Example Of Reducing Costs And Improving Quality Of Healthcare Dissertation Abstract

Abstract

The current government has consistently been trying to reduce the costs of healthcare and at the same time improve its quality. This paper focuses on the current state of healthcare through an extensive research on the population. The research uses a cross sectional design to collect data and come up with relevant conclusions. The research project aimed at using a homogenous population to come up with accurate results. Such a population involves participants who have the same knowledge about the subject. Such participants are easily obtained through a referral process where one participant refers another. A total of 50 participants have been interviewed through the use of questionnaires and one on one interviews. Since the research aims at community hospitals where the quality of healthcare is regarded the lowest, people who frequent these hospitals were the main target. The study implements mixed methods theoretical framework. Here, both the quantitative and the qualitative methods of inquiry are put into play. This method ensures that the strengths in one method are replaced by the weaknesses in another.
While qualitative approaches are important in measuring the known phenomena, quantitative approaches measure the unknown, thus combining the two provides the best results for the research. The results of the research have been processed through three processes of collection, analysis and recording of the data received from the population. This paper documents the processes involved in collecting the data put down in this paper as well as the results of the research. The researcher takes into consideration the ethical principles, which govern research such as the anonymity of respondents.

Sarah Helm

MBA/HM, Western Governors University, 2012
BSHS/M, University of Phoenix, 2009
Doctoral Study Submitted in Partial Fulfillment

List of Tables iv

List of Figures v
Section 1: Foundation of the Study 1
Background of the Problem 1
Problem Statement 2
Purpose Statement 3
Nature of the Study 4
Research Question (Quantitative Only) 4
Hypotheses (Quantitative/Mixed Method Only) 5
Theoretical or Conceptual Framework 5
Operational Definitions 7
Assumptions, Limitations, and Delimitations 8
Assumptions 8
Limitations 9
Delimitations 9
Significance of the Study 9
A Review of the Professional and Academic Literature 10
Transition 11
Section 2: The Project 12
Purpose Statement 12
Role of the Researcher 13
Participants 16
Research Method and Design 16
Research Method 16
Research Design 16
Population and Sampling 17
Ethical Research 19
Data Collection Instruments 21
Data Collection Technique 22
Data Organization Technique 23
Data Analysis 24
Study Validity (Quantitative Only) 26
Transition and Summary 34
Section 3: Application to Professional Practice and Implications for Change
Introduction
Presentation of the Findings (Quantitative Only)
Applications to Professional Practice
Implications for Social Change
Recommendations for Action
Recommendations for Further Research
Reflections
Summary and Study Conclusions

References 36

Appendix A: Concent Form 37
List of Tables
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Note that tables are numbered in sequence beginning with 1. Do not number tables according to the section in which they appear (Table 4-1, 5-2, etc.) In the List of Tables, a period, not a colon, or a dash follows table numbers. This applies to figure numbers in the List of Figures as well.
List of Figures
Figure 1. Figure caption, sentence case xx
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Alternately, follow the instructions in the Instructions for Using the Walden DBA Template, which will allow automatic updating of the List of Figures.
Section 1: Foundation of the Study
Background of the Problem
Various factors contribute to increased health care costs, which include inefficient use of medical advancement, improper payment system, the high cost of health care services, and failure to align the health care objectives of the facilities with those of national government. The rationale behind improving the sector is that it provides diverse opportunities for everyone to engage in healthy lifestyles. Scholars such as Frank-Lightfoot & Davis (2016) have linked effective control of the health care costs with better health outcomes. For instance, in the U.S, the health care spending has increased rapidly over the past century, yet the quality of care offered is still low. The issue has attracted concerns from different scholars, leading to the development of various policy issues such as the Affordable Care Act to manage the high costs. Various studies attribute the rise in the cost of the demographic changes among other significant issues. The health spending is increasing at a higher rate than the economy can sustain, which if not adequately managed will harm the economy (Venkataraman, 2015).
Productivity growth in the sector achieved through enhancing the quality of care offered at the facilities. The process will include the introduction of new procedures and practices, which affect positively towards increased rate of survival, low morbidity, reduced pain among other significant contributions (Cox et al., 2015). Therefore, the desired productivity growth will take account of both cost and health outcomes. According to Coddington & Moore (2012), management of the health care costs will contribute towards efficient utilization of the limited healthcare resources in the most equitable way possible. Therefore, the policy planner should consider managing both cost and quality of healthcare that provided at the respective facilities. Conducting more research in the study area will help stakeholders to come up with practical strategies for controlling the escalating costs as well as managing quality care at the facilities. Having exhausted on the background to study topic, the paper now focuses more on the problem statement.
Problem Statement
Spending in the healthcare sector has grown substantially fast in the past few years (McClellan & Rivlin, 2014). Most countries have experienced a substantial growth in health care expenditures, but the expenditures in the United States are particularly high. Currently, the healthcare industry accounts for more than 17 percent of the Gross Domestic Product (“Centers for Disease Control,” 2013). Medical practitioners, among other stakeholders, have strived for the best processes to ensure quality patient care. However, such efforts have had little impact on the ground, as many care-seekers still lack quality services as they come in and out of our healthcare facilities (Lehmann, Ammenwerth & Nohr, 2013).
The general business problem is that expenditures in the healthcare sector are ballooning; at this rate of increase should be monitored and controlled. As a basic want, the ideal is patient care served with quality standards, as per the expectation of the human race of this century. The specific business problem is that collaborative strategies involving all stakeholders need adoption, in order to reduce the high expenditures while at the same time improving the quality of services offered (Shi & Singh, 2014).
Purpose Statement
The purpose of this case study is to determine ways through which the community hospitals in Las Vegas, Nevada can provide quality acute care to ICU patients while reducing costs. The purpose of this case study was to provide a quantitative evaluation of the qualitative studies. It anticipated that a better understanding of the patient needs and cost considerations is necessary for determining the issues and challenges faced by hospitals and the caregivers (Melnyk & Fineout-Overholt, 2012).
Consequently, the case study will consider the use of mixed methods of data collection. The findings will make it possible to determine the effects of patient factors, such as diagnosis, age, and acuity levels, on the costs required and the quality of care. Conversely, the study will make it possible to determine whether organizational structures or processes related to the aspect of staffing influence patient outcomes (Hinshaw & Grady, 2011).
Containing costs while providing quality acute care depends on the staff-patient ratios in place, as well as the coordinating processes. These considerations are vital for the provision of high-quality critical care to the community while simultaneously reducing costs. The finances saved used to fund other development projects in the community such as sponsoring people to pursue higher education (Gabow & Goodman, 2014). Concerns about the adequacy of quality acute care provision emanate from some of the cost-cutting strategies adopted by managed care (Huston, 2014).
Nature of the Study
This paper will make use of mixed methods of data collection. There is a growing awareness in the benefits of merging qualitative and quantitative methods to health research. Mixed methods research is also fitting when assessing complex interventions in healthcare. A mixed methods approach yields a more comprehensive analysis of data that use of qualitative or quantitative methodologies alone does not. The logic for utilizing mixed methods based on the assumption that the weaknesses in one method offset by the strengths of the other (Farquhar, et.al, 2011). Furthermore, there is evidence that mixed methods research is also increasing in popularity in accounting research, as it offers opportunities for both theory building and theory testing (Grafton, et.al, 2011).
Research Question
The independent variable in this research is the amount of cost reduction (a quantitative variable), while the dependent variable is the quality of care provided by community hospitals, a qualitative variable.
Null Hypotheses:
Community hospitals within the Nevada Health System will be unable to provide quality care while simultaneously reducing consumer costs.
Cost containment will not lead to improved customer satisfaction and engagement.
Alternative Hypothesis:
Community hospitals within the Nevada Health System will be able to provide quality care while simultaneously reducing consumer costs.
Cost containment will lead to improved customer satisfaction and engagement.
Hypotheses (Quantitative/Mixed Method Only)
Two major elements in the research design are the hypotheses and the variables used to test them. A hypothesis is a provisional idea whose merit deserves further evaluation. Two hypotheses, the null (H0) and alternative (H1), are to be stated for the research question or research subquestions. For examples of correlation and quasi-experimental null and alternative hypotheses, consult the DBA Doctoral Study Rubric and Research Handbook.
Theoretical or Conceptual Framework
This study will implement a mixed-methods theoretical framework, which will combine both qualitative and quantitative methods of inquiry. The strength of a mixed methods approached is based on the assumption that weaknesses in one method will be compensated for by strengths in the other. A mixed-methods approach to research has been growing in acceptance and popularity in the health sciences (Evans, Coon, & Ume, 2012). Quantitative methods are most useful for measuring the frequency of “known” phenomena and correlative patterns, including inferences of causal relationships; in this research, quantitative methods used to determine which medical practices and procedures have the highest associated overhead costs, and which of these will subsequently afford the greatest opportunity for cost reduction. On the other hand, qualitative methods allow researchers to discover correlations that were previously unknown and are better at explaining how and why such correlations occur (Pasick et al., 2009).
Greene, Caracelli, and Graham (1989) identify five different components of a mixed-methods evaluation model. These include triangulation, a quantitative method that measures the distances between disparate data points; complementarity, which focuses on the amount of overlap between different, sets of phenomena; development; initiation; and expansion, which involves the process of extending the research to other areas of interest.
The data collections process in a mixed-methods approach to research and evaluation involves to strategies: one for the collection of qualitative data, and one for the collection of quantitative data. After the data has been collected and is ready for analysis and evaluation, it may be necessary to integrate the data through a transformative process. Tashakkori and Teddlie (1998) describe the process of converting codified qualitative data into quantitative data as quantization; alternatively, the process in which quantitative data transformed into qualitative data known as quantization.
An emergent method design recommended for the theoretical framework of this research. This will allow researchers to determine the appropriate methods over the course of the process of research, rather than adhering to a research schedule that was predetermined at the outset of the research. This will also allow the researchers to make design decisions, including those regarding sampling distribution and recruitment, as the research progresses (Evans et al., 2009). In addition, a sequential design may be appropriate for the research, so that data collected in one phase of the research process may contribute to the collection of data in following processes.
A mixed-methods theoretical framework is most appropriate for this type of research in that it can address recurring patterns, both in terms of temporality (which addresses the time in which related phenomena take place) and in terms of the concurrent development of related themes. A mixed-methods approach will provide an orderly and effective scheme for uniting qualitative and quantitative observations and facts, which will address both what, is occurring and why it occurs (Polit & Beck, 2004).
Operational Definitions
Acute care
Hospitals Today (2000) define acute care as a level of healthcare provision where a client is treated for emergency issues concerning severe incidents of illness.
Cost reduction
Cost reduction refers to the procedure employed by organizations to minimize expenses and boost profits (Bragg, 2010).
Healthcare sector
According to WHO (2015), health sector constitute of the institutions, personnel and resources set up or integrated into compliance with determined policies that key purpose is to encourage, reinstate and preserve health.
Mixed methods research
The methodology for the study is a mixed method. It involves the compilation of both qualitative and quantitative data and the integration of the potencies of each to answer study questions (Creswell, 2014).
Quantitative methods
According to Bless, Higson-Smith, and Sithole (2013), quantitative research conducted using a range of methods, which use measurement to record and investigate aspects of social reality.
Quality standards
According to NICE, quality standards refer to prioritized measures devised to drive quantifiable excellence improvements in an area of healthcare (National Institute for Health and Care Excellence, 2015).
Qualitative methods
According to Leedy and Ormrod (2013), qualitative research approach aims to discover and develop a deeper understanding of how certain things occur and why. Qualitative data helps the researcher to gain a deeper understanding of how things happen, people’s behavior and to attach meaning to things.
Assumptions, Limitations, and Delimitations
Assumptions
The assumptions based on the null hypothesis of the study.
Community hospitals within the Nevada Health System will be unable to provide quality care while simultaneously reducing consumer costs.
Cost containment will not lead to improved customer satisfaction and engagement.
Limitations
The research will depend on data collected through various techniques, as like any other research, limitations are time and financial constraints. Another limitation is an overreliance on primary data, since no literature review, the study will depend on data collected to conclude. It is always good to collect primary data for research, but secondary data needed for reliability and validity purposes.
The study relies on mixed-method research to collect data.
Limitations
The limitations of this method include:
• Mixed methodology is a very complex research design
• The methodology requires more resources and time to execute.
• It is hard to strategies and executes one technique by borrowing on the results of another such as quantitative research and qualitative findings.
• In most cases, it is challenging to determine discrepancies that occur in the analysis of the findings (Creswell, 2014).
Delimitations
The healthcare industry is vast, different sectors have different means of reducing costs, and improving quality. Therefore, the scope of this study is the measures the Nevada Health System uses to reduce costs and improve healthcare quality.
Significance of the Study
Reducing costs and improving quality in healthcare is an integral part of any healthcare institution. By studying how the Nevada Health System reduces costs and improves quality, other institutions can borrow the approaches and help to ensure patients get the best care. The aim is to provide a quantitative evaluation of the qualitative methods the institutions use. A better understanding of patient needs and cost considerations is necessary for determining the issues and challenges faced by hospitals and caregivers. Therefore, the study is pertinent for hospitals and caregivers since it will offer practical solutions collected either through questionnaires or through interviews.
A Review of the Professional and Academic Literature
This is the section where you will report the literature regarding your theoretical/conceptual framework. The approach to this section may vary by the specific purpose. For example, if your study will be grounded in transformational leadership theory, you are examining or exploring your phenomenon through a leadership lens. You want to report on extant research that was grounded in transformational leadership theory. You would want to report on the literature that is as close to your topic/phenomenon as possible. In addition, you will want to include the literature for any key variables, if you are conducting a quantitative study. Consult Appendix A in the DBA Doctoral Study Rubric and Research Handbook for an outline with minimum requirements for a quantitative study. In addition, follow the guidance in the Literature Review section of the DBA rubric in the handbook.
Critical analysis and synthesis of the literature will be an important piece of the review. The review of the literature is not to be a regurgitation of what you have read. It is also not to teach about a topic; rather, it is to show your mastery of the research on your topic and provide a comprehensive up-to-date literature review of your topic. Start with an introductory section and then report the literature. This should be an exhaustive review of the literature using the chosen theoretical/conceptual framework and consist of the key and recent writings in the field. Repeat this approach if you are using more than one theory or conceptual framework. In addition, there must be a critical analysis and synthesis for each variable in quantitative studies.
Transition
This section summarizes Section 1 and the gives an overview of the next two sections. Do not introduce any new material in the summary.
Section 2: The Project
Provide a one or two paragraph introduction to Section 2. This introduction should provide a clear outline of the Project section.
Purpose Statement
The purpose of this case study is to determine ways through which the community hospitals in Las Vegas, Nevada can provide quality acute care to ICU patients while reducing costs. The purpose of this case study was to provide a quantitative evaluation of the qualitative studies. It anticipated that a better understanding of the patient needs and cost considerations is necessary for determining the issues and challenges faced by hospitals and the caregivers (Melnyk & Fineout-Overholt, 2012).
Consequently, the case study will consider the use of mixed methods of data collection. The findings will make it possible to determine the effects of patient factors, such as diagnosis, age, and acuity levels, on the costs required and the quality of care. Conversely, the study will make it possible to determine whether organizational structures or processes related to the aspect of staffing influence patient outcomes (Hinshaw & Grady, 2011).
Containing costs while providing quality acute care depends on the staff-patient ratios in place, as well as the coordinating processes. These considerations are vital for the provision of high-quality critical care to the community while simultaneously reducing costs. The finances saved used to fund other development projects in the community such as sponsoring people to pursue higher education (Gabow & Goodman, 2014). Concerns about the adequacy of quality acute care provision emanate from some of the cost-cutting strategies adopted by managed care (Huston, 2014).
Role of the Researcher
The role taken by the researcher is conducting specific, observable behaviors using more structures techniques and involved in participants when collecting data related to ethnographic information using less structured techniques (Oshima & Emanuel, 2013). The researcher, however, does not interact with the subjects during data collection. The research can as well remain detached from the subjects and, therefore, uninfluenced by their view and ideas.
The researcher as an observer places limitations on the behavior and settings observed especially in natural circumstances. The researcher may not be in a position to collect supporting data by asking the subjects questions. As a result, he or she can fail to appreciate the perspectives of the subjects and at the subjects and understand the social meaning, which underpins their interaction. This is one reason this role is most often used in more structured observation, where the researchers are interested only in categorizing instances of observable behaviors.
While the Belmont Report does mention that the roles of researcher and practitioner may overlap, ultimately it regards the two roles as distinguishable based on two assumptions:1). The research is biomedical or behavioral in nature and involves some intervention that usually takes place in controlled settings (Merriam, & Tisdell, 2015). 2) Research has a testable hypothesis and results in generalizable findings. These assumptions reflect a positivist philosophy of research that is not inclusive of more naturalistic research methods.
The students will recognize and limit the potential for bias. Before any data collection, techniques designed; they will take into account issues or aspects that might affect the study design and results measured. The finding of a research study should be as free from bias as possible.
The focus group interview protocol developed to ensure that research questions addressed in the data collection phase of the study. The interview protocol serves as a guide for research during interview sessions (Nilsson et al., 2016). The protocol assists in ensuring that all groups asked the same question to elicit relevant responses. Additionally, the interview protocol allows the research to limit his voice during the sessions by only asking the protocol questions and follow-up prompts, which keeps the participant voice active during each focus group interview.
Participants
Screening ensures that participants meet eligibility criteria and understand what a participant in the study entails. Participants either can randomly allocated to treatment and control groups or assigned to different groups based on predetermined characteristics to create balanced or similar groups are the beginning of the study.
Very often, researchers gain access to their potential participants through a relationship with community leaders or stakeholders. Stakeholders may include formal and informal group leaders, service providers, business people, and residents who have an interest in the particular community.
The participants use strategies such as summarizing the discussion, reaching and ratifying agreements (Aponte‐Rivera et al., 2014), asking for clarification or explanations and keeping to the agendas. In contrast, the person in charge uses strategies such as assigning turns to speak, moving to the exit point on the agenda, or opening and closing particular phases.
All studies include research question, which are overreaching questions that represent the research goals. The research questions must be open-ended questions such as beginning with what or how (Elo et al., 2014). It is also important for the research question to align well with the study for participants to find easy time answering.
As the population of the world increases, a reduction in health care costs becomes a paramount significance. Within the last few decades, most researchers have attempted to come up with a way of reducing the costs of hospital systems that can assist in maintaining and even improving the quality of healthcare. Surgical units, for example, are a major section of hospitals cost containment for a different reason. One, the surgical units form part of the most costly and least applied units in a hospital. Second reason is that a patient who undergoes surgical operations forms a significant part of the demand for other hospital departments. For this reason, increased utilization of surgical units has extreme importance in meeting the increasing demand for the services of healthcare and reducing costs that assist in improving service quality. Thirdly, surgical units directly have effects on other operations of the hospital’s units such as management of resources, financial management, and purchasing accompanied with waiting times of the patients. Therefore, it has a high potential for reducing costs also in such areas.
Research Method
The study focused on qualitative data collection. In the qualitative data collection method, both primary and secondary data applied.
This data collection technique entails descriptions of characteristics and scenarios that the researcher intends to deduce from the chosen population. The researcher used information gathered from secondary sources as well as primary sources. The primary source used was interview questions administered on the employees working in the healthcare centers. Interview is an efficient way to gather information, speech combined with some non-verbal communication can be used to get what the person intends to express.
The study included use of open- ended questions; this makes it difficult to replicate the answers and allows the data analysis to be even more subjective (Chandra and Sharma, 2013). The interviews done by use of questionnaires fifty participants given the forms to fill in the data. The information collected and analyzed. The secondary data obtained from scholarly journals. This books and articles contained information from experiments and research done previously. Some of them had peer reviews meaning that experts in the healthcare industry had gone through them and verified the information they had. The reviews ensure that the data collected is valid, accurate, and relevant to the industry (Zou and Sunindijo, 2015).
Research Design
The study design chosen for this study was a qualitative interview study, in this study a cross sectional approach.
According to Macklin (2012), the approach is considerate when it comes to time, and the subjects get to answer the questions in the particular environment (Macklin, 2012). The researcher uses the subjects in a specified period to ensure all the data is collected and the environment of study manipulated.
Lack of manipulation makes the study relevant, as the researcher does not get the opportunity to influence people who targeted in the study to participate (Mackey and Gass, 2015). In comparison to the longitudinal study that the subjects studied over a long period, the approach was more convenient. Longitudinal study gives the study time to develop as it is observed but in this case, the topic of study being comparison of strategies that are already in place it is beneficial to use the cross sectional study. The study therefore based on the perspective of the employees in healthcare centers.
Data Analysis done after the data collected and all the questionnaires checked for completeness. The researcher was then went through all the answered questions and compiled the findings. Unlike in a quantitative study where the study depends mostly of the tool of data collection, the researcher is mostly the tool of analysis for the data collected from the subjects (Kumar, 2012). The challenge of analyzing the data is mainly in the validation of the data, as the researcher has to show evidence of the credibility of the work done.
Population and Sampling
Several factors that affect the size of the sample needed for an accurate measurement of data. The principle of saturation needs to guide the process. The saturation point in homogenous groups appears to be about 12 participants. Homogenous groups are those composed of people having the same level of the knowledge about the study. Saturation is important to obtain the proper results but, in many studies, the saturation point changes as the group changes. When this occurs, another group formed through the process of the study by breaking into other homogenous groups.
The primary sampling in this study comes from people referring other people. The referral process is helpful to gain a sampling that is homogenous in nature. In the emergent framework, the attempt to gain a homogenous sampling will become more difficult without a homogenous group to begin the process. Since the hypothesis relates to the costs of a community hospital, only those people who are aware of the cost structure of a hospital can offer an accurate assessment. Therefore, the addition of the chain sampling will permit a sharing of individuals. As one person who is familiar with hospital finance suggests another who is, also familiar the chain sampling procedure will continue. This sample then will be composed of a homogenous group.
In some studies, the number of participants is determined by the researcher’s time to conduct the study. The exact number of people required to make an adequate sample varies. It can vary as much as one to a hundred in some studies. Another opinion suggests a sample size of 30 to allow for the benefits of the Central limit Theorem. The bell shaped curve will begin to form a cure if the data is normally distributed. The sample size of 30 is a lower boundary for a large sample inference about the mean of the quantitative variable. Thirty is a suggested number of participants, suggested in the research.
Determinates of this study control the data saturation. A small study should reach saturation at a higher level than a larger study. Data saturation occurs when the replication of the data in another study is possible or when new information in obtained. There is not a specific number in this study that will verify or nullify the hypotheses but by using the guidelines, that data saturation reached and there is enough information to replicate the study, sufficient data saturation obtained.
A focus group of leading healthcare executives would lend itself to a starting point to secure participants in the study. Chain sampling after this would be to ask the participants to refer another set of focus group members or an individual member. This will increase the number of participants. For example, interviewing a small number of executives, allows them to express their individual views, and elicit a group discussion. This will help to gather a meeting of the homogenous group and ask for referrals to others. Rich descriptors provided from gathered data. The descriptors are useful to analyze the concepts, and put the material in a well-defined perspective .
Ethical Research
In research, it is important to respect the rights of an individual as they are participating in the research study. The use of the Informed Consent procedure will help to ensure that all participants are aware of the study parameters and uses. The participants agree that they are freely and voluntarily giving an informed consent. The researcher has the responsibility to explain clearly the nature of the research and how the results disseminated. Participants should have the right to refuse to participate without any ramifications from the researchers. The participants are to be reassured as to the confidentiality of the data gathered and their personal information, if any rendered. The ability of the participants to withdraw is also an option for the researchers. A sample of the Informed Consent document is available in Appendix A.
b. Withdrawal from study. For a variety of reasons, a participant may need to withdraw. In addition, the researcher may find that a participant is not part of the homogenous group that desired. When this happens, some conditions need to assess. The first is on the condition of the data that the interviewer has received any information from the subject. If the subject has participated to some point that, the researcher can use the data, the conclusion drawn that the participation was sufficient. The participant counted in the study.
If the situation is that the participant does not wish to be included then the data received from the participant needs to be withdrawn. Therefore, the circumstances of the withdrawal will precipitate the procedure.
c. Incentives for participating. In any research study, it is difficult to get participants to take the time to complete the study. It does require a commitment of their time. Some incentives offered to help ease the burden that the time commitment might place on them. In this case, however, the incentives are altruistic in that the results will help the community in the future to lower the health costs of the area. This may be incentive enough for the group of homogenous people that are involved.
d. Ethical Responsibilities. Society depends on statistical research to help in decision-making and to produce conclusions that are useful in society. In this study, the statistics developed from the results may in fact, have an impact on the hospital costs in the future. Because of the obvious importance of the study, it is necessary to ensure that ethical practices of the researchers followed. Professionalism by the researchers will be of the highest level to guarantee that each treated with total respect and comfort. The data received from the study guarded against the possibility of the researchers to come to a predetermined result. Data received from the study will not contain any personal information. The results of the study archived in a file away from the initial place of the study. The printed materials, including but not limited to the Informed Consent forms will be stored in a safe for five years to protect the identity of the participants.
Data Collection Instruments
Given that a quantitative study design has adopted for the current study, a rigid technique of online questionnaire and computer data collection from different programs used as one of the primary data collection instruments for its main purpose of quantification of data. Different programs used to export the data to SPSS, the most popular statistical quantitative software packages. This will help generate tabulated reports, charts, and plots of distributions and trends, as well as generate descriptive and more complex statistical analysis.
When it comes to the feature of validity, it is imperative to note that all the tools integrated into the process and practices of studying essential measures found to have an impact to the study (Salganik & Levy, 2015). Indeed, these are pertinent variables expected to provide a leeway of what expects to be a relative in ascertaining the quality, content, legal representative. This consideration is essential when it comes to summarizing everything found from the final data sets collected.
When it comes to achieving statistically significant results, an issue will enhance in the current study, as it is something that the researcher would appreciate realizing upon completing the study. Its reliable instruments include online questionnaire and computer data collection incorporates in the process of collecting primary data for the study. Evidently, with such level of reliability observed in the study results, there is a high chance of attaining better results as measures of validity (Palinkas, Horwitz, Green, Wisdom, Duan, & Hoagwood, 2015). This expects to be with key measures that objective sustaining the value of reliability throughout the study procedures and practices adopted.
Data Collection Technique
When it comes to data organization, it is noted that a variety techniques will be incorporated in ensuring all possible facets of data are appropriately organized and prepared for the analysis. One of them is the use of notebooks for recording anything not covered through the interview instrument. Therefore, it is part of the organization techniques to identify and select the observation technique. Such notes will be of great significance during the process of analyzing the data collected and forming conclusions and inferences as per the study topic, the research questions identified, and the possible research hypotheses developed (Putnam, Molton, Truitt, Smith, & Jensen, 2016).
Therefore, data analysis will be a summary of the collection offered by each research participant, hospital, and or physician group, hence, making it easy for undertaking a research analysis with the inclusion of a thematic analysis process. Making tables and graphs with trend lines is another relevant technique adopted in this study. The effectiveness is associated with the fact used in recording the researcher’s speculations, reactions, and concerns that relate to the study. As a result, it will make the study process to be enjoyable and reflective of what had been learned in class and, hence, improving on the accuracy of the data and other information that will be shared throughout the study process (Currow et al., 2015). A thematic analysis will also bring order to the findings extracted from the interview and hence add more significance and appropriateness to the study topic.
Data Organization Techniques
When it comes to data organization, it is noted that a variety techniques will be incorporated in ensuring all possible facets of data are appropriately organized and prepared for the analysis. One of them is the use of notebooks for recording anything not covered through the interview instrument. Therefore, it is part of the organization techniques to identify and select the observation technique. Such notes will be of great significance during the process of analyzing the data collected and forming conclusions and inferences as per the study topic, the research questions identified, and the possible research hypotheses developed (Putnam, Molton, Truitt, Smith, & Jensen, 2016).
Therefore, data analysis will be a summary of the collection offered by each research participant, hospital, and or physician group, hence, making it easy for undertaking a research analysis with the inclusion of a thematic analysis process. Making tables and graphs with trend lines is another relevant technique adopted in this study. The effectiveness is associated with the fact used in recording the researcher’s speculations, reactions, and concerns that relate to the study. As a result, it will make the study process to be enjoyable and reflective of what had been learned in class and, hence, improving on the accuracy of the data and other information that will be shared throughout the study process (Currow et al., 2015). A thematic analysis will also bring order to the findings extracted from the interview and hence add more significance and appropriateness to the study topic.
Data Analysis
The respondents will answer questionnaires on their own. Therefore, patients who have already recovered and gone home from the hospital will be the ones in the sample. It is likely that some questions will result in missing data. The respondents called using the telephone number that indicated on the form and interviewed about the questions. Resampling conducted to replace respondents who will be unreachable.
The data cleaned to remove outliers from the dataset. Unreasonable responses eliminated from the dataset. Random resampling used to replace the study participants’ data removed.
SPSS used to conduct data analysis. Data analysis conducted in two stages. The first stage will use descriptive statistics to create an overview of the demographic profile of the selected sample. In the second stage, inferential statistics applied to answer the research question.
The main study variables are the cost of provision of healthcare to ICU patients and the quality of the resultant healthcare. A multi-regression analysis will analyze the data. A t-test for regression coefficients used to answer the key study question. The cost of healthcare measured in US dollars. The data obtained from the initial community hospitals billing department. Information on the costs billed to each of the sampled respondents recorded. The quality of healthcare measured using a 10-point Likert Scale. Respondents given a statement to the effect “I received high-quality service.” They will then be required to select a multiple choice that correspondents to the extent of agreement or disagreement with the statement.
A multiple linear regression conducted that includes the identified extraneous variables as control variables. The obtained co-efficient for the quality of healthcare will then be tested at 5% significance level. If it is significant, then there is a relationship between the cost of healthcare provision to ICU patients and the quality of healthcare. The sign of the coefficient will allow us to assess the nature of the relationship (positive or negative).
Inferential statistics applied on the study sample to make conclusions about the population. Therefore, the sample assumed to represent the entire population. A random sample strategy used to obtain the study participants. The strategy is appropriate will ensure that the sample is representative (Emerson, 2015). Inferential statistics also assumes that the population is normally distributed. This study assumes that population is normally distributed since it is large.
The study will use a multiple linear regression. Therefore, the standard OLS assumptions will apply. The study assumes that the variance of the errors is constant (there is no heteroscedasticity). In other words, the variance of errors does not change as the intendent variable increases. A plot of residuals versus fitted will be used to test this assumption. The assumption met if the residuals data points do not form a unique pattern. If they form a unique pattern, then the assumption not met. The other assumption is that errors are normally distributed and have a mean of zero. A Q-Q plot used to assess this assumption. The assumption met if most of the data points are along the 45-degree line. If they steer away from the line, then the assumption violated.
If the assumptions violated, then an OLS specified model is not the best fit for the data. Therefore, an alternative model specification that best suits the data applied. AIC will be used to model specification that provides the most information given the dataset that is available. The other model specifications that tested include quadratic, log-log and log models.
Study Validity
As indicated above, the proposed research will be mixed method research to mitigate limitations of qualitative and quantitative research methodologies, while enjoy benefits of the both. Precisely, the key disadvantage of qualitative research concerns high subjectivity and descriptiveness of its findings achieved with a relatively small sample (Merriam & Tisdell, 2015). Contrary to qualitative methodology, quantitative research enables collection of factual evidence to the suggested cause-effect relationship between the defined variables, but fails to get an insight of the studied matter. As a combination of both methodologies, mixed method research allows obtaining an in-depth understanding of a given phenomenon, supporting it with numerical results (Creswell, 2014).
In the context of this inquiry aimed at investigating whether reduction of health care costs is likely to improve the quality of care delivered, mixed research methodology will be an asset to test the relationship between these two notions and to comprehend industry specifics through perceptions, experiences, and observations provided by healthcare personnel. In line with the pursued research goal, the project will be a non-experimental correlational study based on mixed research methodology. Hence, survey will be a method for data collection, while its complex questionnaire will be an instrument.
Most of the questionnaire content will be close-ended statements with predefined answers. A few open-ended questions in the end of the survey questionnaire will provide respondents with an opportunity to reflect their perceptions, attitudes, and ideas on the efficiency of reducing costs to enhance the quality of care. Since, the quantitative part of this research is the core one dedicated to measure the relationships between cost reduction and care quality improvement, considerations for study validity will focus on quantitative results. In fact, the overall questionnaire content will stem from a thorough review of prior research in the field that will indicate already validated and approved instruments of data collection in health care research.
In general, validity estimates the extent to which the selected theoretical framework and collected data supports the test to be measured. When speaking about quantitative research validity, scholars distinguish four types of validity, such as construct validity, internal validity, statistical conclusion validity, and external validity (Rasinger, 2013). Construct validity measures whether the defined variables or constructs represent what there meant to represent. Statistical evidence on health care expenditures in the United States indicate a continuing growth, while practitioner reports and scholarly findings urge the need for the industry’s reform because of an ongoing decrease in access to care services and the overall quality of care delivered. The problem is the most acute in intensive care, where timely and effective care is essential for further well-being or even life of a patient. Hence, costly intensive care services leave low-income population groups underserved.
On this ground, cost reduction seems an effective solution in improving availability and quality of care services in the country. In line with this argument, the researcher has defined cost reduction as an independent variable and quality of care received by intensive care unit (ICU) patients as a dependent one. By investigating the relationship between these two variables, the researcher is going to define whether cost-reduction initiatives are likely to improve the quality of care provided to ICU patients. Hence, the defined constructs fit the current research purpose, which indicates their capability of measuring the relationship between cost reduction and quality of care. Careful determination of research variables signifies that the proposed study has construct validity.
Internal validity estimates the extent to which a researcher is capable of asserting that an independent variable produces an effect on the dependent one. Threats to internal validity concerns those things that confound or confuse results achieved through data collection and analysis as well as overall research findings. When evaluating research results, it is essential to consider potential threats to internal validity that might have affected the overall research process, survey, and testing. The list of potential threats includes “history, maturating, testing, instrumentation, regression, ceiling and floor effects, attrition, selection, and the Hawthorne effect” (Perrin, 2014, p. 72).
History concerns an event occurred during the research process that produced an influence on participating individuals. Maturation refers to natural changes happened to study participants over time. Testing implies disparities observed from pre-test or post-test explained by participants’ becoming familiar with the test content. Instrumentation threat entails measurement of changes noted in respondent performance that are not associated with or credited to the executed intervention or treatment (Perrin, 2014). Regression is stated when performance of some participants drastically differs on pre-test and post-test procedures, such as well initial performance and poor post-test and vice versa. Ceiling and floor effects constitute two dimensions of a threat to internal validity, where the former refers to extremely well performance at both pre- and post-test procedures, which challenges detection of any changes, and the latter concerns cases when individual’s performance begins and remains low. Attrition concerns individuals who left the study during the process (Perrin, 2014).
Selection refers to heterogamous nature of the selected sample. Giving account for initial differences in participating individuals evokes a conclusion that research results are inconclusive being a product of differences rather than the executed intervention. Finally, the Hawthorne effect named after workers at the Western Electric Company, Hawthorne, IL, and refers to self-initiated improvement in performance due to the awareness of being watched (Perrin, 2014). Since the proposed study is cross-sectional, not longitudinal one, threats of history and maturation are inapplicable to this project. The non-experimental correlational nature of the planned inquiry indicates irrelevance of testing, instrumentation, regression, and ceiling and floor effects.
For this cross-sectional study, attrition will be inapplicable as well, since invited healthcare employees will either provide completed surveys or not. The researcher will execute analytical procedures, relying on the entire data set received. Withdrawal of some respondents from the research after the researcher distributes survey questionnaire will not influence data analysis or damage results. The treat of selection is not subject to this study as well, since the researcher will recruit participants on the ground of population targeting and sample selection. As already discussed above, focused on community hospitals in Las Vegas, NV, the proposed research will address health care personnel employed in the city’s healthcare facilities. Thus, at the onset of the project, participants will be homogeneous, representing a population of medical personnel of Las Vegas community hospitals. Finally, the Hawthorne effect will not take place in the proposed study, since participants will receive survey forms via email for completion and backward submission to the researcher. To sum up, threats to internal validity are not applicable to this research because of its cross-sectional non-experimental design.
Statistical conclusion validity assesses the degree to which a researcher comes to correct statistically validated conclusion concerning the hypothesized and tested relationship. In other words, statistical conclusions validity refers to the extent to which statistical measurement allows building valid inferences about the planned covariation (Wulferth, 2013). Statistical conclusion validity is absent under conditions that facilitate the Type I error rate, when testing results reject the null hypothesis despite of its being true in reality (Anderson et al., 2014). The most typically recalled threats to statistical conclusion validity are low statistical power, low reliability of instrumentation, and violated assumptions of statistical tests.
Statistical power concerns the sensitivity of the used statistical tests to achieve meaningful results or treatment effects (Murphy, Myors, & Wolach, 2014). The extent of statistical powers depends on several factors, such as sample size, effect size, and alpha level calculated by statistical significance testing. In regard for the sample size, statistical power is greater when its sample size is larger and vice versa. In terms of effect size, high correlation effect between variables contributes to the inquiry’s statistical power. Alpha level illustrates the cutoff for separating change from non-chance results (Murphy, Myors, & Wolach, 2014).
The latter is not applicable to the proposed study, since it will be non-experimental correlational research, which eliminates the possibility of chance-based findings. The issue of effect size seems irrelevant as well, since the researcher has suggested a cause-effect relationship between only two variables, relying on academic and analytical publications on the subjects. Specifically, high price of some health care services, including intensive care, makes this type of care unavailable for low-income patient populations. Hence, cost reduction is associated with a greater access to ICU services and the overall quality of care provided. This allows expecting a high effect size between cost reduction and the quality of ICU care.
The final constituent of statistical power – sample size – is the most relevant for the proposed study. A small sample size is a precursor to the study’s failure to reject the null hypothesis (Type II error), lowering statistical power of the testing and making results insignificant. Furthermore, sample size is accountable for statistical significance and effect size – small sample is unable to demonstrate a strong correlational effect and ensure statistical significance (Erchul & Sheridan, 2014). In the pursuit of high statistical power, the researcher expects to recruit the minimum of 50 healthcare personnel employed in ICUs at community hospitals of Las Vegas, NV. Because of the narrow focus on this specific care sector, the sample of 50-100 medical employees seems representative for this population group. Hence, the researcher expects no threats to statistical conclusion validity due to low statistical power.
Another factor that poses a threat to statistical conclusion validity concerns low instrumentation reliability (Erchul & Sheridan, 2014). Apart from considering instrument reliability properties during its development process, the researcher needs to assess it for reliability for a specific sample selected. In this pursuit, the researcher will conduct an internal consistency reliability check of the designed survey against the selected sample of health care professionals (Terry, 2014).
The procedure will enable comparison of the reported reliability coefficient and the researcher-calculated reliability coefficient. The researcher will determine the reliability coefficient by calculating Cronbach’s alpha using SPSS statistical software. Computations of Cronbach’s alpha will estimate the internal consistency of the survey questionnaire and the extent to which items tested correlate with one another. Ranging from 0 to 1, Cronbach’s alpha coefficient signifies reliability of the instrument, when it exceeds .70 (Terry, 2014). In reference to careful instrument development and sample selection, the researcher expects a positive coefficient of instrument reliability against sample.
The last, but not the least threat to statistical conclusion validity refers to violated assumptions of statistical tests. In fact, all statistical tests take place to measure formulated assumptions. Violated assumptions are likely to mislead the researcher and the audience about probabilities of error making, including Type I and Type II errors. Statistical tests rely on diverse assumptions concerning the collected data. These assumptions refer to the interval level of data, random sampling, and normal distribution of scores. In case of either of the assumptions violation, results of statistical analysis may lack accuracy (Grove, Burns, & Gray, 2012). In order to prevent violation of data assumptions, the researcher take a range of measures. Precisely, the researcher will conduct parametric assumptions testing using ANOVA software package to prove equal intervals between data categories to ensure reliance on interval-level data. In addition, the researcher will utilize a random sampling technique in support of data accuracy. According to theory, random sampling allows minimizing sampling bias, while increasing external validity (Grove, Burns, & Gray, 2012).
External validity refers to the extent to which results gained in a research project are true for the general population. Thus, external validity implies the applicability of obtained findings to other samples, settings, and research methods. The focus of external validity is on unique characteristics of the research that may be subject to consideration about the possibility of achieving the same results in different circumstances. A threat to external validity claimed when there is a factor that reduces the capability of generalizing results obtained in a given study (Gravetter & Forzano, 2011). The most widely used technique of achieving external validity concerns rich description of the research process and planning, covering aspects of method, instrumentation, and population sampling. In regard for this study, the researcher has addressed and clarified all methodological issues of the proposed project in detail.
Transition and Summary
Based on the data presented in this section, the following section will deal with application of the research findings to professional practice and implications for change that this study’s outcomes can suggest. First, the application aspect will relate to the ways in which the situation of low-income groups with limited access to intensive care improved for better coverage and care outcomes. Second, the implications for change will relate specifically to seeking balance between cost reduction and quality improvement. All these issues analyzed in detail for the sake of accomplishing the research aim and providing valuable practical healthcare recommendations.
References
Coddington, D. C., & Moore, K. D. (2012). Reducing healthcare costs through better chronic disease management: these days, one often hears healthcare organizations speak of their commitment to improving quality of care and" bending the cost curve." But there's more than a little irony involved if the organization that's talking this line is not focusing attention on improving care for patients with chronic disease. Healthcare Financial Management, 66(8), 126-129.
Cox, J. C., Sadiraj, V., Schnier, K. E., & Sweeney, J. F. (2015). Higher quality and lower cost from improving hospital discharge decision-making. Journal of Economic Behavior & Organization.
Frank-Lightfoot, L., & Davis, A. (2016).Creating a hospital-based community health worker program using college students: Reducing costs and improving quality. Nurse Leader, 14(2), 120–124.
Venkataraman, S. (2015). Cost-Quality Tradeoff in Healthcare: Does it Affect Patient Experience? The Quality Management Journal, 22(3), 38-45.
Appendix A: Consent form
Consent to Participate in a Research Study
Principal Investigator: [name, credentials, institutional affiliation]
Co-investigator: [name, credentials, institutional affiliation]
Invitation to Participate in a Research Study
We/I invite you to be part of a research study about [topic and purpose]. The study funded by [full sponsor name(s), if any].
Description of Your Involvement
If you agree to be part of the research study, we/I will ask you to [details].
Benefits of Participation
You may directly benefit from being in this study because [details].
OR
Although you may not directly benefit from being in this study, others may benefit because [details].
Risks and Discomforts of Participation
There may be some risk or discomfort from your participation in this research [list the specific risk, discomfort, and describe what you will do to minimize these].
Compensation for Participation
For your participation in this research project, you will receive [details].
Confidentiality
We/I plan to publish the results of this study. We/I will not include any information that would identify you. Your privacy protected and your research records will be confidential.
It is possible that other people may need to see the information you give us as part of the study, such as organizations responsible for making sure the research is done safely and properly like Walden University, government offices or the study sponsor, [sponsor name(s), if any].
Storage and Future Use of Data
I/We will store your data/specimens [why and how, duration, who has access, and time reference for destruction of data or specimens, if applicable].
Voluntary Nature of the Study
Participating in this study is voluntary. Even if you decide to participate now, you may change your mind and stop at any time. You do not have to answer a question you do not want to answer. Just tell me/us and I/we will go to the next question. If you decide to withdraw before this study is completed, [details about disposition of data].
If you have questions about this research, including questions about scheduling or your compensation for participating, you may contact [PI name, contact info for PI (and faculty advisor, if PI is a student)].
If you have questions about your rights as a research participant, or wish to obtain information, ask questions or discuss any concerns about this study with someone other than the researcher(s), please contact Walden University.
Consent
I agree to participate in the study.
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Printed Name
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