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The aim is to conduct systematic research to determine potential patterns, limitations, and measures to maximize positive results in relation to change management in a healthcare setting. The methodology that will be used for the research will be a systematic analysis of secondary data from statistics and findings retrieved from scholarly and peer-reviewed journals, case studies, dissertations, government healthcare reports, reputable academic publications, scholarly quality improvement initiatives, and other reputable repositories on factors such as workload, safety culture, training and education, leadership, organizational culture, and organizational culture impact the change process (Cleary et al., 2019; Le-Dao et al., 2020). Traits of effective change management leaders will include strategic thinking, effective communication, ability to inspire others, emotional intelligence, decisiveness, and empathy (Taylor et al., 2015). Interdisciplinary teams will comprise individuals who interact interdependently, dynamically, and adaptively toward achieving a shared and valued mission or goal for quality improvement as stipulated under the ADKAR Model of change management (Carman et al., 2019). The ADKAR Model applies here as it describes the sequence of the successful change process. It was developed after studying over 700 organizations and is used by thousands of change leaders worldwide (Yancy, 2021; Mislan et al., 2021; Taylor et al., 2015). The findings will focus on metrics such as faster time scales, fewer customer complaints, lower healthcare costs, lower employee absenteeism, higher retention, and improved healthcare outcomes among the patients to achieve safety, efficiency, effectiveness, patient-centeredness, timeliness, and accessibility to healthcare.
A secondary analysis of empirical data from public health departments, state health departments, hospitals, non-profit organizations, and healthcare facilities will be performed. The systematic review methodology implied the use of existing data to find answers to research questions that are different from the questions asked during the original research (Long-Sutehall et al., 2011). The analysis will be conducted to pursue the interest of the present paper. However, the study that will be included in the systematic review will offer a broader examination of the topic as change management is a complex subject that requires an in-depth investigation. Nonetheless, the techniques applied during the search stage will not limit the inclusion of articles highlighting the topic from various perspectives. For example, a peer-reviewed paper on change management in an area different than healthcare will be considered as it will be a piece of additional evidence for implementing or disregarding various organizational techniques. Using the systematic review methodology will make use of the primary datasets that are readily available for secondary analysis (Nilsen et al., 2020; Long-Sutehall et al., 2011). Furthermore, a secondary analysis of empirical data is the most efficient choice since it is more convenient and time-effective considering the few variables under study.
Data collection in the secondary analysis of the research methodology is cost-effective since data can be collected from multiple reputable sources freely using digital technologies without leaving the office. The search will be primarily contacted on the Google Scholar database as it will facilitate access to numerous reliable sources. Journals such as the Journal of Health Organization and Management, Health Services Management Research, and similar peer-reviewed scientific platforms will be considered during the search process. Moreover, statistical data will be collected from the following platforms: Data.gov, Statista, Figshare, Re3, and The U.S. Bureau of Labor Statistics. Metrix such as patient satisfaction, employee satisfaction, key performance indicators, performance improvements, change readiness, and employee engagement will be considered during the search to determine the most effective measures.
Examining pre-existing statistical data was the best choice because data collection has already been collected by other researchers. This will help to save both time and money that the researcher could have spent on collecting data. For instance, steps such as going through IRB approval, developing a data collection method, and implementing it are eliminated. Another advantage of manipulating pre-existing statistical data is that the pre-existing secondary data is freely available, publicly available, and reliable. This means that different data sets can be used by conducting a meta-analysis on the topic. Secondary analysis will help explore the matter to establish how various top-performing hospitals are successfully implementing change in management practices, aligning perfectly with the purpose statement and the problem statement.
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