Assessing Climate for Systems Improvement Initiatives in Healthcare

Increasing medical costs have made healthcare organizations look at reducing their operating costs while meeting their demands, which made them move towards adopting systems improvement methodologies that have been successful in other business sectors, especially from manufacturing industries. The success of these improvement methodologies is contingent on employees of the organization being ready to adopt and embrace them which necessitates behavior change of employees. This study aimed to develop measures based on the Transtheoretical Model (TTM) to assess employees’ attitudes and readiness to adopt improvement methodologies and the effects of employees’ demographics like supervisory level, length of service, work group and age on the adoption process. The study was conducted at the Providence VA Medical Center (PVAMC) which is trying to implement improvement methodologies. All employees were surveyed five times over a period of two and half years using TTM measures. Exploratory factor analysis indicated an 8-item single factor structure for self-efficacy and a 2-factor 16 item structure for decisional balance. An additional set of survey questions related to processes of change scale did not produce a reliable factor structure to be used for hypothesis testing. The results indicated that self-efficacy, which is the confidence to adopt improvement methodologies, did predict the stage of change with low confidence in precontemplation compared to maintenance. The study did not find support that decisional balance, which is the perception of pros and cons, influences the stage of change. Employees’ length of service, supervisory level and work group influenced the stage of change, and length of service and supervisory level influenced selfefficacy measure while age of employee affected self-efficacy.


INTRODUCTION
Though healthcare is one of the most important sectors of the United States economy, it falls short in providing effective and efficient patient centered care. Over the past decade, healthcare costs have increased at a disturbing and unwarranted rate (Gawande, 2009;Zhang et al., 2009;Wellman, 2011). As the external environment becomes more volatile, pressure has increased for healthcare organizations to provide effective care with fewer resources. This has led the healthcare organizations to focus on reducing their operating costs while still providing high quality care to patients and satisfying their employees. In order to meet these demands, healthcare is adapting systems improvement initiatives that have been successful in other business sectors, especially from manufacturing industries.
Systems improvement initiatives are important for any healthcare organization to provide high quality, reliable products and services in the present economy with less cost. The industrial engineering principles which were made popular in automotive manufacturing industries are now being embraced by healthcare. Systems improvement initiatives have taken different forms over the years, such as PDCA (plan, do, check, act) cycles, TQM (total quality management) methods, Six-sigma, Lean Manufacturing, Quality Circles, TPS (Toyota Production System) and other variations specific to individual companies or industries. In the past decade especially, many practitioners have been transferring methods developed in traditional manufacturing industries to office, service, and healthcare settings. Adopting process improvement initiatives provides a systematic framework for organizations to work on both simple and complex problems. Healthcare organizations present many unique features given that the 'product' is patient care, and it is humans as patients who 'flow' through the system during 'production'. Adapting process improvement principles will be unsuccessful unless organizations focus on continuous improvement and develop a culture of continuous improvement. In order to develop a culture of continuous improvement, the organizations' focus should not be limited to introducing new tools or techniques but should concentrate on developing consistent behavior patterns across the organization (Rother, 2010). Organizations' success on adopting the improvement methodologies depends on many factors such as management commitment and involvement, employee involvement, and resource allocation.
Most attempts to change an organizations' culture fail as the principles of psychology of change are ignored (Winum, Ryterband and Stephensen, 1997).
Though high level management initiates new methodologies or changes for improvement, these types of top-down initiatives will not help change the culture of the organization. Attempts to change culture with any new initiatives must match the readiness of the targets of change i.e., all employees of the organization. Individual behavior change is needed for the organization to change its culture. If most of the employees are not willing to adopt the new initiatives that were introduced by the management there will be chaos created which ultimately results in wasted resources and animosity developed against management. So, it is important to measure the adoption rate of employees in the process of implementing new systems improvement initiatives.
The purpose of this study is to develop a tool to measure the organizational change or culture change due to process improvement initiatives using a theoretical model called the Transtheoretical Model of change (TTM). The Transtheoretical Model was developed on the core concept that organizational and individual behavior change occurs in stages and over time. The model defined four theoretical concepts that are needed for change. These are Stages of changereadiness to take action; Decisional Balance -pros and cons of changing; Self-efficacyconfidence to make and sustain changes; and Processes of changeten cognitive, affective, and behavioral activities that facilitate change. In this study the TTM is associated with the involvement of employees in process improvement trainings, participation in process improvement projects and incorporating continuous improvement in everyday work. The TTM is used to measure employees on their stage of readiness to adopt continuous improvement and to provide strategies to help them move from one stage to the next based on their responses to the decisional balance, efficacy and processes of change questions.
In order to develop the tool to measure organizational culture and to identify the factors that affect the adoption of improvement methodologies in healthcare organizations, an 81 question survey was developed using the constructs of the providing inpatient and outpatient services and it has started to adopt industrial engineering techniques such as lean and six-sigma to improve their processes. The medical center also received a three year grant from FY 10 to FY 12 from a national VA systems redesign office to work on systems improvement initiatives and to develop a culture of continuous improvement. The current study focused on measuring change in organizational culture relative to demographic factors of employee supervisory level, age, length of service, work environment and exposure to trainings.
The survey, along with the disclosure form, was approved by the Institutional Review Board (IRB) at the University of Rhode Island and the Providence VA Medical Center. The survey was administered through Survey Monkey, which is a private company that enables users to create their own Web-based surveys. The identity of the respondents was protected by making changes to the survey monkey settings so that responses collected from the surveys are completely anonymous. The web link of the survey was sent through the work email addresses of all employees.
Paper copies of the survey were also made available to workgroups with less access to computers or for employees who prefer paper format. The research team worked with the Office of the Director at the Providence VA Medical Center to send survey links and reminder e-mails to all employees.
After each survey, results were collected from Survey Monkey and multivariate data analysis was done using SAS and SPSS statistical analysis software.
The same data analyses were done after each of the surveys to determine reliability and validity of the instrument. Missing value analysis was performed to find out the percent of missing values and to analyze the missing patterns in the responses which helped to identify the appropriate imputation method to use to fill in missing values.
Descriptive statistical analysis was done to check for any outliers and to find out if the data was normal or not. Correlations between the items were looked at to identify any predictive relationships and the directionality of relationships between items in the survey. Principal Component Analysis (PCA) was done to determine the number of factors to retain and to find the correlation between the factors. PCA was conducted after each survey for all of the sub-scales to check if the validity of the scales changed over time. Cronbach's alpha was looked at to measure the internal consistency of the scales, where the closer the coefficient is to 1, the more reliable the scale.
Multivariate analysis of variance (MANOVA) was used to find out whether there were mean differences among groups (work groups, supervisory level, age…) due to a combination of factors. The hypotheses framed in the survey were analyzed to see if they vary over time, and the analysis results were also used to see how specific workgroups progressed over time through stages of change. All of the survey results were compared to the medical center records of systems improvement initiatives that have occurred in those workgroups, such as improvement methodology trainings, improvement projects or other major initiatives.
The survey results were reported to the medical center management and employees at various events after each survey completion. The research team, as members of the medical center Systems Redesign Advisory Council, helped the systems redesign department to develop the optimal conditions for change in the organization by providing stage-matched interventions that reduced resistance and increased participation in process improvement activities.

REVIEW OF LITERATURE
There are several process improvement methodologies defined in the literature to improve products, processes and services by using a set of tools and techniques (Ozcan, 2009)  Engineering. These improvement methodologies help understand processes and align them with customer needs with the ultimate aims of improving quality or reducing costs. Many businesses across various industries have significantly improved through the use of one or more improvement methodologies. The efforts put forth by industries improvement techniques goes to waste unless the initiatives are recognized and adopted by all levels of employees, thus creating a change in the organizational culture. There is a need to measure the cultural change that is happening in the organization to reassess the efforts put on implementing improvement methodologies.

Lean Methodology and Culture of Continuous Improvement
Lean methodology is built on a set of principles and structures which were first demonstrated by Toyota who popularized their Toyota Production System (TPS) (Ohno, 1998). The basic concept of lean is to maximize customer value by minimizing waste in the processes and using fewer resources. Lean tries to reduce costs, defects, inventory, space, and lead times and also attempts to increase productivity, customer satisfaction, profit, capacity and quality. The five principles of lean, as defined by Womack and Jones (1996) are Value, Value Stream, Flow, Pull, and Perfection. These principles can be put into action through a variety of tools and methods. The principles and tools of lean can be arranged into the "house of lean" or "Toyota house" which is shown in figure 2.2.1, as depicted by Liker (2004). The "roof" of the house represents the goals of the system, which included quality, cost, delivery, safety, and morale. The first principle of lean, value, could also be shown in the roof of the house, and is actually a principle of customer focus, or customer defined value. The house has a "foundation" of corporate philosophy with associated vision and mission, as well as stability and standardization in work processes. The two "pillars of lean" have to do with "flow" and "quality," respectively. Finally, residing inside the house are people or employees in the organization, working in teams towards a culture of continuous improvement and reduction of waste in the system. Lean helps identify the underlying problems in the organization and creates a way for improvement. The success of lean implementation depends on the readiness of the organization which includes support from the high level management and willingness to change among front line employees. Lean is often viewed as a set of tools and procedures, which can cause many organizations to fail in successful implementation of lean methodology. Creating a culture of continuous improvement is essential, apart from implementing tools and processes for making improvements (Detert and Schroeder, 2000). A culture of continuous improvement is defined as the effort to make incremental improvements to processes and services that define an organization and sustain them. According to Latta (2009) change in organizations occurs through different ways like strategic change and process changes. The success of creating a culture of continuous improvement lies in employee motivation and commitment (Womack, Jones and Roos, 1990). Successful lean implementation can change working habits and the work environment which may influence the belief, values, and working practices of the employees (Chatman and Flynn, 2001). According to Lukas et al. (2007), impetus to transform, leadership commitment to quality, improvement initiatives that actively engage staff, alignment to achieve consistency of organization wide goals with resource allocation and actions at all levels of the organization, and integration to bridge traditional intra-organizational boundaries between individual components are important for an organization's success in moving towards sustained, highly reliable, evidence based improvements.
It is relatively easy to change the way things are done, but sustaining them and integrating it into a culture is more challenging. Behavior change should happen to the individual employee, and those employees contribute to the change at the organizational level (Barker and Barker, 1996). According to Spiker and Lesser (1995), employee resistance is one of the main reasons why many organizations fail to sustain cultural changes. In order to change the culture, organizations need to identify why employees do things in their particular way, and understand how this affects organizational culture, so that new practices can be sustained.

Process Improvement Methodologies in Healthcare
Over the past decade, medical care costs have increased at a disturbing and unwarranted rate (Gawande, 2009;Zhang et al., 2009;Wellman, 2011 think, and feel in relation to those problems" (Schein, 2004). Schein also said, an 'organization's culture will also define what actions are taken in reaction to various situations' (Schein, 2004). Organizational change can also be described as numerous individuals undergoing a similar change process during the same period of time.
Organizations are an amalgam of various employee demographics such as age, length of service, and education level, with several management levels. Organizations' culture depends on its employees and the success of any new intervention depends on employee readiness to accept the intervention and adopt it (Armstrong, Reyburn and Jones, 1996). According to Armstrong et al. (1996) supervisory and non-supervisory staff members express more negative attitudes towards change than their managers and executives. Studies on employee burnout and their performance show that older employees and employees who are in their jobs for more time experience less burnout (Brewer and Shapard, 2004) and steer less towards change (Edelwich and Brodsky, 1980

Organizational Change Models
There are a number of organizational change models in the literature-Lewin's  (Levesque, Prochaska and Prochaska, 1999). A brief history of the Transtheoretical model and its core constructs are explained in detail in the next section.

Transtheoretical Model of Change (TTM)
The Transtheoretical Model of change (TTM) (Prochaska and DiClemente, 1983) is used to measure change in organizations' culture due to continuous improvement initiatives. This model has been used in research from over 20 years to measure the effectiveness of interventions (Levesque, et al., 2001) with its application mostly to behavior change studies (Pendlebury, 1996). The model was originally applied to individuals' health behavior change; it has also been successfully applied to organizational behavior change (Levesque, Prochaska, and Prochaska, 1999;. TTM has even been previously used in healthcare settings to study the readiness of physicians for continuous quality improvement, or CQI (Levesque, et al., 2001). The basic theory behind TTM is that organizational and individual change occurs in stages over time.
The four theoretical concepts that were defined in the model as essential to change are 1) Stage of Change -Intention to take action 2) Decisional Balance -Pros and cons of changing 3) Self-efficacy -Confidence to make and sustain changes 4) Process of Changeten cognitive, affective, and behavioral activities that facilitate change (Prochaska and DiClemente, 1983).

Stage of change
The TTM understands change as progress over time, and that people, or organizations, move through a series of five stages when adapting new behaviors. The change process is not linear, but is fluid, and individuals can revert back to earlier stages before attaining permanent behavior change (Prochaska and DiClemente, 1986). The stages of change are defined as: 1) Pre-contemplation stage -not intending to take action within the next 6 months 2) Contemplation stage -intending to take action within the next 6 months 3) Preparation stage -ready to take action 4) Action stage -explicitly engaged in new behavior 5) Maintenance stage -sustaining the changes for at least 6 months.

Decisional Balance
Change requires the consideration of associated pros and cons. Studies have shown that a decisional balance inventory with two scales relating to the Pros and Cons of change is the best available predictor of future change (Velicer, et al., 1985).
In the change process the balance of pros and cons systematically relates to stages of change (Prochaska, et al., 1994).

Self-efficacy
There are two components in this concept of behavior change -confidence to make and sustain changes and temptation to revert back to earlier stages. Levels of self-efficacy change when people, or organizations, move through various stages of change. People or organizations experience greater confidence to change in the later stages.
Processes of change Prochaska et al. (1982) derived a set of 10 fundamental processes by which people change using a comparative study of 24 major systems of psychotherapy. The set was refined following further theoretical analyses and empirical studies (Prochaska and DiClemente, 1983). The 10 processes are consciousness raising, dramatic relief, self-reevaluation, environmental reevaluation, social liberation, self-liberation, helping relationships, reinforcement management, stimulus control and counter conditioning.
These 10 processes were originally defined for individuals, but were adapted for assessment of organizational-level processes of change in the adoption of continuous quality improvement in healthcare (Levesque, et al., 2001). The definitions of the organizational-level processes of change for culture of continuous improvement shown in Table 2  Redesign office received a grant called an Improvement Capability Grant with the goal of "Developing a Culture of Continuous Improvement" and has the following stated aim, "The Medical Center will clarify and communicate a deep commitment to continuous improvement, expand improvement capabilities, apply the most effective methods available and make improvement an integral part of everyday work for all staff within three years."(Appendix B). As part of creating a culture of continuous improvement, the systems redesign office offered various improvement methodology trainings in lean, six-sigma, facilitation, etc. The systems redesign office also provides technical support for teams that want to work on process improvement initiatives.

Study Hypothesis
It is expected that the rate of adoption and implementation rates of new methods for systems improvement will vary between different groups. This could include different departments or workgroups, different demographic groups, different healthcare settings, and different industries, as described below. 2) Different demographics of employees including age, length of service, and supervisory level. For example, employees who have been with an organization longer or who are older or who have different responsibilities in the system will respond differently to change initiatives.
3) Different types of healthcare settings such as large or small hospitals, publicly or privately funded hospitals, or hospitals versus medical clinics, physician offices, independent labs, same day surgery centers, urgent care centers, etc.
4) Different types of work settings, such as healthcare versus manufacturing or service or transportation companies.
In the present study, levels 1 and 2 are studied at Providence VA Medical Center. Levels 3 and 4 described above cannot be studied at a single facility, but contributes to longer-term research involving multiple facilities and settings. The specific hypotheses that were tested in this research are given below.

Hypothesis 1
Null hypothesis (H 0 ): The supervisory role of the employee does not impact the adoption rate of process improvement initiatives.

Alternate hypothesis (H 1 ):
The supervisory role of the employee impacts the adoption rate of process improvement initiatives.

Hypothesis 2
Null hypothesis (H 0 ): The length of service of the employee at an organization does not impact the adoption rate of process improvement initiatives.

Alternate hypothesis (H 1 ):
The length of service of the employee at an organization impacts the adoption rate of process improvement initiatives.

Hypothesis 3
Null hypothesis (H 0 ): The age of the employee does not impact the adoption rate of process improvement initiatives.
Alternate hypothesis (H 1 ): The age of the employee impacts the adoption rate of process improvement initiatives.

Hypothesis 4:
Null hypothesis (H 0 ): Employee work group does not impact the adoption rate of process improvement initiatives.
Alternate hypothesis (H 1 ): Employee work group impacts the adoption rate of process improvement initiatives.

Hypothesis 5:
Null hypothesis (H 0 ): Employees who have greater exposure to training will not be more positive about the culture of CI compared to employees who do not have training.
Alternate hypothesis (H 1 ): Employees who have greater exposure to training will be more positive about the culture of CI compared to employees who do not training. In the studies (smoking cessation, alcohol cessation, exercise studies, physician quality improvement study) that used TTM in healthcare a six month time period was selected to classify each stage.

Instrument Development
In this research, the time dimension of six months was selected and the employee is said to have developed a culture of continuous improvement when they have been involved in improvement activities for more than six months without reverting back to old habits. The stage of change dimension has been asked in two 1. In this workgroup, there is time to reflect on how well our processes work for providing patient care.
2. This workgroup actively uses data to support quality improvement activities.

My immediate supervisor(s) establish(es) forums for and provide(s) time and
resources for participating in quality improvement activities.
4. Employees in this workgroup receive training in quality improvement.
5. In this workgroup, people value the work of quality improvement teams. level. The demographics were consciously placed at the beginning of the survey so that even the partial survey responses can be used in the hypotheses analysis.

Survey Administration
The research survey has been approved by the Institutional Review Board (IRB) at University of Rhode Island and Providence VA Medical Center. The survey disclosure form that was approved by both IRB boards is attached in Appendix D. As part of this study, the survey will be administered twice each year (fall and spring) from 2011-2013 for a total of five times and the time plan is attached is Appendix A.
The survey will be administered through Survey Monkey, which is a private company that enables users to create their own web-based surveys. The responses collected from the surveys will be anonymous and Survey Monkey allows various user settings that can protect the identity of a respondent. The web link with the survey was sent to all employees to their work e-mail address. Paper copies were also made available at department offices and meetings if respondents preferred this format. The research team worked with the office of the director at PVAMC to send survey links and reminder e-mails to all employees.

Survey Analysis
This section provides a summary of the statistical methods used to analyze the data collected from the surveys. This method is also suggested for instrument development and when factor analysis has to be done (Schafer and Olsen, 1998 , 1986). After the number of components to retain was decided, factor loadings were analyzed and items that loaded on more than one factor or loadings less than 0.4 or items that do not load on any factor were removed from the scale (Redding et al., 2006). The analysis for number of retained components is repeated until all the retained items load perfectly on the number of factors retained.
After any item removal, the process of PCA and item analysis was repeated to assess the new distribution of variance until there are least three items with significant loadings on each retained component and the rotated factor pattern shows a simple pattern. Correlations are run on items and components to check that none of the components are collinear with each other. Additionally, the internal consistency reliability of each factor was reexamined using Cronbach's coefficient Alpha.
Validity of the instrument: Validity determines the degree to which the research instrument truly measures that which it was intended to measure (Carmines and Zeller, 1979). In order to assess the external validity of the decisional balance, self-efficacy scales they were assessed across stage of change to examine the functional relationships. Also, as the validity of All Employee Survey and the quality improvement survey were already established, the items that were picked to be used in the current instrument were tested against the items from those survey results. The results from the PCA will be used to examine the construct validity of the instrument which determines if the items are grouped together in the manner intended. If the items that measure the same factor show strong correlation then the instrument is said to have high validity.
Analysis of Variance (ANOVA): ANOVA's and MANOVA's will be conducted to measure how the demographics affect the items in the scale or sub-scales. ANOVA will be used to test whether there are mean differences among groups (work groups, supervisory level, age…) due to a combination of factors. If the ANOVA's between groups are significant, post hoc Tukey's test will be conducted to determine which groups differ significantly from each other. Significance level of 0.05 was considered to accept the null hypothesis or not.

CHAPTER 4 ANALYSIS
This chapter discusses about how the data analysis is carried out after each of the surveys. Firstly, data is cleaned up to delete non-conforming responses and examined for missing data which is replaced using the appropriate imputation method.
Descriptive statistics are examined to find out normality of the data and identify any outliers. Cronbach's alpha were run to check the reliability of the scales. Principal component analysis was run to find out the factor structure of the scales. MANOVA analysis was done to find out the external validity of the scales.

Treating missing values
After the survey responses were received, the data was examined and any respondents that did not answer beyond demographics were deleted. Univariate statistics were run to examine outliers, missing values and normality of the data. EM algorithm method was chosen based on Little's test between respondents with missing data and without missing data. If the null hypothesis is rejected in Little's test, we can say data is missing completely at random (MCAR) and if null hypothesis accepted the data is missing at random (MAR). The data is checked for any outliers that are +/-3 from its mean value. The normality of the data is tested based on the skewness and kurtosis values of the items. The same steps were followed each time the survey was administered.

Descriptive Statistics
The

Exploratory Factor Analysis -Principal Component Analysis
Decisional

FINDINGS
This chapter includes results from the hypothesis testing that was described in the methodology section. The scales for stage of change, self-efficacy, and pros and cons are tested to see how they vary with supervisory level, age of employee, length of service of employee, current work group in which they work and the amount of training received.

Hypothesis 1
In order to test hypothesis 1, that employees in a supervisory role adopt process improvement initiatives earlier than employees who do not have any supervisory role, ANOVA's are conducted to check if SOC, self-efficacy and decisional balance scales are different between different supervisory levels.

Hypothesis 3
To test hypothesis 3, which is that employees in different age groups adopt process improvement initiatives differently, ANOVA's are done to check if SOC, selfefficacy and decisional balance scales are different between employees in different age groups. To test the hypothesis the survey responses are classified based on age into two categories-employees who are less than 50 years old and employees who are more than 50 years old.                                          This means that employees are more influenced by external factors to continue to be involved in improvement initiatives than their self-confidence. This can be due to a lot of factors like immediate supervisor or co-worker(s) support, inadequate training, or failure to assess the personal benefits of being a participant.

Stage of Change
The stage of change measure for assessing cultural change in the healthcare organization was based on the traditional individual behavior application of TTM using 6 months as the timeframe between stages. The SOC responses plotted followed a bath-tub pattern in all of the surveys with the majority of the respondents categorizing themselves as in either the pre-contemplation or maintenance stages. The overall shift was positive between stages as time progressed but the percentage of respondents in the precontemplation and action stages was lowered as time progressed. Management should take action to not lose employees who said they want to be involved in improvement activities by providing the right kind of motivation and finding strategies to sustain the employees who were already involved in improvement initiatives. This can be done by continuously promoting improvement methodologies, providing dedicated time to get involved in improvement initiatives and recognizing teams that were successful.

Self-Efficacy
Self-efficacy for readiness to get involved in process improvement activities showed an opposite pattern. The variance between stages of change of getting involved in process improvement initiatives accounted for variability of between 7% and 13% for pros and between 1% and 4% for cons, which is consistent with previous TTM studies (Velicer et al., 1999) and supports the external validity of the decisional balance scale.
Overall, the mean of pros and cons slightly reduced with time while the mean difference between pros and cons stayed the same at all time points.

Hypothesis 1
As hypothesized, employees in a supervisory role are more inclined to adopt process improvement initiatives than employees without any suoervisory control.
ANOVA's on stage of change and self-efficacy by supervisory level showed significant difference on employees with no supervisory control compared to their managers and executives. This shows that employees who have supervisory control have more confidence to adopt new methodologies as they will have easy access to tranings and new information with less hierarchical process to get approval for involvement. The perception of pros and cons have not changed significantly between employees with different supervisory control and also showed similar pattern in all surveys.

Hypothesis 2
Hypothesis 2 is that employees with longer length of service at the organization are less inclined to adopt process improvement initiatives than employees with shorter length of service. ANOVA's on stage of change and self-efficacy by length of service showed a significant difference for employees with longer lengths of service compared to employees with less service with means increasing with length of service. Though there is difference between employees based on their length of service, we reject the hypothesis. This could be due to employees who are new to the organization might not be aware of the available resources to be involved in trainings and projects and might be busy with learning how to get the day to day activities done. The perception of pros and cons have not changed significantly between employees with different lengths of service and showed similar pattern in all five surveys.

Hypothesis 3
Hypothesis 3 is that employees who are older in age are less inclined towards adopting process improvement initiatives compared to younger employees. The ANOVA's conducted for employee groups who are less than 50 years and more than 50 years on stage of change, pros and cons by age are not significant, showing that there is no difference between employees age groups. Self-efficacy showed significant difference between the two age groups and employees who are older than 50 years showed much more confidence to participate in improvement initiatives compared to the other group. Analyze survey data and report to management July 2011 Risks or discomfort, and decision to quit at any time: There is not any foreseeable risk or discomfort associated with the study. The decision to take part in this study is entirely voluntary and your employer will not know what you decide. Your responses will not be reported with your name or any identifying information other than your workgroup code.
Combinations of demographic groups with less than 10 employees will not be identified.
You may skip any question. If you decide to take part in the study, you may quit at any time.
Benefits of this study: Although there is no direct benefit to you for taking part in this study, the researcher may learn more about the ways that different hospital departments implement system redesign and problems that can occur. Thus, the research findings will benefit the hospital in general and may help to improve processes and patient care.
Confidentiality: Your participation in this study is confidential. None of the information will identify you by name. The researchers will not be able to access your email or IP address in Survey Monkey. You are encouraged to read the privacy agreement of Survey Monkey before participating. Data will be analyzed and kept on password protected computers in locked offices at the University of Rhode Island and in restricted folders at Providence VA Medical Center that are only accessible to the project investigators. Data will only be reported in aggregate, and any groups with less than 10 respondents will not be reported.