, and compared a second time to confirm the

initial interpretation. Upon conclusion of the data-entry process, the information was

cross-checked for accuracy and no entry errors were detected.

Hypotheses were tested to reject the null when it is actually false or fail to reject

the null when it is actually true (Howell, 2004). The potential always exists for findings

based upon statistical testing that was incorrect. Statistical analysis was performed and

results interpreted. Statistically significant results are interpreted not likely to have

occurred purely by chance and therefore, have other causes for their occurrence. The

results of non-statistical significance can be interpreted only that the experient failed to

find any difference.

The process of selecting an appropriate statistical technique is important when

conducting research (Polonsky & Waller, 2005). Descriptive statistics provides a

summary about the sample and observiations that have been made. The data collected

were analyzed using the spearman rank correlation, Kruskal- Wallis one way analysis,

Wilcoxin 2 sample test, Shapiro Wilk test, and the Levene test. A Spearman rank

correlation test was performed to assess how well the relationship between two variables

can be described. The Kruskal- Wallis one way analysis of variance was performed for

comparing more than two samples to indicate at least one of the samples is different from

the other. An alternative to the t-test is the Wilcoxin 2 sample test to assess whether the

population mean ranks differ. Lastly, to test the null hypothesis, both the Shapiro Wilk

and Levene tests were completed.

As noted earlier, data collected in this study were analyzed using SAS computer

software. Descriptive analysis was performed for all variables, and continuous variables

were examined for normal distribution. Depending upon the level of measurement and

distribution of each variable, the data were expressed in frequencies and proportions,

means and standard deviations, or medians and interquartile ranges. Participant

responses are presented in charts and statistically analyzed, focusing on frequencies,

means, and standard deviations. A narrative conclusion is based upon a finding for each

statistical set. The data-analysis plan consists of three phases. The first involves

identifying the dependent and independent variables. The second applies descriptive

statistics that facilitate a description of responses to each survey question and reveal

overall trends (Creswell, 2003). The third phase of the data-analysis plan evaluates or

otherwise analyzes the quantitative data.

Summary

The data collected in this study were analyzed via descriptive statistical methods.

The research examined whether perceived barriers to implementing quality-improvement

measures have an impact on the reduction of medical error. The participants have been

described, as well as the proposed procedures, tools, and planned statistical analyses.

The appropriateness of the research method and design has been examined relative to the

goals of the study (Creswell, 2003). The results reflect the desire to significantly further

patient safety within the health-care environment and meet the burden of an increasingly

demanding medical-care accountability system. Statistical analysis of the survey data

facilitated presentation of the findings.

Chapter 4: Results

The results of the research are reported in this chapter. The findings are organized

and reposted by research questions, with the results of the hypothesis testing given. The

purpose of chapter 4 is to provide a detailed analysis of the statistical methods used to

translate the collected survey data into a conclusion in response to the research questions

and hypothesis. The purpose of this quantitative study was to explore the perceptions of

hospital personnel regarding quality-improvement initiatives as a collective barrier

potentially inhibiting the reduction of medical error. Toward this end, data were

collected from a sample of nurses, management, and administrative leaders within the

health-care setting.

Instrument

The SAQ, a psychometrically validated survey for use within the medical field

(Sexton et al., 2006), was administered for the collection of data. The research questions

addressed six key domains—teamwork climate, job satisfaction, perceptions of

management, safety climate, work conditions, and stress recognition—to determine the

impact of the perceptions of the survey respondents with regard to the culture of safety

within their organizations. The study sample included 162 participants—81 unit nurses,

37 nurse managers, and 44 administrators—of a nonprofit, tertiary hospital within central

Florida. The data collected from all 162 participants were used in the study analysis.

The following research questions and hypotheses guided this research:

R1. Do perceptions of barriers exist that influence quality care improvement within

hospitals and the reduction of medical error?

H1A stated that significant barriers exist that impede quality care improvement

within hospitals and the reduction of medical error.

H10 stated that no significant barriers exist that impede quality care improvement

within hospitals and the reduction of medical error.

R2. Do the perceptions of barriers to quality care improvement within hospitals

differ among nurses and hospital managers and administrators?

H2A stated that the perceptions of nurses and hospital managers and

administrators significantly differ with regard to barriers to quality care improvement

within the hospital setting.

H20 stated that the perceptions of nurses and hospital managers and administrators

do not significantly differ with regard to barriers to quality care improvement within the

hospital setting.

Demographic Data. The sample in this study consisted of 162 participants. As

illustrated in Figure 1, the majority are female (n = 136). The participants were between

29 and 44 years of age with between 6 months and more than 20 years experience

working in a health care role. Figure 2 indicated a mean age of 32.4 years and a mean

number of years experience in the field of 20.68. Age was widely disbursed and the

standard deviation relative to years of experience was high. Participant demographics in

this study were limited to personal information and were used to describe the sample and

complare to national norms. This information included gender; age; and years of

experience within the health-care field, current specialty, and at the study site. The

numbers and percentages of participants by gender are shown in Figure 1.

Another voluntarily submitted demographic was the age of the participants, which

was not correlated with years of experience within the health-care field (see Table 1).

Figure 2 indicates the mean age of the study participants and mean years of experience

within the health-care field. The participants ranged from 29 to 45 years of age and

between 6 and 10 years of experience within the health-care field. The mean age of the

sample was 32.4 years and the mean number of years experience within the health-care

field was 20.68 (see Figures 3–5).

Figure 1. Number and percentages of study participants by gender.

100

120

140

160

Number of participants

Percentage of participants

Male

Female

Table 1

Mean and Standard Deviations for Age and Experience of the Study Participants

Variable M SD

Age in years 32.4 9.56

Years experience within health-care field 20.68 10.09

Figure 2. Mean age of the study participants and mean years of experience within the

health-care field.

Findings

Descriptive statistics use basic aspects of the data collected in a study to ―provide

simple summaries about the sample and the measures‖ (Trochim, 2006, p. 1). Simple

analysis was conducted in this current study with quantitative data and the results were

compiled. The compilation of results are provided in the tables and figures.

Explanations of the frequencies and percentages, the mean and standard deviations, and

the analyses of variance applicable to the study are explained in detail. Analyzing data

Age Field

drawn from responses to a Likert-type scale requires unique statistical procedures (Boone

& Boone, 2012). Likert (1932) developed a procedure for measuring attitudinal scales in

response to the difficulty of measuring character and personality traits. The resultant

Likert scale is a series of response alternatives to corresponding statements. The

combined responses create an attitudinal measurement scale.

In this current study, the Likert response ratings are not combined to form a

composite scale. A Likert scale is composed of a series of four or more Likert-type items

combined into a single score during the analysis to provide a quantitative measure or

personality trait. The Steven’s Scale of Measurement (Ary, Jacobs, & Sorenson, 2010)

assesses four categories of data—nominal, ordinal, interval, and ratio. The Safety

Climate Survey administered in this study includes a series of individual questions and a

series of statements to be rated on a Likert-type scale. Therefore, both ordinal and

interval data were analyzed.

Numbers are assigned to the responses on the Likert-type scale, which correspond

to the ordinal measurement scale. However, Likert scale data are analyzed at the interval

measurement scale. Data analysis was conducted in this study for ordinal, Likert-type

data calculating mode and median for central tendency and frequencies for variability.

Additional analysis included the Spearman rank and the Kruskal-Wallis one way analysis

test. Analysis of the Likert-scale responses included the mean for central tendency and

standard deviations for variability. Additional data analyses included Cronbach’s alpha,

the Shapiro-Wilk test, and the Levene test for homogeneity variance.

Frequency distribution. The frequency distribution was determined by a count of

the values rated by each respondent to each question. This includes the general

descriptive information collected from all participants in response to each survey

question on the Likert scale (1 = disagree strongly, 2 = disagree slightly, 3 = neutral,

4 = agree slightly, 5 = agree strongly), the percentage of missing data, and the overall

mean or standard deviation. Table 2 provides general descriptive information collected

from the participating hospital administrators in response to each survey question, as well

as the percentage of missing data and the overall mean or standard deviation. Minimal

dispersion was evidenced among the responses, with the exception of Question 18, which

deviates from the engagement of the respondent. As a result, responses to this question

rate the lowest with the highest percentage of responses across all three groups (i.e.,

administrators, nurse managers, and nurses).

2.5

3.0

3.5

4.0

4.5

5.0

3039< 30  40

SCD SED PMD

Age

S u

rv e

y R

e s p

o n

s e

Figure 3. Safety-climate domains based upon participant age. SCD = safety-climate

domain; SED = safety-environment domain; PMD = perception-of-management domain.

2.5

3.0

3.5

4.0

4.5

5.0

37 2  8

SCD SED PMD

Years Experience in Specialty

S u

rv e

y R

e s p

o n

s e

Figure 4. Safety-climate domains based upon years participants in specialty. SCD =

safety-climate domain; SED = safety-environment domain; PMD = perception-of-

management domain.

2.5

3.0

3.5

4.0

4.5

5.0

37 2  8

SCD SED PMD

Years Experience in Position

S u

rv e

y R

e s p

o n

s e

Figure 5. Safety-climate domains based upon years of participant experience in position.

SCD = safety-climate domain; SED = safety-environment domain; PMD = perception-of-

management domain.

Table 2

Frequency Distribution for the Administrators Study Group (N = 44)

Survey question

1 _______

2 _______

3 _______

4 _______

5 _______

Missing ________

N % N % N % N % N % N %

1 4 9.09 1 2.27 7 15.91 10 22.73 19 43.18 3 6.82

2 1 2.27 3 6.82 7 15.91 4 9.09 23 52.27 6 13.64

3 3 6.82 1 2.27 3 6.82 16 36.36 20 45.45 1 2.27

4 1 2.27 4 9.09 2 4.55 13 29.55 22 50 2 4.55

5 1 2.27 5 11.36 3 6.82 6 13.64 27 61.36 2 4.55

6 0 0 3 6.82 8 18.18 14 31.82 19 43.18 0 0

7 1 2.27 5 11.36 5 11.36 6 13.64 27 61.36 0 0

8 1 2.27 1 2.27 3 6.82 7 15.91 31 70.45 1 2.27

9 1 2.27 4 9.09 1 2.27 4 9.09 34 77.27 0 0

10 0 0 4 9.09 3 6.82 9 20.45 27 61.36 1 2.27

11 1 2.27 2 4.55 3 6.82 15 34.09 22 50 1 2.27

12 0 0 0 0 0 0 3 6.82 38 86.36 3 6.82

13 3 6.82 2 4.55 4 9.09 11 25 22 50 2 4.55

14_nurses 1 2.27 1 2.27 2 4.55 7 15.91 18 40.91 15 34.09

14_pharmacists 2 4.55 5 11.36 9 20.45 10 22.73 9 20.45 9 20.45

14_physicians 2 4.55 7 15.91 8 18.18 6 13.64 4 9.09 17 38.64

15 1 2.27 2 4.55 9 20.45 14 31.82 18 40.91 0 0

16 2 4.55 1 2.27 5 11.36 9 20.45 27 61.36 0 0

17 1 2.27 0 0 2 4.55 8 18.18 31 70.45 2 4.55

18 23 52.27 8 18.18 2 4.44 6 13.64 4 9.09 1 2.27

2 4.55 2 4.55 4 9.09 4 9.09 31 70.45 1 2.27

Table 3 provides general descriptive information collected from the participating

nurse managers in response to each survey question, as well as the percentage of missing

data and overall mean or standard deviation. Nearly 50% of the nurse managers did not

respond to Question 14. This is reflective of literature suggesting an environment of

distrust or fear among those holding mid-management positions and routinely engaging

with clinical coworkers. Table 4 provides general descriptive information collected from

the participating nurses to each survey question, as well as the percentage of missing data

and the overall mean or standard deviation. In contrast to the responses from the nurse

managers to Question 14, the nurses responded favorably. This may indicate that the

nurses feel safe and supported by other clinicians and hospital administrators.

Table 5 identifies the median, mode, range, and quartile range for data collected

from all respondents (i.e., administrators, nurse managers, and nurses). The range for the

nurse-manager responses is significant. The data show there is low to no variation in the

responses from these respondents in the quartile range. However, the minimum to

maximum range for this group indicates the greatest variance among all participants. To

assess the statistical difference between the independent and dependent variables on an

ordinal scale, a Spearman rank correlation was performed. As with any correlation

calculation, the Spearman rank correlation is appropriate for both continuous and discrete

variables including ordinal variables.

Table 3

Frequency Distribution for the Nurse-Managers Study Group (N = 37)

Survey question

3 4

Missing

N % N % N % N % N % N % 1 0 0 2 5.41 2 5.41 3 8.11 30 81.08 0 0

2 0 0 2 5.41 1 2.7 6 16.22 27 72.97 1 2.7

3 0 0 1 2.7 1 2.7 9 24.32 26 70.27 0 0

4 0 0 0 0 1 2.7 4 10.81 31 83.78 1 2.7

5 0 0 0 0 0 0 4 10.81 33 89.19 0 0

6 0 0 2 5.41 1 2.7 4 10.81 30 81.08 0 0

7 0 0 3 8.11 1 2.7 4 10.81 29 78.38 0 0

8 0 0 0 0 0 0 0 0 37 100 0 0

9 0 0 0 0 1 2.7 2 5.41 34 91.89 0 0

10 0 0 1 2.7 0 0 7 18.92 28 75.68 1 2.7

11 0 0 0 0 1 2.7 6 16.22 30 81.08 0 0

12 0 0 2 5.41 0 0 2 5.41 32 86.49 1 2.7

13 0 0 2 5.41 1 2.7 7 18.92 26 70.27 1 2.7

14_nurses 0 0 0 0 0 0 2 5.41 25 67.57 10 27.03

14_pharmacists 1 2.7 4 10.81 7 18.92 8 21.62 8 21.62 9 24.32

14_physicians 0 0 4 10.81 2 5.41 11 29.73 2 5.41 18 48.65 15 0 0 1 2.7 0 0 6 16.22 30 81.08 0 0

16 0 0 1 2.7 2 5.41 9 24.32 25 67.57 0 0

17 0 0 0 0 1 2.7 1 2.7 34 91.89 1 2.7

18 24 64.86 6 16.22 2 5.41 4 10.81 1 2.7 0 0

19 0 0 0 0 0 0 2 5.41 35 94.59 0 0

Table 4

Frequency Distribution of the Nurses Study Group (N = 81)

Survey

question

Missing

N % N % N % N % N % N %

1 5 6.17 11 13.58 10 12.35 27 33.33 28 34.57 0 0

2 5 6.17 10 12.35 11 13.58 16 19.75 37 45.68 2 2.47

3 14 17.28 13 16.05 11 13.58 17 20.99 26 32.1 0 0

4 3 3.7 14 17.28 3 3.7 30 37.04 31 38.27 0 0

5 9 11.11 14 17.28 9 11.11 9 11.11 40 49.38 0 0

6 12 14.81 13 16.05 6 7.41 21 25.93 29 35.8 0 0

7 10 12.35 12 14.81 10 12.35 17 20.99 31 38.27 1 1.23

8 6 7.41 7 8.64 7 8.64 20 24.69 41 50.62 0 0

9 0 0 4 4.94 4 4.94 18 22.22 55 67.9 0 0

10 4 4.94 4 4.94 6 7.41 28 34.57 39 48.15 0 0

11 5 6.17 13 16.05 9 11.11 15 18.52 38 46.91 1 1.23

12 3 3.7 0 0 4 4.94 15 18.52 56 69.14 3 3.7

13 12 14.81 3 3.7 5 6.17 14 17.28 44 54.32 3 3.7

14_nurses 6 7.41 9 11.11 6 7.41 9 11.11 36 44.44 15 18.52

14_pharmacists 8 9.88 11 13.58 11 13.58 18 22.22 16 19.75 17 20.99

14_physicians 7 8.64 10 12.35 6 7.41 17 20.99 13 16.05 28 34.57

15 12 14.81 4 4.94 16 19.75 23 28.4 26 32.1 0 0

16 3 3.7 4 4.94 3 3.7 17 20.99 54 66.67 0

2 2.47 3 3.7 4 4.94 23 28.4 49 60.49 0

36 44.44 21 25.93 9 11.11 13 16.05 2 2.47 0

19 2 2.47 8 9.88 6 7.41 20 24.69 45 55.56 0

Table 5

Descriptive Statistics From Survey Responses

Administrator

_______________________

Nurse manager

_______________________

Nurse

_______________________

Survey

question

Median

Mode

Quartile

range

Median

Mode

Quartile

range

Median

Quartile

range

1 4 5 2 41 5 5 0 37 4 5 2 81

2 5 5 2 38 5 5 1 36 4 5 2 79

3 4 5 1 43 5 5 1 37 4 5 3 81

4 5 5 1 42 5 5 0 36 4 5 1 81

5 5 5 1 42 5 5 0 37 4 5 3 81

6 4 5 2 44 5 5 0 37 4 5 3 81

7 5 5 2 44 5 5 0 37 4 5 3 80

8 5 5 1 43 5 5 0 37 5 5 1 81

9 5 5 0 44 5 5 0 37 5 5 1 81

10 5 5 1 43 5 5 0 36 4 5 1 81

11 5 5 1 43 5 5 0 37 4 5 2 80

12 5 5 0 41 5 5 0 36 5 5 1 78

13 5 5 1 42 5 5 1 36 5 5 2 78

14_nur 5 5 1 29 5 5 0 27 5 5 2 66

14_pha 4 4 2 35 4 4 2 28 4 4 3 64

14_phy 3 3 2 27 4 4 1 19 4 4 2 53

15 4 5 2 44 5 5 0 37 4 5 2 81

16 5 5 1 44 5 5 1 37 5 5 1 81

17 5 5 1 42 5 5 0 36 5 5 1 81

18 1 1 2 43 1 1 1 37 2 1 2 81

19 5 5 1 43 5 5 0 37 5 5 1 81

Note. Nur = nurses; pha = pharmacists; phy = physicians.

The Spearman rank correlation is a nonparametric measure of statistical

dependence between two variables. It assesses how well the relationship between two

variables can be described using a monotonic function. If there are no repeated data

values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a

perfect monotone function of the other variables. The results (see table 6) show that data

compared between the administrator group and the nurse manager group for question 7,

―management/leadership does not knowingly compromise safety concerns for

productivity‖, indicates the Spearman rank of .60536 and p-value of <.0001. This high

predictor of observations reveals an agreement between the two groups about the

importance of safety.

The Kruskal-Wallis one-way ANOVA is a nonparametric method for testing

whether samples originated from the same distribution and can be used for comparing

more than two independent samples or are unrelated (Corder & Foreman, 2009). While

the test does not identify where differences occur or the number of differences, the results

can lead to significant findings when one of the samples varies from the other samples.

Table 7 provides the results from the Kruskal-Wallis test run for the current study.

Participant responses to Questions 13, 14, 16, and 18 indicated significant differences

between the administrators, nurse managers, and nurse study groups.

http://en.wikipedia.org/wiki/Non-parametric_statistics

Table 6

Spearman Product-Moment Correlation

Survey question

Administrator

_____________________

Nurse manager

___________________

Nurse

___________________

Spearman r p value N Spearman r p value N Spearman r p value N

1 .51897 .0005 41 .11312 .505 37 .33786 .002 81

2 .13784 .4092 38 .03545 .8374 36 .4528 < .0001 79

3 .37144 .0142 43 .12814 .4498 37 .479 < .0001 81

4 .42794 .0047 42 .22367 .1897 36 .36929 .000 81

5 .52767 .0003 42 .30549 .066 37 .55717 < .0001 81

6 .42991 .0036 44 .49069 .002 37 .52171 < .0001 81

7 .60535 < .0001 44 .03682 .8287 37 .44911 < .0001 80

8 .26862 .0816 43 – – 37 .22412 .0443 81

9 -.00132 .9932 44 .11538 .4965 37 .16271 .1467 81

10 .30785 .0446 43 .08789 .6102 36 .44571 < .0001 81

11 .32743 .0321 43 .33013 .046 37 .42855 < .0001 80

12 .05837 .717 41 .03122 .8566 36 .03862 .7371 78

13 .33397 .0307 42 .15853 .3558 36 .26038 .0213 78

14_nur .51786 .004 29 .28222 .1538 27 .44567 .0002 66

14_pha .1725 .3217 35 .19194 .3278 28 .40306 .001 64

14_phy -.07151 .723 27 -.21523 .3762 19 -.05537 .6937 53

16 .03453 .8239 44 -.06985 .6812 37 -.0709 .5294 81

17 .27015 .0836 42 .1864 .2764 36 .35384 .0012 81

18 -.10646 .4968 43 -.09032 .595 37 -.26576 .0165 81

19 .31004 .043 43 .23854 .1551 37 .52075

< .0001 81

Note. Nur = nurses; pha = pharmacists; phy = physicians.

Table 7

Kruskal-Wallis Test and Multiple Comparison

Post hoc multiple comparison

(X: significant different, p < .05)

Survey question p value of

Kruskal-Wallis

Nurse manager vs.

administrator

Administrator vs.

nurse

Nurse manager vs.

nurse

1 < .0001 X

2 .0090

3 < .0001 X

4 < .0001 X

5 < .0001

6 < .0001 X

7 .0001 X

8 < .0001 X X

9 .0235

10 .0078 X

11 .0004 X

12 .0088 X

13 .1000 N/A

14_nurses .0011 X

14_pharmacists .6882 N/A

14_physicians .4289 15

< .0001 X

16 .6900 N/A

17 .0011

18 .1905 N/A

19 .0001 X

Safety-climate domains. A multiple comparison assessment was run following

the Kruskal-Wallis test due to the significant differences. While the Kruskal-Wallis test

can identify a difference between two or more study groups, a multiple comparison

assessment identifies which groups are different. The first two study groups compared

against the survey questions in this research were the administrators and nurse managers.

With 7 out of the 19 survey questions, a difference was indicated between these groups;

however, significance in the responses was evident with only five of the questions. The

most significant difference found within the survey responses emerged in the comparison

between the nurse manager and nurse study groups. This result was evident with 15 out

of 19 of the questions.

For Question 8, the multiple comparison test revealed that all three of the

comparison groups according to the age of the participants indicated no significant

difference. However, the p-value for Kruskal Wallis test for the safety environment, .0580,

according to the participants’ years of experience in the position, indicates significance.

Finally, the Kruskal-Wallis test was conducted across the demographic variables of age,

years of experience in current position, experience within current specialty, and experience

within the organization (see Tables 8–11).

Table 8

Survey Responses According to the Age of the Participants

Domain

> 30

(N = 24)

30–39

(N = 26)

≥ 40

(N = 105)

p value of Kruskal

Wallis Test

Safety climate 3.93 ± .59 3.79 ± .74 4.00 ± .64 .2274

Safety environment 4.40 ± .73 4.23 ± .82 4.31 ± .77 .7627

Perceptions of

management 4.27 ± .78 3.88 ± 1.13 4.08 ± .96

.6204

Table 9

Survey Responses According to Years of Participant Experience in Position

Domain

≤ 2

(N = 48)

3–7

(N = 48)

≥ 8

(N = 66)

p value of Kruskal

Wallis Test

Safety climate 4.03 ± .7 4.01 ± .56 3.85 ± .69 .2142

Safety environment 4.43 ± .75 4.42 ± .67 4.13 ± .83 .0580

Perceptions of

management

4.33 ± .83

4.08 ± .98

3.87 ± 1.04

.0499

Table 10

Survey Responses According to Participant Experience in Specialty

Domain

≤ 2

(N = 35)

3–7

(N = 38)

≥ 8

(N = 89)

p value of Kruskal

Wallis Test

Safety climate 3.77 ± .59 4.06 ± .62 3.97 ± .69 .0693

Safety environment 4.17 ± .66 4.44 ± .71 4.3 ± .83 .1026

Perceptions of

management

3.94 ± .84

4.21 ± .93

4.06 ± 1.05

.2599

Table 11

Survey Responses According to Years of Participant Experience in Organization

Domain

≤ 2

(N = 18)

3–7

(N = 60)

≥ 8

(N = 84)

p value of Kruskal

Wallis Test

Safety climate 3.67 ± .91 3.9 ± .68 4.04 ± .56 .2103

Safety environment 4.13 ± .96 4.29 ± .76 4.35 ± .74 .5558

Perceptions of

management

3.81 ± 1.1

4.04 ± 1.01

4.14 ± .93

.2507

Three domains of safety were analyzed as interval in this study. The first is the

safety climate, which was addressed in Survey Questions 1, 2, 8, 9, 10, 11, and 18.

Safety climate can be described as the shared safety perceptions within a social unit at a

given time. Recent research has found important relationships between measures of

safety climate and safety performance (Sexton et al., 2006). The second domain is safety

engagement, which was addressed in Survey Questions 1, 3, 8, 12, 13, 17, and 19.

Positive safety engagement requires effective reporting from staff on frontline safety

problems and other related concerns. As problems are communicated, mechanisms must

be in place to respond to the reporter regarding any subsequent action. Effective

leadership sends a strong message and creates the conditions necessary to eliminate error.

The final analysis conducted with the ordinal data was the Wilcoxon two-sample test to

compare the study groups across the three domains and by gender (see Table 12). While

the female sample was significantly larger than the male group, no significant difference

was evidenced in the responses between these populations.

Table 12

Gender of Study Participants

Domain

Female

(N = 136)

Male

(N = 26)

p value of

t test

Wilcoxon two-

sample test

Safety climate 3.92 ± .69 4.09 ± .49 .2240 .2461

Safety environment 4.27 ± .80 4.51 ± .55 .0667 .0885

Perceptions of

management

4.03 ± 1.01

4.3 ± .79

.1898

.2490

Item analysis allows the characteristics of a particular question or survey item to

be observed to ensure inclusion is appropriate. Cronbach’s alpha facilitates

determination of internal consistency or the average correlation of items in a survey to

gauge reliability of the instrument. It is used to indirectly indicate the degree to which a

set of items measures a single or common characteristic. Cronbach’s alpha was run on all

three domains of this study—safety climate, safety engagement, and perceptions of

management. Table 13 provides the results of the item analysis via Cronbach’s alpha.

This statistic is an overall correlation with values ranging between 0 and 1. Nunnaly

(1978) indicated that .7 is an acceptable reliability coefficient. When a variable

generated from a set of questions returns a stable response, the variable is reliable. The

higher the score, the more reliable the generated scale. The results of all three domains of

this study indicated a statistically significant correlation among the survey questions

constituting the domains.

Table 13

Cronbach’s Alpha Results by Domain

Domain Cronbach’s coefficient alpha (Standardized)

Safety climate .717194

Safety environment .826000

Perceptions of management .907032

The standard deviation and mean was calculated for each of the three domains

(i.e., safety climate, safety environment, and perceptions of management; see Table 14).

The smallest amount of variation occurred with the standard deviation of the nurse-

manager responses across all three domains. The highest amount of variation occurred

with all responses between each study group within the perceptions-of-management

domain. The Shapiro-Wilk assessment tests the null hypothesis of a normally distributed

population (see Table 15). If the probability value is less than the chosen alpha level, the

null hypothesis is rejected. If this value is greater than the chosen alpha level, the null

hypothesis is accepted. The Shapiro-Wilk test was run for each of the three domains (i.e.,

safety climate, safety environment, and perceptions of management) and each group (i.e.,

administrators, nurse managers, and nurses).

Table 14

Basic Statistics

Domain

All

(N = 162)

Administrator

(N = 44)

Nurse manager

(N = 37)

Nurse

(N = 81)

Safety climate

3.95 ± .66

3.98 ± .62 4.33 ± .31 3.76 ± .72

Safety environment

4.3 ± .77

4.39 ± .70 4.79 ± .28 4.04 ± .84

Perception of

management

4.07 ± .98

4.17 ± .83

4.73 ± .35

3.71 ± 1.07

Table 15

Normality Check

P value of Shapiro-Wilk test

Domain

Administrator

(N = 44)

Nurse manager

(N = 37)

Nurse

(N = 81)

Safety climate .0029 .0681 < .0001

Safety environment < .0001 < .0001 < .0001

Perception of

management

< .0001

Note. A probability value less than .05 violates the normality assumption.

Null Hypothesis 1 stated that no significant barriers exist that impede quality care

improvement within hospitals and the reduction of medical error. Null Hypothesis 2

stated that the perceptions of nurses and managers and administrators do not significantly

differ with regard to barriers to quality care improvement within the hospital setting. For

the administrator and nurse study groups, the probability value of the data set is less than

.05 across all three domains; therefore, both null hypotheses are rejected. Survey

responses from the nurse-manager group were significantly different from the

administrators and nurse study groups. Within the safety-engagement and perceptions-

of-management domains, the probability value is less than .05; therefore resulting in

rejection of both null hypotheses.

Survey responses within the safety-climate domain yielded a probability value

greater than .05, leading to acceptance of Hypotheses 1 and 2. Hypothesis 1 stated that

significant barriers exist that impede quality care improvement within hospitals and the

reduction of medical error. Hypothesis 2 stated that the perceptions of nurses and

hospital managers and administrators significantly differ with regard to barriers to quality

care improvement within the hospital setting. In summary, this parametric test clearly

indicated that the administrator and nurse study groups are more aligned in identifying

differences between their perceptions of barriers to quality improvement than they are

with the nurse-manager group.

The Levene test for homogeneity variance was conducted to test the variances

among all three domains of this study (see Table 16). This test is used to assess the

equality of variances between the study groups of administrators, nurse managers, and

nurses, as well as to test the null hypothesis of equal population variances. Due to the

probability value of less than .05, the obtained differences in sample variances are

unlikely to have occurred from random sampling drawn from a population with equal

variances. Therefore, the null hypothesis of equal variances is rejected and it is

concluded that a difference exists between the population variances.

Table 16

Constant Variance Check

Domain Levene test for homogeneity variance

Safety climate .0064

Safety environment .0030

Perception of

management

< .0001

Note. A probability value less than .05 violates the homogeneity variance assumption.

Research Question One (RQ1)

R1 asked, ―Do perceptions of barriers exist that influence quality care

improvement within hospitals and the reduction of medical error?‖ To examine this

research question, the survey responses were analyzed to determine whether barriers to

quality improvement within the culture of the participating central-Florida hospital

played a role in the reduction of medical error. The ordinal and Likert-type data were

analyzed for frequencies, tendency, and variability. The results indicated that the nurse-

manager survey responses continually fell outside those of both the administrators and

nurse groups, with increased variability that is suggestive of an environmental barrier

within the safety culture.

The Spearman rank was conducted to assess the statistical difference between the

independent variable, the perceptions of barriers to quality care improvement, and the

dependent variable, the statistical data of the sample characteristics. The nurse-manager

group yielded the highest number of values significant for moderate to strong correlation.

Therefore, H1A was accepted, which stated that significant barriers exist that impede

quality care improvement within hospitals and the reduction of medical error.

Research Question Two (RQ2)

R2 asked, ―Do the perceptions of barriers to quality care improvement within

hospitals differ among nurses and hospital managers and administrators?‖ To examine

this question, the survey responses within the three domains of safety climate, safety

environment, and perceptions of management were examined via the Kruskal-Wallis test

and multiple comparison assessments. Significance occurred among all three study

groups (i.e., administrators, nurse managers, and nurses) and within each of the domains.

The greatest significance was reflected in the difference in responses when comparing the

nurse managers with the nurses. Confirmation of the differences across the comparison

groups was obtained through the multiple comparison test. Breakdown of the

perceptions-of-management domain yielded the most significant results, indicating that

perceptions do exist that may contribute to both a negative and positive safety culture.

Therefore, H2A was rejected, which stated that the perceptions of nurses, hospital

managers, and administrators do not significantly differ with regard to barriers to quality

care improvement within the hospital setting.

Summary

In chapter 4, a discussion of the analysis of the data colleceted from 162

completed surveys was provided. Data collection was discussed including the survey

instrument used and the response rate from each of the groups. Descriptive statistics,

Spearman rank, Kruskal-Wallis one-way analysis, Wilcoxin 2 sample test, Shapiro-Wilk,

and Levene tests were conducted. The data analysis performed tested two null

hypotheses: no significant barriers exist that impede quality care improvement within

hospitals and the reduction of medical error, and the perceptions of nurses, hospital

managers, and administrators do not significantly differ with regard to barriers to quality

care improvement in the hospital setting.

Overall, perceptions of barriers did exist among nurses, nurse managers, and

administrators. The administrator and nurse study groups are more aligned in identifying

differences between their perceptions of barriers to quality improvement than they are

with the nurse manager group. For the administrator and nurse study groups, the

probability value of the data set is less than .05 across all three domains; therefore, both

null hypotheses are rejected. Survey responses from the nurse-manager group were

significantly different from the administrators and nurse study groups. Within the safety-

engagement and perceptions-of-management domains, the probability value is less than

.05; therefore resulting in rejection of both null hypotheses.

In chapter 5, a discussion of the conclusions and recommendations of the results

by framing tehm with the research questions and hypothesis is provided. Implications of

the findings and contribution to leadership are addressed in chapter 5. Recommendations

for leadership and future research concludes chapter 5.

Chapter 5: Conclusions and Recommendations

The general problem addressed in this study was the increase in medical error

within the health-care industry, resulting in increased deaths and health-care costs

(Berntsen, 2004). Messner (1998) cited organizational culture, restructuring, quality-

control functions, and costs as contributing to the slowed progress in quality

improvement. In 2008, medical error cost the United States $19.5 billion (Andel,

Davidow, Hollander, & Moreno, 2012). Hospitals continue to seek ways of improving

quality and operational efficiency, as well as cutting costs, via innovative quality-

improvement strategies. More specifically, this study was conducted to determine

whether the perceptions of hospital personnel with regard to quality-improvement

initiatives can act as a collective barrier potentially inhibiting the reduction of medical

error. Barriers to progress have included the culture and complexity of health care,

continuing skepticism due to the perception of system failures as the underlying cause of

most health-care error, and the fear of malpractice liability that inhibits a willingness to

discuss or even acknowledge such error.

Studies conducted after the IOM (1999) report on medical error have revealed

minimal hospital progress toward reducing the number and significance of such errors

(Berntsen, 2004; Young, 2005). Despite improvements in addressing medical error,

research remains needed to determine how barriers perceived by various health-care

employees impact their reduction. This research study was intended to gain a better

understanding of how the perceptions of hospital employees with regard to quality

improvement may impact their reduction.

The findings from chapter 4 revealed that perceptions of barriers did exist among

nurses, nurse managers, and administrators as well as, differences among each of the

groups. The administrator and nurse study groups were more aligned in identifying

differences between their perceptions of barriers to quality improvement than with the

nurse manager group. The data analysis conducted in this study identified the largest

variance in perceptions between unit nurses and hospital administration. Significant

relationships were found between the literature and research findings.

Within health care, four studies have reported a link between the number of

medication errors and other outcomes through measures of selected safety behavior and

contextual factors within hospital units (Hofmann & Mark 2006; Katz-Navon, Naveh, &

Stern 2005; Neal & Griffin, 2006; Vogus & Sutcliffe, 2007). Weingart, Farbstein, Davis,

and Phillips (2004) found that a climate of safety corresponded to lower rates of injury

and accident reports for four hospitals. The review of related literature conducted for this

study established the importance of the research to future health-care leadership and

hospitals. The gap in establishing collaboration led to a comprehensive understanding of

the factors that continue to influence incidence of medical errors within health-care

facilities.

Findings and Interpretations

The purpose of this quantitative study was to explore the perceptions of hospital

personnel regarding quality-improvement initiatives as a collective barrier potentially

inhibiting the reduction of medical error. The study site is a nonprofit, tertiary care

facility within central Florida. The population sample consisted of 162 participants—81

unit nurses, 37 nurse managers, and 44 administrators. The dependent variables were

analyzed against the independent variable to determine whether the perceptions of the

hospital personnel with regard to quality improvement affect the reduction of medical

error. The findings may be used to improve leadership capabilities in the development

and implementation of quality-improvement measures. Medical outcomes may be

improved by gaining a better understanding of how to effectively develop quality-

improvement measures.

An electronic, Web-based survey instrument was administered to collect data

facilitating answers to the research questions. A statistical-analysis system was applied

to find differences between the study groups. The survey data were cross-referenced with

the demographics obtained. The responses to the online survey were aligned with the

overall scores. The research tools used in data collection answered the following

research questions and their corresponding hypotheses:

R1. Do perceptions of barriers exist that influence quality care improvement

within hospitals and the reduction of medical error? H1A stated that significant

barriers exist that impede quality care improvement within hospitals and the

reduction of medical error. H10 stated that no significant barriers exist that

impede quality care improvement within hospitals and the reduction of medical

error.

R2. Do the perceptions of barriers to quality care improvement within hospitals

differ among nurses and hospital managers and administrators? H2A stated that

the perceptions of nurses and hospital managers and administrators significantly

differ with regard to barriers to quality care improvement within the hospital

setting. H20 stated that the perceptions of nurses and hospital managers and

administrators do not significantly differ with regard to barriers to quality care

improvement within the hospital setting.

Data analysis evaluated the manner in which the perceptions of hospital personnel

with regard to perceptions of barriers to quality improvement might augment or diminish

leadership capabilities in implementing effective quality-improvement measures. The

statistical survey data were analyzed and cross-referenced with the collected participant

demographics. The hypotheses and null hypotheses were tested to determine whether

quality-improvement outcomes might also improve leadership capabilities and interaction

among health-care leaders, concurrently reducing medical error.

Research Question one. Research question one was developed to ask if the

perceptions of health care workers influence quality improvement inititiatives to reduce

medical error. The null hypothesis stated perceptions of health care workers have no

significant influence on quality improvement initiatives to reduce medical error.

The results indicated that the nurse-manager survey responses continually fell

outside those of both the administrators and nurse groups, with increased variability that

is suggestive of an environmental barrier within the safety culture. The nurse-manager

group yielded the highest number of values significant for moderate to strong correlation.

Research Question two. Research question two was developed to ask if the

perceptions of barriers to quality improvement differ among nurses, nurse managers, and

administrators. The null hypothesis stated no significant difference of perceptions of

barriers to quality improvement occurs among nurses, nurse managers, and

administrators.

The results indicated significance occurred among all three study groups (i.e.,

administrators, nurse managers, and nurses) and within each of the domains safety culture

domains. The greatest significance was reflected in the difference in responses when

comparing the nurse managers with the nurses. Breakdown of the perceptions-of-

management domain yielded the most significant results, indicating that perceptions do

exist that may contribute to both a negative and positive safety culture.

Limitations

The potential study limitation that leaders may be hesitant to participate in

research with a potential to report negative findings related to their employing

organizations was not found to be accurate. Survey distribution via a third party Web

link to internal e-mail facilitated reversal of this limitation. Unlike self-administered

surveys, the distribution of questionnaires through an internal mail system provides

sufficient privacy for participants to respond with confidence surrounding matters of

confidentially (Rea & Parker, 2005). The number and cross section of respondents was

fairly distributed across frontline employees (i.e., unit nurses); middle management (i.e.,

nurse managers); and hospital leadership (i.e., administration), adequately representing

the target population. Results of the 162 participants yielded responses from 81 unit

nurses, 37 nurse managers, and 44 administrators demonstrating leaders willingness to

participate.

A second potential limitation to the study was the lack of honesty with the survey

responses. With self-reported data, respondents can be prone to memory error or harbor

an unwillingness to disclose accurate information (Rea & Parker, 2005). Data results

from research question two indicated perceptions of quality improvement initiatives did

100

significantly differ among nurses, nurse managers, and administrators demonstrating

honest responses from the participants.

Implications and Recommendations

The results of this study have importance to healthcare leaders. Many studies on

hospital quality of care have focused on the patient perspective (Gonzalez-Valetin &

Padin-Lopez, 2005; Juwaheer & Kassean, 2006; Pakdil & Harwood, 2005; Wann-Yih,

Shih-Wen, & Hsin-Ping, 2004). Fewer health-care studies have discussed the perceptions

of executive leaders, as they pertain to quality care (Frost & Kumar, 2001; Ovretveit,

2005). To develop a stronger understanding of quality improvement and implement

effective patient-safety initiatives, health-care organizations must understand the

perceived patient-safety climate (Kho, Carbone, Lucas, & Cook, 2005). An

understanding of how the perceptions of health-care personnel can act as a collective

barrier to quality improvement and the reduction of medical error will encourage better

decision making in the implementation of quality-improvement measures. More

effective quality-improvement measures will, in turn, aid in the reduction of medical

error and increase patient safety.

Adjusting to a blame-free culture is challenging due to the prevalence of

physician self-blame (Engel, Rosenthal, & Sutcliffe, 2006). The shift requires a systems

perspective (Collins et al., 2009). The function of a leader to produce outcomes through

performance measures are understood; however, Burke et al. (2006) found that leadership

behavior differs among health-care personnel. Identifying and understanding these

differences can aid in addressing failed organizational and administrative processes and

101

systems. The findings of this current study may provide a significant contribution to

existing research on perceived barriers to quality improvement.

The results of this study may benefit health-care administrators and physician

leaders seeking to establish strong patient-safety cultures through the development of

quality improvement plans. A safety paradox seems to be emerging as quality

improvement decreases with the demand for increased regulatory reporting and

implementation of mandated reporting measures (Schofield, 2007). One way to interpret

the findings of this study is to consider that quality-improvement measures may be more

effective in reducing medical error if they are built upon the characteristics of the

personnel implementing the measures.

Balding (2005) noted that, traditionally, change is managed from a top-down

approach with a majority of quality-improvement activities aimed at Boards and senior

managers rather than frontline staff. While it is essential to align quality-improvement

initiatives with strategic business plans, to sustain any long-term engagement with quality

improvement, top administrators must communicate more effectively with frontline staff.

The top-down approach assumes that staff accepts and implements senior-management

decisions without influencing those decisions (Sabatier, 1997). However, it is the

frontline staff and their willingness to adopt an improvement initiative that ultimately

determines initiative success (Powell, Rushmer, & Davies, 2009). The perceptions

between frontline staff and nursing managers (i.e., middle managers) are more closely

aligned. Middle managers are key links in the process of quality improvement because

they observe the effects of change and thereby adapt and, to a certain extent, control the

speed and influence of change (Balding, 2005). The data from this study suggest that the

102

low variance in the survey responses between nursing managers and unit nurses is a result

of the middle-manager role of interpreting and tailoring high-level quality-improvement

measures into understandable and meaningful processes for frontline staff.

It is necessary to achieve a balance between forward progress with best practice

and effective quality-improvement measures. Barriers to successful quality improvement

arise from the problems associated with teams of multiple professionals of varying age,

experience, and knowledge. Identifying how perceived barriers to quality improvement

can act as a collective barrier to the reduction of medical error will aid health-care leaders

in implementing more effective quality-improvement measures.

Future research. Further research surrounding the relationship between the

safety climate of hospitals and safety performance would be beneficial. The additional

data would facilitate the development of benchmarks, the exploration of differences

among hospital personnel in related perceptions, and the development of strategies to

reduce the negative consequences of such differences. A set of improved management

tools toward greater hospital safety is paramount. Health-care leaders and staff are

critical to aligning available resources with the strategies required to create a true culture

of safety within medical facilities (Vogelsmeier, Scott-Cawiezell, Miller, & Griffith,

2010).

A shared perspective between managers and their clinical staff, including but not

limited to, frontline staff, on quality-improvement programs allows for their most

effective implementation and subsequent success. Many differences in these

perspectives, found throughout existing related literature, are attributed to the perceptions

of service quality among the various stakeholders within health-care organizations.

103

Another gap in related literature is exploration of the perceptions of health professionals

involved in quality-management measures. Further research is required to understand the

various segments of health-care systems and different management issues and causes of

misconceptions in order to develop the appropriate approaches for their handling.

Understanding and reducing department- and profession-level differences is an

underlying strategy for improving the safety culture, which in turn, will improve overall

quality and reduce error (Boan, Nadzam, & Clapp, 2012). Other researchers have

analyzed the differences between medical staff and other hospital staff in their

perceptions of patient safety. Abbas, Bassiuni, and Baddar (2008) found that physician

perceptions of patient safety were high compared to those of nurses and paramedical

personnel.

Judge and Elenkov (2005) concluded that ―efforts to bridge differences in

perceptions between top management, middle management, and frontline workers will

reap considerable rewards, at least with respect to environmental performance‖ (p. 899).

These findings corroborate those of Singer, Lin, Falwell, Gaba, and Baker (2009) who

concluded that a safety culture depends upon a consistency in perception. Because safety

cultures differ significantly, not only between hospitals but also between job classes, if

hospitals are not addressing such disparities between key groups, they are missing a key

element of performance.

Summary

Several studies have suggested potential discrepancies between managerial and

frontline views on certain aspects associated with quality improvement (Balding; 2005;

Bognár et al., 2008; McAlearney, 2008). A shared perspective between managers and

104

their clinical staff on quality-improvement programs allows for their most effective

implementation and increases their success. However, managers are expected to have a

stronger customer focus and a broader organizational perspective (Alrashdi & Oasmi,

2012).

Hospital success depends heavily upon how effectively the workforce (i.e.,

physicians and employees) is organized to deliver the level of health-care services that

produce optimal outcomes (e.g., patient satisfaction, clinical quality, and financial

outcomes). Hospital and other health systems could garner greater returns on the quality

improvement of services by systematically leveraging the perceptions of patients,

physicians, and employees to guide initiatives. The perceptions of an organization are

socially constructed, rather than based solely upon individual experience. Social groups

within organizations become boundaries within which individuals interact as they

construct their understanding of the enterprise (Boan et al., 2012).

105

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Appendix A: Study-Site Permission

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Appendix B: Informed Consent

CONSENT FOR VOLUNTARY RESEARCH PARTICIPATION

I volunteer to participate in this qualitative study and understand that

1. I will be provided with an online link to a survey to complete by rating each item with

one of the following responses: (1) disagree strongly, (2) disagree slightly,

(3) neutral, (4) agree slightly, (5) agree strongly, or (6) not applicable.

2. The questions I will be answering address my views on issues related to quality-

improvement implementation within the hospital for which I am employed. I

understand that the primary purpose of this research is to identify perceived barriers

to implementing quality-improvement systems that may assist in the reduction of

medical error.

3. My name will not be used, nor will I be identified personally in any way, at any time,

or within any subsequent documentation related to this study.

4. I may withdraw my participation from the study at any time with no adverse

consequences.

5. I have the right to review the material prior to the final oral exam completed by the

researcher or publication of the study.

6. I understand that the survey results will be included in the doctoral dissertation of

Cyndi Bergs and may also be included in manuscripts submitted to professional

journals for publication.

7. I am free to participate or not participate in this study without prejudice.

8. I understand that no anticipated risk exists in conjunction with participation in this

study, but that I may experience a degree of emotional discomfort due to the

disclosure of my personal feelings surrounding specific work tasks.

Researcher Signature Date

Participant Signature Date

131

Appendix C: Invitation to Participate

Dear Potential Research Participant:

Your assistance in a research project is requested. The research is being conducted by

me, Cyndi J. Bergs, MBA, MHA. I am a doctoral student in the Doctorate of Health

Administration program at the University of Phoenix. The study is titled Perceived

Barriers to Quality Improvement and Reduced Medical Error: A Quantitative Study. The

purpose of this research is to examine the perceptions of hospital personnel regarding

quality-improvement initiatives as a collective barrier potentially inhibiting the reduction

of medical error. A total of 110 participants is anticipated.

Upon executing this informed consent, you will be asked to complete an online survey.

Completion of the instrument is anticipated to consume approximately 20 minutes.

Participation in this research is strictly voluntary and you may decline to participate or

choose to withdraw at any time during the study with no adverse consequences. There

are no known risks to participants. Although there are also no direct benefits,

participation in this study may improve your understanding of factors contributing to

barriers encountered in the development and implementation of quality-improvement

processes instituted to reduce medical error.

Information provided by all participants will be held strictly confidential and reported

only in an anonymous fashion (i.e., no names, addresses, or other personal identifiers).

All data collected will be transferred to a password-protected computer and held by the

researcher for a period of 5 years. All data will subsequently be destroyed. All

information will be reported as group averages; participant names will never be

associated with the survey responses.

If you have any questions or concerns regarding this study or your role as a participant,

please feel free to contact me at (xxx) xxx-xxxx or my dissertation mentor, Dr. Mary Tan,

at (601) 750-6502. The Institutional Review Board can be reached through (407) 303-

5581.

Thank you for considering participation in this important study.

Cynthia J. Bergs, MBA, MHA

Doctoral Candidate

University of Phoenix

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Appendix D: Permission to Use Survey

133