DNP-DPI- Project Revision

DNP-DPI- Project Revision

26

Improving Medication Adherence among Type II Home Healthcare Diabetic Patients

Submitted by

Bola Odusola-Stephen

A Direct Practice Improvement Project Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Nursing Practice

Grand Canyon University

Phoenix, Arizona

September 30, 2021

© Bola Odusola-Stephen, 2021

All rights reserved.

GRAND CANYON UNIVERSITY

Improving Medication Adherence among Type II Home Healthcare Diabetic Patients

by

Bola Odusola-Stephen

has been approved.

September 30, 2021

APPROVED:

Bridget Drafahl., PhD, CNL, CNE, RN-BC., DPI Project Chairperson

Bamidele Jokodola., DNP., DPI Project Mentor

ACCEPTED AND SIGNED:

________________________________________

Lisa Smith, PhD, RN, CNE

Dean and Professor, College of Nursing and Health Care Professions

_________________________________________

Date

Abstract

Medication adherence is essential in controlling chronic health conditions such as Type II diabetes in home health patients. At the project site, there was no standard procedure for identifying and addressing patient medication adherence. The purpose of this quantitative quasi-experimental quality improvement project was to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients of a home healthcare agency in urban Texas over four weeks. The nursing theory and change model that guided the project was Orem’s self-care deficit theory and Roger’s diffusion of innovation model. The total sample size was N= 30, n = 15 in the comparative group and n = 15 in the implementation group. The data was extrapolated from the facility’s electronic health record. To analyze the comparison and implementation group data, a chi-square test was used, and showed that X2 (1, N = 30) = .159, p =. 999. The p-value of .999 showed no statistical significance between medication adherence for the comparative versus the implementation group. Despite the lack of statistical significance, clinical significance was noted with the nurses consistently using the tool and conducting medication adherence screenings to assist the patient in remaining compliant with their treatment regimen. The findings suggested that implementing a medication adherence program could improve patient compliance rates. Future recommendations would include using larger populations of home health patients for the project.

Keywords: diabetes mellitus type II, Diffusion of innovation model, home-based care, medication adherence, MAP resources, Orem’s self-care deficit theory

Dedication Comment by Author: If you are not using these pages then they need to be deleted.

An optional dedication may be included here. While a practice improvement project is an objective, scientific document, this is the place to use the first person and to be subjective. The dedication page is numbered with a Roman numeral, but the page number does not appear in the Table of Contents. It is only included in the final practice improvement project and is not part of the proposal. If this page is not to be included, delete the heading, the body text, and the page break below.

Acknowledgments Comment by Author: Again if you are not using these pages then delete.

An optional acknowledgements page can be included here. This is another place to use the first person. If it applies, acknowledge, and identify grants and other means of financial support. Also acknowledge supportive colleagues who rendered assistance. The acknowledgments page is numbered with a Roman numeral, but the page number does not appear in the Table of Contents. This page provides a formal opportunity to thank family, friends, and faculty members who have been helpful and supportive. The acknowledgements page is only included in the final practice improvement project and is not part of the proposal. If this page is not to be included, delete the heading, the body text, and the page break below. If you cannot see the page break, click on the Show/Hide button (go to the home tab and then to the Paragraph toolbar).

Table of Contents Chapter 1: Introduction to the Project 16 In Chapter 1, the project, background information, and problem statements were described. Other segments included the purpose of the project, the clinical question, the advancing of scientific knowledge, and the significance of the project. The last sections comprised the rationale for using a quantitative method and quasi-experimental design, definition of operational terms, assumptions, limitations, and delimitations. The last few sentences provided a preview of Chapter 2. 17 Background of the Project 17 Patients with diabetes often express difficulties adhering to medication regimens, reinforcing the critical role of receiving education from home healthcare providers (Wong et al., 2020). This is partly because the patients do not have sufficient knowledge and education regarding diabetes and proper management of the disease (Wong et al., 2020). With diabetes being one of the leading diagnoses for patients needing home health services, healthcare agencies must educate their staff to evaluate the factors prohibiting patients from adhering to their medication regimen. 18 Problem Statement 18 The project contributed to solving the problem by introducing a standardized method of evaluating the patient’s medication adherence. It improved the healthcare providers’ knowledge and awareness regarding the obstacles or factors the patient may face in maintaining a medication regimen. This helped the facility adhere to the current Centers for Disease Control and Prevention (2020a) guidelines to help the participants maintain their normal daily glucose levels, deter healthcare costs, frequent hospitalizations, and infections. 19 Purpose of the Project 19 The project contributed to the nursing field by increasing the healthcare providers’ knowledge and awareness of the obstacles and other risk factors involved in patients not adhering to their medication regimen. It helped increase dialogue between the provider and patient in sharing the details of their behavior (Bussell et al., 2017). This created a positive, blame-free atmosphere allowing the patients to discuss their medication-taking behavior (Bussell et al., 2017). 20 Clinical Question 20 The independent variable was the MAP resources. The dependent variables were the medication adherence rates. To address the clinical question, the medication adherence rate for 30-days before and 30-days after implementing MAP resources were compared using a chi-square test. The chi-square test allowed for a comparison of the medication adherence rate for patients 30 days before and 30 days after the implementation, answering the clinical question. The level of significance was set to .05, indicating a p-value of less than .05 revealed statistical significance. The clinical question was aligned with the problem statement in examining the effect of the MAP resources on diabetic patients. 20 Advancing Scientific Knowledge 21 The findings noted by Holecki et al. (2018) reinforced the beneficial nature of implementing the MAP resources, improving the quality of patient care received. This quality improvement project fits within helping to correct the gap noted in the literature (regarding medication adherence) for this population. It contributed to the clinical site by assisting diabetic patients to maintain their medication regimens. The project findings assisted the participants in decreasing potential infections, hospitalizations, and incurring financial costs. 24 Rationale for Methodology 24 A quantitative methodology supported the project because it permitted the primary investigator to remain objective in providing the project’s findings (Leedy & Ormord, 2020). The methodology allowed the primary investigator to summarize the data to support generalizations for a larger or similar population. The method was less costly, with easy replication for future quality improvement projects to obtain the same results (Leedy & Ormord, 2020). 25 Nature of the Project Design 25 Definition of Terms 27 Assumptions, Limitations, Delimitations 29 Delimitations are choices the primary investigator made, describing the boundaries placed on the project. One project delimitation noted was the inclusion criteria of the participants. Patients with diabetes, ages 35 to 64, were included in the project. Since this project’s focus was to explore medication adherence among diabetes patients, which was a concern at the project site, it narrowed the field to learn about other patients and their compliance issues. The second delimitation was where the project was conducted, an urban area in the southeastern region of the United States, thereby impacting the generalizability of its findings. 31 Summary and Organization of the Remainder of the Project 31 Chapter 2: Literature Review 34 Theoretical Foundation 36 Review of the Literature 38 Patient-related Factors 38 In conclusion, the subject was selected because of its importance in helping Type 2 diabetic patients sustain self-management of their disease. It is imperative that clinicians do not assume that the patients understand the instructions and can read. Utilizing the teach-back method could ensure the patient’s knowledge and comprehension level of the material given to them. Healthcare providers should use a health literacy screening tool to guarantee to instruct the patient on their literacy level. 54 Socioeconomic Factors 54 Interventions 58 Summary 63 Chapter 3: Methodology 65 Statement of the Problem 66 Clinical Question 67 Project Methodology 68 Project Design 69 The electronic medical record used to collect data was Cradle Solutions, software for home health companies. It serves the specialized needs of home health care providers that give a web-based point-of-contact information entry and management (Cradle Solutions, 2021). It complied with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solutions, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator obtained the measurement of the medication adherence rates and aligned it with new protocols and guidelines developed by the facility. 71 Population and Sample Selection 71 Instrumentation or Sources of 73 The source of data for this project was the electronic medical record. It complied with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solutions, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator measured the medication adherence rates and aligned them with new protocols and guidelines developed by the facility. 74 Validity 74 Reliability 75 Data Collection Procedures 76 To maintain the confidentiality of data, hard copies of the demographic and MAP surveys were stored in a locked cabinet in the primary investigator’s home. The results were saved on the primary investigator’s digitally password-protected laptop. To ensure additional cyber-security, an encryption program was installed. Under Grand Canyon University’s Institutional Review Board guidelines, the data will be kept for three years (June 2024). The primary investigator will clean the laptop data using ERASER (a computer program) and Iron Mountain shredding services (Eraser, 2020). 78 Data Analysis Procedures 78 Potential Bias and Mitigation 80 The second bias was related to recall bias, a systematic error that occurs when participants do not accurately remember previous events or experiences (Creswell & Creswell, 2018). The project could be affected because the participants are self-reporting to the nurses using the MAP resources. To prevent this bias, nurses were trained to carefully teach each participant using the same method, preventing their responses from being influenced (Creswell & Creswell, 2018). 81 Ethical Considerations 81 As stated in the Belmont Report (1979), justice refers to allocating burdens (Office for Human Research Protections, 2016). It was possible that the project would lead to unwanted stigma from the participants’ colleagues, family members, or friends. Each participant was treated uniformly following their wishes, so it did not affect the project’s findings. There could be a potential conflict of interest since the primary investigator works at the facility. To minimize the conflict, the primary investigator did not interact with the participants. 82 Limitations 82 Summary 83 Descriptive Data 84 Data Analysis Procedures 87 Results 89 Summary 91 Chapter 5: Summary, Conclusions, and Recommendations 93 Summary of the Project 94 Summary of Findings and Conclusion 94 Theoretical Implications 95 Practical Implications 97 Future Implications 97 Recommendations 98 Recommendations for Future Projects 98 Recommendations for Practice 99 References 101 Appendix A 118 Grand Canyon University IRB Approval Letter 118 MAP Resources 119 Appendix C 120 Permission to Use the MAP Resources 120

List of Tables

Table 1 . Descriptive Data 61

Table 2 . Descriptive Data Ages 62

Table 3 . Medication Adherence Rates 65

List of Figures

Figure 1. Mean Knowledge Scores 66

Chapter 1: Introduction to the Project
According to the Centers for Disease Control and Prevention (2020), diabetes affects one in ten Americans. Diabetes prevalence continues to rise and is expected to increase by 0.3% per year until 2030 (Lin et al., 2018). Proper and effective medication adherence in individuals with Type II diabetes is vital (Kvarnström et al., 2018). This is significant among healthcare patients because diabetes is one of the leading diagnoses for admission to a home health care facility (Sertbas et al., 2019). In this population, approximately 45% of the patients cannot maintain glycemic control (HgbA1c < 7%) (Polonsky & Henry, 2016). Poor medication adherence is associated with increased rates of morbidity and mortality, increased financial costs for hospitals, insurance companies, and frequent hospitalizations (Polonsky & Henry, 2016). At the project site, the primary investigator, in collaboration with the stakeholders, observed that ten percent of the patients were not adhering to their prescribed medication regimen. This prompted frequent hospitalizations, infections, and other diabetic complications. Upon further investigation, it was found that there was not a standardized method for healthcare providers to evaluate the patients regarding medication adherence. Hence, the Medication Adherence Project (MAP) resources and education intervention were introduced. The project was worth conducting because it focused on diabetic home health patients who are not the focal point of many literature reviews. Little information is noted regarding the healthcare team's impact in addressing this population’s lack of medication adherence. The primary investigator introduced a standardized method of managing patients’ medication adherence using MAP resources and education to minimize frequent hospitalizations and infections and increase their quality of life (Starr & Sacks, 2010). The project's purpose was for the primary investigator (PI) to investigate how healthcare team members can address the various factors affecting medication adherence among diabetic patients receiving home health care. In Chapter 1, the project, background information, and problem statements were described. Other segments included the purpose of the project, the clinical question, the advancing of scientific knowledge, and the significance of the project. The last sections comprised the rationale for using a quantitative method and quasi-experimental design, definition of operational terms, assumptions, limitations, and delimitations. The last few sentences provided a preview of Chapter 2. Background of the Project Home-based healthcare has existed since 1909 (Choi et al., 2019). Present-day, home-based healthcare is often selected because of an individual’s personal preferences. While home-based healthcare is not appropriate for all patients, Szanton et al. (2016) noted that this care option is best when an individual’s condition can be managed without admission to a hospital. Patients with diabetes or hypertension are often recipients of home-based healthcare (Wong et al., 2020). Adhering to diabetes medication regimen requirements can be complex. Raoufi et al. (2018) conducted a study using a multi-stage stratified cluster sampling method to recruit its participants. Two thousand one-hundred eight three diabetic patients took part in the study. Of the participants, 51.4% tested their glucose level more than once a month (Raoufi et al., 2018). The authors also noted that 10% of the participants did not monitor the glucose levels correctly or adhere to the medication requirements. Patients with diabetes often express difficulties adhering to medication regimens, reinforcing the critical role of receiving education from home healthcare providers (Wong et al., 2020). This is partly because the patients do not have sufficient knowledge and education regarding diabetes and proper management of the disease (Wong et al., 2020). With diabetes being one of the leading diagnoses for patients needing home health services, healthcare agencies must educate their staff to evaluate the factors prohibiting patients from adhering to their medication regimen. Problem Statement It was not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients. The population affected are home health Type II diabetic patients in an urban healthcare agency in Texas. At the project site, nursing administration and staff cited a lack of medication adherence among diabetic patients. According to data obtained from the site’s electronic health record (EHR), home healthcare providers documented that ten percent of diabetic home healthcare patients are not adhering to their medication regimens. In terms of chronic disease management, researchers have noted increased implications associated with a lack of adherence to medication regimens (Camacho et al., 2019; Hamrahian, 2020; Misquitta, 2020). The lack of medication adherence can be attributed to inadequate drug-related knowledge, medication costs, poor understanding of medication regimen, etc., reinforcing the need for this quality improvement project (Heath, 2019; Sharma et al., 2020). Kvarnström et al. (2018) emphasized healthcare providers play a critical role in ensuring medication adherence. To promote medication adherence among patients of a home healthcare facility, the primary investigator will introduce a standardized method for the healthcare providers to assess the patient’s medication adherence. The staff will achieve greater insight by using MAP resources and an education intervention created by Starr and Sacks (2010). The tools used in this project are the New York City Department of Health and Mental Hygiene Medication Adherence Project toolkit. The toolkit and training guides included: (1) the questions to ask poster, (2) an adherence assessment pad, and (3) my medications list. The purpose of this quantitative quasi-experimental quality improvement project was to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients of a home healthcare agency in urban Texas over four weeks. Comment by Author: Delete, you should only mention once in each chapter and you are talking about this below. The project contributed to solving the problem by introducing a standardized method of evaluating the patient’s medication adherence. It improved the healthcare providers’ knowledge and awareness regarding the obstacles or factors the patient may face in maintaining a medication regimen. This helped the facility adhere to the current Centers for Disease Control and Prevention (2020a) guidelines to help the participants maintain their normal daily glucose levels, deter healthcare costs, frequent hospitalizations, and infections. Purpose of the Project The purpose of this quantitative quasi-experimental quality improvement project was to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas over four weeks. The independent variable is the MAP resources. The dependent variable is medication adherence rates. The project contributed to the nursing field by increasing the healthcare providers’ knowledge and awareness of the obstacles and other risk factors involved in patients not adhering to their medication regimen. It helped increase dialogue between the provider and patient in sharing the details of their behavior (Bussell et al., 2017). This created a positive, blame-free atmosphere allowing the patients to discuss their medication-taking behavior (Bussell et al., 2017). Clinical Question A well-developed clinical question must be related and relevant to patient care. This helps the primary investigator search for evidence-based answers. The clinical question that will direct this quality improvement project is: To what degree does the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas? The independent variable was the MAP resources. The dependent variables were the medication adherence rates. To address the clinical question, the medication adherence rate for 30-days before and 30-days after implementing MAP resources were compared using a chi-square test. The chi-square test allowed for a comparison of the medication adherence rate for patients 30 days before and 30 days after the implementation, answering the clinical question. The level of significance was set to .05, indicating a p-value of less than .05 revealed statistical significance. The clinical question was aligned with the problem statement in examining the effect of the MAP resources on diabetic patients. Advancing Scientific Knowledge This direct practice improvement project sought to enhance medication adherence among diabetic home healthcare patients using the MAP resources. Various researchers have cited the benefits of patient-provider engagement and collaboration to improve medication adherence (Ong et al., 2018; Polonsky & Henry, 2016; Wong et al., 2020). The advancement of a small step forward at the clinical site was to improve medication adherence rates among diabetic patients. Positive patient-related outcomes will probably occur using the MAP protocol. This will add to the current literature and address the gap found regarding non-medication factors among home health diabetic patients. The importance of medication adherence among diabetic patients has been demonstrated in many studies (Ong et al., 2018; Polonsky & Henry, 2016). Despite this, a limited amount of literature has been published regarding how Type 2 patients in the home health setting are affected by this knowledge. In the United States, home healthcare nurses are essential providers in the community (Omidiran, 2018). As a result, they may provide deeper insights regarding strategies that will cause higher medication rates (Omidiran, 2018). The expected population of older adults is expected to double from 40 million to roughly 88 million by 2050 (Administration on Aging, 2015; Omidiran, 2018). Many of these individuals live in a community-dwelling setting (other than a hospital such as home health) and are non-compliant for various reasons. The theoretical framework used in the quality improvement project was Orem’s self-care deficit theory (1995), which was developed to improve patient health outcomes in the context of nursing contribution (Yip, 2021). It comprises three related sections: theory of self-care, self-care deficit, and the nursing system (RenpenningcN et al., 2003). It fits the project because it includes healthcare providers assisting patients in diabetic patients’ self-care and management to improve their function at a home level (RenpenningcN et al., 2003). The patients cannot effectively manage medication adherence for diabetes, which affects their quality of life and health. Orem’s self-care deficit theory advances the project by contributing to previous research conducted on Type II diabetic patients using the theory (Borji et al., 2017; Ghafourifard & Ebrahimi, 2015; Shahbaz et al., 2016). Based on the theory, the primary investigator could increase the patient’s awareness of their disease and minimize non-compliance with their regimen (Borji et al., 2017). The theory helped identify the educational needs of home healthcare patients, which are more important than the proper treatment (Borji et al., 2017). It has been recommended that Orem’s self-care deficit theory be implemented to increase a patient’s knowledge level and adherence to self-care practices (Shahbaz et al., 2016). The change model used in this quality improvement project is the Diffusion of Innovation Model developed by Rogers (2003). There are five stages: a) knowledge or awareness, b) persuasion or interest, c) decision or evaluation, d) implementation or trial, e) confirmation or adoption (Rogers, 2003). Diffusion is defined as a social process, which occurs among individuals in response to knowledge regarding a new strategy for improving their health (Dearing & Cox, 2018). This change model provided the primary investigator with methods to share and educate regarding a new diabetic prevention strategy (Lien & Jiang, 2016). The model has been utilized in various fields to help healthcare providers understand and translate new concepts, treatments, disease knowledge, and educational methods (De Civita & Dasgupta, 2007; Lien & Jiang, 2016). For this project, the primary investigator used the MAP resources to provide the participants with an alternative approach to be integrated into the daily practices to improve quality of life and diabetic outcomes. Utilizing these methods will help the project advance by helping healthcare providers implement a standardized method to evaluate the patient’s medication-taking behaviors. Significance of the Project The significance of the project is related to providing a comprehensive array of treatment options for patients with chronic diseases such as diabetes (Polonsky & Henry, 2016). Home-based care has been gaining popularity as a means of meeting the unique needs of various population groups (Holly, 2020). Type II diabetes patients who qualify for home-based care options must demonstrate their willingness to work with the home healthcare agency at the selected project site. When patients who receive home-based care fail to adhere to the care requirements set forth, adverse outcomes can ensue (Polonsky & Henry, 2016). The significance of the project is related to providing a comprehensive array of treatment options for patients with chronic diseases, such as diabetes (Polonsky & Henry, 2016). Home-based care has been gaining popularity to meet the unique needs of various population groups (Holly, 2020). Type II diabetes patients who qualify for home-based care options must demonstrate their willingness to work with the home healthcare agency at the selected project site. When patients who receive, home-based care cannot adhere to the care requirements set forth, adverse outcomes can ensue (Polonsky & Henry, 2016). The findings noted by Holecki et al. (2018) reinforced the beneficial nature of implementing the MAP resources, improving the quality of patient care received. This quality improvement project fits within helping to correct the gap noted in the literature (regarding medication adherence) for this population. It contributed to the clinical site by assisting diabetic patients to maintain their medication regimens. The project findings assisted the participants in decreasing potential infections, hospitalizations, and incurring financial costs. Rationale for Methodology The methodology chosen for this quality improvement project is quantitative. Creswell and Creswell (2018) noted a quantitative methodology is best suited for projects that require data in numerical form. In this project, the numerical data will be presented using charts and graphs. These charts and graphs will allow readers to compare medication adherence rates pre-project implementation and post-project implementation. While qualitative research studies are beneficial, they examine experiences, perspectives, and beliefs about a specific issue (Creswell & Creswell, 2018). The data collection used in this type of method is interviews (semi-structured, one-on-one, and focus groups). For this project, the primary investigator is not seeking to understand the participants’ feelings, behaviors, or lived experiences related to medication adherence. A quantitative methodology supported the project because it permitted the primary investigator to remain objective in providing the project’s findings (Leedy & Ormord, 2020). The methodology allowed the primary investigator to summarize the data to support generalizations for a larger or similar population. The method was less costly, with easy replication for future quality improvement projects to obtain the same results (Leedy & Ormord, 2020). Nature of the Project Design A quasi-experimental design was used for this project. Quasi-experimental designs are used to compare data before and after implementing an initiative/intervention. Price et al. (2017) stated that in a pre-intervention/post-intervention design, the dependent variable is measured once before the treatment is implemented after it is implemented. This design is used when research occurs in a controlled environment. While this project was conducted in a controlled environment, the primary investigator selected a quasi-experimental design because it was more cost-effective than an experimental project design (Schweizer et al., 2016). Since data pre-project and post-project implementation need to be collected and analyzed to explore the intervention’s impact, a quasi-experimental design is most appropriate. A correlational design was considered but not appropriate for the project because the primary investigator was not seeking to understand the relationships occurring among the variables (Creswell & Creswell, 2018). This design is typically descriptive, relying on a hypothesis (Leedy & Ormord, 2020). The primary investigator was not seeking the relationships between the independent variable (MAP resources and education intervention) and the dependent variable (medication adherence rates). The targeted population was home health patients ages 35 to 64 years old. The selected site serves approximately 100 patients annually, and 30 patients are diagnosed with Type II diabetes. The inclusion criteria were males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria comprising individuals with language or cognitive deficits and diagnosed with Type I diabetes. The data collection process began once approved by Grand Canyon University Institutional Review Board (IRB). Recruitment occurred from the primary investigator giving the patients informational flyers during their home health visits with the providers. The nurses answered questions regarding the project’s risks, benefits, and purpose and instructed them that their participation was voluntary. The primary investigator used a convenience sample because of the access to the participants. Data were collected four weeks before project implementation from the electronic medical records (Cradle Solutions software) (medication adherence rates) (Cradle Solutions, 2021). In the last three days of the first week, the primary investigator educated the healthcare providers regarding MAP resources. The staff began implementing the tool, and the post medication adherence rates were assessed four weeks post-implementation. The primary investigator documented the data in a Microsoft Excel 2016 codebook developed by the primary investigator. Once completed, it was exported into the SPSS-27 and analyzed using an independent t-test. A five-item demographic questionnaire was used for descriptive statistics of the population. The survey included (age, gender, years with Type II diabetes, oral or insulin, and education). Pre-intervention and post-intervention data were obtained via the project site’s EHR. The questions that will be analyzed are: (1) “Have you experienced any increase in thirst?” (2) “How often do you urinate?” (3) “Do you often feel fatigued even when doing little tasks?” and (4) “Do you experience blurred vision?” In addition to the questions, home healthcare providers will ask the patient “Are you taking your medications?” Any information attained from the question and due to probing, observation of patient’s medications, and patient-related medication adherence were documented in the project site’s EHR. The data was analyzed using an independent t-test to determine the statistical significance. Definition of Terms The following operational terms were used interchangeably throughout the manuscript. In this project, the terms utilized were significant to the project’s foundation and background. By using functional terms, the reader was provided with transparency and insight into the project. Adherence Assessment Pad. The Adherence Assessment Pad is part of the MAP resources that explores answers via the patient perspectives. Using the Adherence Assessment Pad, nursing staff members will be able to explore the concerns of patients and adjust, pending further project team review, to the patient’s medication regimen (Starr & Sacks, 2010). Home-based Healthcare. The term home-based healthcare or home healthcare references the medical care that is provided to patients in the comfort of the patient’s home (Polonsky & Henry, 2016). Home-based healthcare services differ depending on a patient’s needs, diagnosis, and other factors. Medication Adherence. The term medication adherence references the extent to which a patient, caregiver, or home nurse follows the recommended guidelines on managing a medical condition (Ahmed et al., 2018). My Medications List . Is a list that provides a breakdown of the patient’s medications, in an easy-to-follow chart format, thereby improving patient medication adherence (Starr & Sacks, 2010). Questions to Ask Poster . Is a part of the MAP toolkit, which will be utilized during this project. When using the Questions to Ask Poster, home healthcare providers answer six questions to patients about medication adherence and medication knowledge. The questions that providers will answer include: (1) “Why do I need to take this medicine?,” (2) “Is there a less expensive medicine that would work was well?,” (3) “What are the side-effects and how can I deal with them?,” (4) “Can I stop taking any of my other medicines?,” (5) “Is it okay to take my medicine with over-the-counter drugs, herbs, or vitamins?,” and (6) “How can I remember to take my medicine?” Providers must answer all the questions and should assume that individuals have no medication knowledge, thereby confirming that patients know and understand these critical answers (Starr & Sacks, 2010). Type II Diabetes. For this project, Type II diabetes is the topic of exploration. It is described as an impairment of the body regulating and using glucose as a fuel source. Type II diabetes is a chronic condition where an excess amount of sugar is circulating in the blood stream (Mayo Clinic, 2019). Assumptions, Limitations, Delimitations As with all practice improvement projects, assumptions, limitations, and delimitations must be addressed. Assumptions are considered self-evident truth (Grand Canyon University, 2021). They are statements that are deemed plausible by other individuals and peers who read the project. The first assumption was that the participants would self-report honestly to the best of their recollection. To minimize social-desirability bias, the primary investigator compared the participant’s answers with other data (laboratory values for glucose levels) (Leedy & Ormrod, 2020). The second assumption was that the primary investigator had adequately described the current situation at the project site. To ensure that fabrication and falsification of the project findings did not occur, the primary investigator observed the nurses during the patient visit to monitor the interactions. To safeguard that the project results were not influenced by skewed statistical findings, an outside source was used to serve as a statistician. The third assumption was related to the inclusion criteria for the targeted population. Demographics and characteristics of the population were the same to meet their needs. Identifying the relevant inclusion and exclusion criteria was essential while conducting the quality improvement project, as these factors influence the external validity of the project (Patino & Ferreira, 2018). Leedy and Ormrod (2020) stated that limitations are factors that the primary investigator had no control over. The first limitation was the primary investigator’s lack of control over the environment related to the novel coronavirus pandemic (COVID-19). The pandemic has affected the method by which the project was implemented. The primary investigator did not interact with the participants during the project. Instead, five registered nurses were educated to implement the project. The pandemic has increased many patients’ fear related to one-on-one interaction with their primary care providers. The primary investigator did not know if there was a possibility with the new variant (Delta-variant) if the project would be modified to virtual monitoring to minimize the participant’s risk of COVID-19 infection. The second limitation was conducting the project (four weeks versus longer) (cross-sectional versus longitudinal). A cross-sectional project allowed a snapshot of a specific moment (Leedy & Ormrod, 2020). A longitudinal project would have allowed the primary investigator to provide a richness of data regarding the topic. The primary investigator could identify and convey the findings related to the participants’ behaviors, patterns of change, experiences, and reduce recall bias (Coolican, 2014). This type of project would have allowed the primary investigator to test whether the variables were casual or the result of other differences (Leedy & Ormrod, 2020). Delimitations are choices the primary investigator made, describing the boundaries placed on the project. One project delimitation noted was the inclusion criteria of the participants. Patients with diabetes, ages 35 to 64, were included in the project. Since this project’s focus was to explore medication adherence among diabetes patients, which was a concern at the project site, it narrowed the field to learn about other patients and their compliance issues. The second delimitation was where the project was conducted, an urban area in the southeastern region of the United States, thereby impacting the generalizability of its findings. Summary and Organization of the Remainder of the Project The aging population is growing at an increasing rate in the United States, hence snowballing the number of individuals taking medications to manage their Type II diabetes. Kvarnstrom et al. (2018) emphasized that for Type II diabetics, it is essential that proper and effective medication adherence be maintained. For home healthcare patients, 45% of this population fail to maintain glycemic control < 7% (Polonsky & Henry, 2016). This is attributed to poor medication adherence (Polonsky & Henry, 2016). Healthcare providers are a critical component in making a difference by helping patients learn and maintain medication adherence. The quality improvement project used a quantitative methodology. The rationale for using this method was to collect numerical data that could be statistically analyzed. A quasi-experimental design answered the clinical question to determine if the outcome affected the medication adherence rates. Orem’s self-care deficit theory and Roger’s diffusion of innovation model guided the project (Rogers, 2003). Chapter 1 provided detailed support for utilizing the MAP resources to improve medication adherence among diabetic patients at the project site. A quantitative, quasi-experimental design was used to explore the impact of the MAP intervention on improving medication adherence among Type II diabetes patients on the selected project site. Other portions of the chapter included advancing scientific knowledge using Orem’s self-care deficit theory and Roger’s diffusion of innovation model. A detailed description was given related to the project’s significance, the project’s methodology, and design. The last few sections of the chapter comprised the definition of terms, assumptions, limitations, delimitations, and a summarization of the chapter. Comment by Author: This should not be past tense as you are discussing only past tense when talking about the project. Here you are talking about the manuscript. Chapter 2 presented a detailed summary of the literature collected related to the project’s clinical question. Information about the theoretical framework and change model is detailed. The chapter comprised five sections, which highlighted information about literature obtained from 2016 to 2021. The information presented provided readers with in-depth knowledge and the importance of each chosen section. Chapter 3 offered research methodology details that the primary investigator employed. The information presented in the chapter included the selected research design, the target population, and the sample size. Furthermore, data collection tools (specifically the MAP’s resources) and data analysis procedures were discussed. The reliability and validity of the project instruments are detailed. Lastly, ethical considerations for collecting data were addressed. Chapter 4 presented the project’s findings were analyzed using chi-square analysis. Results regarding the descriptive and inferential data analyses were offered. Furthermore, a brief discussion of project-related findings was delivered. The information was presented using graphics, figures, and tables. Chapter 5 delivered the conclusions and recommendations drawn from the project’s results. The impact of the findings, in terms of practical and theoretical knowledge, was offered. Chapter 2: Literature Review There is a global epidemic of diabetes mellitus (DM), and a large proportion of diabetic patients suffer from Type II diabetes (Rana et al., 2019). Adherence to prescribed medications is essential for the achievement of therapeutic success and reduction of diabetic complications (Rana et al., 2019). For Type II diabetic home health patients, this is vital in maintaining self-care and management of the disease. Unfortunately, approximately 30% to 50% of patients adhere to their medication regimen (Hennessey & Peters, 2019). Diabetes is a lifestyle disease, which can be prevented or avoided by making lifestyle changes. Disease management can also occur through adhering to one’s prescribed medication regimen(s). Medication adherence is important since it can help to reduce the likelihood of diabetes-related challenges and complications. In the United States (U.S.), the problem is associated with increased morbidity and mortality rates, with approximately 125,000 deaths and 10% of hospitalizations annually (Hennessey & Peters, 2019). Furthermore, medication nonadherence costs the U.S. healthcare systems roughly $100 billion to $317 billion yearly (Kini & Ho, 2018). The purpose of this quantitative quasi-experimental quality improvement project was to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas over four weeks. Chapter 2 reintroduced the project’s subject matter, background, theoretical framework, and change model. The chapter reviewed previous and current peer-reviewed articles related to medication adherence in Type II diabetic home health patients. The chapter’s themes are related to patient-related factors (non-pharmacological and pharmacological lifestyle changes, patient beliefs). The sub themes were socio-economic factors (medication costs, health literacy, lack of social support), health system factors (trust in the healthcare provider, complicated medication regimen), and interventions (patient education, motivational interviewing, and MAP resources). The primary investigator conducted a literature review utilizing peer-reviewed articles from 2016 to current. The inclusion criteria were articles written in English, topics specific to the project such as barriers to medication adherence, MAP resources, medication adherence, and Type II diabetes. The exclusion criteria were articles not written in English, more than six years, Type I diabetes, or involved children. Databases reviewed were PubMed, Google Scholar, CINAHL, Cochrane Library, EBSCOhost, and Grand Canyon University online library. The review revealed over 632,000 plus results; however, the primary investigator selected 50 articles for this chapter for this project. One of the most problematic issues associated with home care for diabetes patients is adherence to medications. According to Bonney (2016), patients take their medication as prescribed only 50% of the time. Furthermore, patients are often reluctant to share medication compliance details, thereby resulting in health-related complications (Bonney, 2016). Type II diabetes mellitus is at an epidemic proportion globally (Centers for Diseases and Prevention Control, 2020). The incidence of the disease will continue to rise from 382 million individuals to 417 million by 2035 (Polonsky & Henry, 2016; Rana et al., 2019). Healthcare experts are becoming increasingly concerned because of the costs, morbidity, and mortality rates linked with the disease (Polonsky & Henry, 2016). One of the elements contributing to the problem is poor medication adherence (Rana et al., 2019). This is particularly true in-home health Type II diabetic patients. Medication adherence in adults with chronic conditions is roughly between 30% to 50% (Kini & Ho, 2018; Neiman et al., 2017). Omidiran (2018) stated that approximately 64% of older adults who receive home health services were not adherent to their medications. Furthermore, the healthcare system associated with medication nonadherence is costing the U.S. healthcare system $100 billion to $317 billion annually (Rana et al., 2019). As adults in this country age, many are afflicted with chronic diseases such as diabetes (Type II). Many individuals such as Type 2 diabetics are admitted for admission to home health agencies (Sertbas et al., 2019; Wong et al., 2020). Home health agencies have been in existence for over 30 years (Choi et al., 2019). These organizations will continue to grow and impact medical advances and technology (Wong et al., 2020). Hence, there is a need for healthcare providers to become familiar with strategies and barriers linked with medication adherence for this population. Many home health patients have difficulty adhering to their medication regimens. They often express difficulty adhering to the regimens, which reinforces the critical role of home healthcare providers (Wong et al., 2020). This is partly due to type 2 diabetic patients not having knowledge and education related to the disease and proper self-management (Wong et al., 2020). Theoretical Foundation Orem’s self-care deficit theory was selected to guide this quality improvement project. The theory was chosen because of its expectations that an individual must be self-reliant and responsible for their care (Orem, 1985). Dorothea Orem’s theory states self-care is an activity that a person engages in to maintain, restore, or enhance their health (Orem, 1985). The theory further states that nurses should not consider patients as inactive or sheer recipients of healthcare; instead, they should be considered reliable, responsible individuals who can make informed decisions and be active in their health care (Orem, 1985). This theory describes nursing as an action between two or more individuals (RenpenningcN et al., 2003). Furthermore, it assumes that a successful patient with self-care understands it is a primary element in health prevention and illness (RenpenningcN et al., 2003). The theory fits the project because the healthcare providers are in supportive educational roles, which assists the patient when they are ready to learn or cannot complete a task without guidance (Orem, 1985). In addition, the theory relates to healthcare providers assisting patients in their self-care and management to improve their function at a home level (RenpenningcN et al., 2003). The theory has been used in multiple studies regarding patients with chronic diseases (Afrasiabifar et al., 2016; Borji et al., 2017; Khademian et al., 2020). The change model that will be used is the diffusion of innovation developed by Rogers (2003). There are five components of the theory are a) knowledge, b) persuasion, c) decision, d) implementation, and e) adoption (Rogers, 2003). The model is defined as a social process, which occurs among individuals in response to knowledge regarding a new strategy for improving their health (Dearing & Cox, 2018). It is a process communicated within a specific timeframe (for this project, four weeks) (Dearing & Cox, 2018). This change model can provide the primary investigator with methods to share and educate regarding a new diabetic prevention strategy (Lien & Jiang, 2016). The model has been utilized in various fields to help healthcare providers understand and translate new concepts, treatments, disease knowledge, and educational methods (De Civita & Dasgupta, 2007; Lien & Jiang, 2016). For this project, the primary investigator using the MAP resources provide the participants a new approach to be integrated into the daily practices to improve quality of life and diabetic outcomes. Utilizing these methods will help the project advance by helping the healthcare providers to implement a standardized method in evaluating the patient’s medication-taking behaviors. Review of the Literature Diabetes is prevalent in the United States and globally (Rana et al., 2019). It is one of the primary diagnoses for being admitted into home health care (Sertbas et al., 2019). Hence, the usage of home health services has become increasingly popular because it allows patients to remain in a comfortable atmosphere and decrease hospitalizations (Sertbas et al., 2019). There are many studies regarding older adults and diabetes, but minimum regarding home health care patients with diabetes (Sertbas et al., 2019). The review of literature is based on themes centered on patient-related factors, socioeconomic factors, and interventions. Patient-related Factors The World Health Organization (2017) stated patient related factors encompass an individual’s resources, knowledge levels, belief system, perspectives, and expectations. These factors can vary dependent on the non-pharmacologic and pharmacologic lifestyle changes that the person maintains (Nduaguba et al., 2017). Type II diabetes management involves not just medication adherence but observance to monitoring diet and exercise, follow-up, and self-care (Nduaguba et al., 2017). Medication adherence. Medication adherence is a term that refers to one taking medication as prescribed by their healthcare practitioner (Ahmed et al., 2018). Healthcare providers must ensure that the prescriptions provided to patients are suitable to the individual’s conditions. While medication adherence is important, there is a plethora of literature available that expresses the prevalence of medication non-adherence among patients. Various factors continue to impact medication adherence, which includes, but are not limited to, fear, costs, misunderstanding, too many medications, lack of symptoms, mistrust, worry, and depression (American Medical Association [AMA], 2020). To prevent medication non-adherence, providers can seek to understand the needs of patients and provide them with resources that can aid in overcoming non-adherence. Comment by Author: This does not match the subtheme listed in page 35. Ahmed et al. (2018) emphasized that the quality of healthcare can be influenced by the body’s ability to respond to the treatment. A study conducted by Rana et al. (2019) was related to exploring medication adherence to prescribed treatments as a crucial factor for hospitalized Type II diabetic patients in a Bangladesh hospital. The quantitative, descriptive cross-sectional study involved 112 Type II diabetic patients recruited from medical and endocrinology wards. Much of the sample size age was 57.46, 60.7% were male and married. The patient’s medication adherence was measured using the 7-item MCQ scale modified by Ahmad et al. (2013). Data were analyzed using SPSS-21. Descriptive statistics were used to measure the participants’ demographics. An independent sample t-test and one-way ANOVA with post hoc comparisons were used to evaluate the relationships between the variables (p =.05). The results from the Rana et al. (2019) study showed 72.3% of the participants forgot to take their medications, 96.4% chose not to take the medication or miss a dose when feeling better. Most of the patients, 81.3%, did not take their medications with them when traveling. The mean scores of the MCQ were 26.46 (SD =1.58). The study’s results concluded that the level of medication adherence among Type II diabetic patients was suboptimal (Rana et al., 2019). The authors recommended that more attention needed to be given to varied age groups related to medication adherence. Lee et al. (2017a) conducted a quantitative study to determine the medication adherence among Type II diabetic patients in an Asian community. This cross-sectional study involved 382 Asian participants from a primary outpatient care clinic in Singapore. The patient’s medication adherence was measured using a five-item Medication adherence report scale (MARS-5). A low medication adherence score was <25. The sample size was predominately female, with a mean age of 62 years. Using univariate analysis, the results showed 57% of the participants had a low medication adherence score, which was attributed to them being married or widowed, taking fewer than four medications daily, and poor glucose control. The study concluded that younger patients were susceptible to low medication adherence scores (Lee et al., 2017). Although the studies were conducted in different settings (primary care and hospital), the results demonstrated a need for healthcare providers to focus on different age groups and their reasons for not adhering to their medication regimen. The studies were cross-sectional, which indicated the authors were unable to evaluate the participant's habits and trends. This could have changed if they could assist the patients with barriers they faced during the studies. This topic was chosen because inefficient medication adherence is complex, with a variety of contributing causes; hence, there is no universal solution (Rodriguez-Saldana, 2019). For a patient to succeed with medication adherence, the healthcare provider must understand the underlying reasons that are barriers that could be removed or diminished. Teaching the patient new strategies that are patient-centered will help them achieve the new normal. Health-related (diabetes) stigma . This is a health-associated stigma, which is a psychological factor that impacts the lives of individuals with chronic medical conditions such as epilepsy, obesity, and Type 2 diabetes (Liu et al., 2017). Stigma is described as a pattern that differs from culturally defined norms, resulting in a punitive response (Liu et al., 2017). In this study, the authors describe stigma as the experiences of negative feelings, such as exclusion, rejection, or blame, because of the perceived stigma of having diabetes (Liu et al., 2017). Harper et al. (2018) conducted a quantitative, exploratory study with 53 (n=53) African American Type 2 diabetic patients. The purpose of the study was to assess the frequency of stigma and its association with helpful or harmful family behaviors and its consequences (Harper et al., 2018). The Diabetes Family Behavior checklist (DFBC-II) was used to assess the times within the month family members performed helpful (nine times) or harmful (seven items) ranged on a scale from 1 (never) to 5 (once per day) (Harper et al., 2018). Comment by Author: Does not match the subtheme listed above. Data analysis was conducted using Mann-Whitney U/Fisher’s exact tests to evaluate the difference between the participants who agreed with at least one family member stigma item (Harper et al., 2018). Spearman’s correlations evaluated the associations between family stigma and consequences of stigma and supportive or harmful family behaviors (Harper et al., 2018). The results showed the participants were primarily female (74%) with low socioeconomic status. The average number of experiences was 1.26 [+ or -] 1.8, and the average number of consequences reported was 0.6 [+ or -] 1.3. Fifty-seven percent of the participants agreed with having at least one experience with stigma, 23% with one experience of family stigma, 15% with two experiences, 13% with three experiences, and 6% with four or more experiences (Harper et al., 2018). Twenty-eight percent stated they experienced at least one consequence of stigma, and 15% agreed with one consequence (Harper et al., 2018). The study concluded that experiences related to Type 2 diabetes family stigma were everyday occurrences and associated with consequences such as concealment and resentment of self-care could affect patient outcomes (Harper et al., 2018). The study’s limitations were using Black participants and a large female sample; hence, the results could not be generalized. A quantitative study conducted by Liu et al. (2018) measured diabetes stigma and its psychological impact on Type 1 and Type 2 diabetic patients. The sample size included 12,000 individuals with diabetes, with 76% (type 1) and 52% (type 2). The primary investigator will focus on the Type 2 populace in the study. A Qualtrics six-item survey was given to the participants who self-reported. The survey comprised demographics, diabetes treatment behaviors, attitudes related to diabetes management, product choices, and satisfaction (Liu et al., 2018). Each question had a 10-point Likert scale to determine if they strongly agreed to not strongly agree with the impact of diabetes stigma. The results showed the perception of diabetes stigma among Type 2 diabetes was significantly increased with higher therapy intensity (Liu et al., 2018). Forty-nine percent of non-insulin users reported diabetes stigma compared to 55% using insulin (P <0.0005) and 61% of those receiving intensive insulin therapy (P >0.0005) and 61% receiving intensive insulin therapy (P <0.0005) (Liu et al., 2017). The Type 2 participants felt they were a burden on the healthcare system (65%), experienced misunderstandings with others about diabetes, and viewed the disease as contagious (Liu et al., 2018). The limitations of the study were the self-reporting and being collected online. The study concluded that the perception of diabetes stigma appeared to be linked with uncontrolled diabetes, higher visibility of the disease (elevated A1c levels, BMI, depression, and greater therapy) (Liu et al., 2017). The topic was selected because of the stigma experiences that diabetic patients endure. Diabetes stigma is a negative social judgment based on various aspects of diabetes or its management. It may cause a diabetic person to feel excluded, rejected, or sustain a status loss. Healthcare providers should be aware of the stigma to assist their patients' maneuver through the situation and maintain their self-management of the disease. Depression . An individual diagnosed with Type 2 diabetes has a higher probability of developing depression (two to three times) (Badescu et al., 2016). The relationship between diabetes and depression is not understood by researchers (Badescu et al., 2016). Depression can lead to poor lifestyle choices, create complications, and poor health outcomes. A cross-sectional quantitative study conducted by Majumdar et al. (2021) in eastern India examined the predictors of depression, and its prevalence in 1371 (836 males and 535 females) Type 2 diabetic patients (Majumdar et al., 2021). The participants were individuals diagnosed with the disease for over one year and from nine hospitals and clinics. The nine-item PHQ-9 questionnaire and Beck depression scales were used to assess the participants’ depression. Fifty-six participants met the criteria for major depression and 494 patients for minor depression (Majumdar et al., 2021). The results showed that depression was significantly associated with younger age (18- 40 years versus > 60 years) [OR-2.09; 95% CI 1.11–3.96] (Majumdar et al., 2021). Suicidal ideation was noted in 201 patients (14.8%) with HbA1c higher than (12 vs 16.8%) (p =0.016) (Majumdar et al., 2021). The study concluded that there was a high prevalence of depression in Type 2 diabetic patients. Depression particularly affected younger adults, females, lower socio-economic, and poor compliance were factors that influenced them. The study reinforced the need for healthcare providers to screen their patients for depression during their clinical encounters. Comment by Author: Not listed as a subtheme

Hussain et al. (2018) conducted a quantitative, longitudinal study to evaluate depression as a co-morbidity condition and its prevalence in Type 2 diabetic patients in India. A meta-analysis was performed and searched for published studies in various databases. The modified Newcastle-Ottawa Scale was used to assess the methodological quality (Hussain et al., 2018). The prevalence of depression was the primary outcome, and the prevalence base on the demographic sub-group was the secondary outcome (Hussain et al., 2018). Forty-three studies included 10, 270 patients who met the eligibility criteria. The results showed a prevalence for depression was found to be 38% (95% CI: 31%–45%) (Hussain et al., 2018). The authors noted an increased presence of depression in the participants with increased diabetic complications odds ratio of 2.33, 95% CI: 1.62–3.36, p < 0.00001 (Hussain et al., 2018). The study concluded that there was a high prevalence of depression among the Indian participants. Health care providers should use depression screening tools to help Type 2 diabetic patients cope and develop better self-management strategies. A descriptive cross-sectional study conducted by Sathjanesan et al. (2018) determined the frequency of depression and its associated factors among Type 2 diabetics in a diabetic clinic in a Pakistan hospital. One hundred and ten patients participated in the study. Data was collected using a pretested structured proforma. The Beck’s Depression Inventory (BDI) scale was used to screen the participants’ depression. A chi-square analysis was used to evaluate the association between depression and its associated factors. The results showed 20 males (18.2%) and 90 females (81.8%) in the study. Sixty-one (55.5%) of the participants had depression (Sathjanesan et al., 2018). Depression was not associated with age (p < 0.174); however, 47 (77%) individuals >50 years of age had depression (Sathjanesan et al., 2018). The females experienced more mood swings and depression than males (Sathjanesan et al., 2018). Depression was significantly higher among participants with a spouse (70%) than those not living with a spouse (29.7%) with a statistical significance (p <0.002) (Sathjanesan et al., 2018). The study concluded that the prevalence of depression is high among this populace (Type 2 diabetics). It is essential that healthcare providers use, assess, and look for factors that could lead to depression among their patients so they can be improved (Sathjanesan et al., 2018). In conclusion, depression was selected because of the increased emphasis on mental health since the Coronavirus-19 (COVID-19) pandemic. Many individuals are suffering silently regarding depression and anxiety, and the primary care healthcare provider often is the first one with the opportunity to discuss the topic. Depressed individuals are less likely to maintain proper self-management and medication adherence. Hence, the need for clinicians to become knowledgeable and alert regarding the depression conversations with their patients. Diabetes distress. Psychological comorbidity is higher in individuals with Type 2 diabetes (Perrin et al., 2017). Research shows that 30% of diabetic patients experience this phenomenon (Perrin et al., 2017). Diabetes distress is an emotional state where a person experiences stress, guilt, or denial from living with diabetes and self-management (Kreider, 2017). It has been linked to poor health outcomes (Kreider, 2017). A systematic review was conducted by Perrin et al. (2017) to determine the prevalence of diabetic distress in Type 2 adult patients. A total of 55 studies were reviewed from seven databases. The authors performed multiple-fixed and random-effect meta-analyses to synthesize the data. The primary analyses determined the prevalence of diabetic distress with secondary meta-analyses and meta-regression to assess the prevalence among different variables (Perrin et al., 2017). The results showed that 36% diabetes distress was found in individuals diagnosed with Type 2 diabetes. The incidence of distress was higher in people with comorbidities or depressive disorders and females (Perrin et al., 2017). The study concluded that diabetes distress is a leading issue for Type 2 diabetics, females, and depressive symptoms (Perrin et al., 2017). The authors recommended that further research occur to understand better and treat this populace. A cross-sectional, descriptive survey conducted by Kintzoglanakis et al. (2020) in an urban city in Greece focused on adult ambulatory outpatients diagnosed with Type 2 diabetes to assess diabetes distress. Laboratory tests were collected, such as body mass index (BMI), waist circumference, HbA1c, lipid profile, creatinine, uric acid, and urine albumin creatinine. The frequency of diabetes distress was evaluated using a Diabetes Distress Scale for the clinical and sociodemographic connections among the patients. A Spearman’s correlation coefficient was used to test the correlations between continuous variables (diabetes distress levels, HbA1c values, age, and length of diabetes. Mann-Whitney U test and Kruskal-Wallis test were used to determine the relationships between the dichotomous variables (gender, smoking, insulin use, income, physical activity). The results showed that 135 participants had moderate to high levels of diabetes distress (r 0.50 to r 0.70) (Kintzoglanakis et al., 2020). The age range of the participants was 43 to 64 with a mean of 68.8. Age was correlated to increased level of emotional burden (r ¼ 0.203, p ¼ 0.019). Approximately eight percent of the participants suffered from the emotional burden, which correlated with younger individuals, insulin use, duration of insulin use, and the number of injections received daily (Kintzoglanakis et al., 2020). The results showed that an individual with a longer diabetes diagnosis significantly correlated with emotional burden and regimen distress (Kintzoglanakis et al., 2020). The study concluded there was a need for clinicians to screen their patients for diabetes distress. Jeong and Reifsnider (2018) conducted a cross-sectional, correlational descriptive study to evaluate the associations between diabetes-related distress and depressive symptoms with glucose control among Type 2 diabetic Korean Americans. One hundred and nineteen adults were recruited from Korean communities in Arizona. Data collected were a finger-stick glucose test for HbA1c, height, weight, and BMI. The Diabetes Distress Scale was used to measure the participants’ diabetes-related distress (Jeong & Reifsnider, 2018). Descriptive statistics on the demographics were used along with bivariate correlation and multiple linear regression analysis. The mean age of the participants was 67, with 70% of the participants being women (Jeong & Reifsnider, 2018). The results showed roughly 42% of the participants had poor glucose control (7% or higher). The mean score of the depressive symptoms was 12.55 (range 0 to 37), with 31% of a score of 16 or higher (Jeong & Reifsnider, 2018). The mean score of the diabetes distress was 2.03 (ranging from 1 to 4.24) with 14% of the participants reported high diabetes distress (r = 0.26, P < .01) (Jeong & Reifsnider, 2018). The study concluded that the clinical implications for healthcare providers were to be mindful in recognizing the various psychological aspects of Type 2 diabetes in developing patient-centered strategies for effective self-management and glucose support. The study limitation was where it was conducted only on first-generation Korean Americans. Hence, the study could not generalize its findings to the Korean American population in Arizona. Non-pharmacological indicators. There are many medications used for the effective management of diabetes (Raveendran et al., 2018). Effective non-pharmacological therapy should be explored with all Type II diabetics. The measures could include nutrition and exercise. Nutrition interventions are critical in a person with diabetes maintaining an optimal glucose level (80-120mg). The dietary pattern that must be encouraged is consuming fruits, vegetables, low-fat dairy foods, whole grains, and minimal red meat (Asif, 2014). Khazrai et al. (2014) study emphasized that food intake is associated with obesity. However, it is not just the volume of food but the quality of one’s diet. High ingestion of red meat, sugary items, and fried foods contributes to insulin resistance and Type II diabetes (Khazrai et al., 2014). People with diabetes should be educated regarding consuming fruits and vegetables in protecting them since they are high in nutrients, fiber, antioxidants, and a protective barrier against diseases (Khazrai et al., 2014). This topic was selected because educating Type II diabetic home healthcare patients regarding their dietary habits is an integral part of diabetes care. Failure to incorporate healthy eating habits along with medication adherence can lead to severe complications of the disease. Healthcare providers must teach home healthcare patients dietary guidelines according to their food selection, cultural, and personal preferences to change their eating patterns. Pharmacological factors. Type II diabetic patients typically take multiple medications for their condition and other comorbidities (Kirkman et al., 2015). Following one’s medication regimen and treatment improves patient outcomes, reduces healthcare costs, hospitalizations, and mortality (Kirkman et al., 2015). A retrospective study conducted by Kirkman et al. (2015) determined patient, medication, and prescriber factors that influenced diabetic patients and medication adherence. A sample size of 200,000 participants (from 50 states, including the Virgin Islands) was extracted from a pharmacy database (Medco Health Solutions). The participants' eligibility was based on the medication, benefits, and prescription history. Each patient was followed for one year from the medication date to post-implementation of the study. Medication adherence was described as a medication possession ratio > 0.8 (Kirkman et al., 2015). Logistic regression analyses were conducted to evaluate factors independently linked with adherence. The results demonstrated that 69% of the participants were adherent. Other findings illuminated that adherence was associated with one’s age (older), male, higher education and income, and the use of the mail order versus retail pharmacies. Individuals with a new diagnosis of diabetes were less likely to be compliant with their medication regimen.

The authors concluded that demographic, clinical, and system-level factors influenced the participants’ medication adherence regimen (Kirkman et al., 2015). The authors emphasized that younger individuals, newly diagnosed and had minimal medications to take, were at a higher risk for non-adherence. Individuals who used mail-order pharmacies resulted in higher medication adherence due to lower out-of-pocket costs (Kirkman et al., 2015).

Patient’s belief system. One’s culture influences a patient’s beliefs regarding medications, which ultimately affects their medication adherence (Lemay et al., 2018). This remains a challenge for healthcare providers in helping patients to understand the significance of medication adherence (Shahin et al., 2019). A study conducted by Shahin et al. (2019) used a systematic review to determine the importance of an individual’s cultural belief influenced medication adherence. A total of 2,646 articles were selected from various databases such as PubMed, CINAHL, EMBASE, and PsychINFO. Twenty-five of them met the inclusion criteria. The studies focus on diabetes or hypertension.
The study results from Shahin et al. (2019) revealed personal and cultural factors linked with medication adherence. Ten articles (40%) demonstrated an individual’s perception of the illness, five (20%) were affiliated with health literacy, four (16%) cultural beliefs, three (12%) self-efficacy, and five (20%) knowledge illness (Shahin et al., 2019). Shahin et al. (2019) study concluded that one’s cultural influences affect their perception of the importance of medication adherence. Healthcare providers must understand their patients’ pre-existing perspectives of diabetes before offering new information. This is an opportunity for healthcare professionals and patients to have a dialogue to diffuse misconceptions related to the patient’s perceptions. The authors suggested that future research should identify the religious beliefs associated with disease knowledge and medication adherence.

Healthcare providers and the relationships with patients. Patients usually consider their healthcare providers (HCPs) as the most dependable source of data regarding their health condition and treatment. Patients are highly likely to effectively follow the treatment plan when they are involved in having a good relationship with their HCP due to the confidence and trust that has been built over time. Relationship building in healthcare is a vital aspect in the day to day lives of healthcare practitioners due to the nature of their job, which necessitates that they maintain long-term relationships with their patients for enhanced medication and treatment outcomes (Heston, 2018).
Trust is critical to developing, specifically since patients can experience improve health-related outcomes when they value relationships with their HCPs. Patients who have trust in their HCP often believe that their provider has a high level of competence and truly cares about their health-related outcomes (Heston, 2018). Mistrust develops when the patients attain unrealistic, inconsiderate, or insensitive advice from their HCPs, as well as feel emotional distance from them.

Health literacy. Health literacy is described as one’s ability to obtain, communicate, process, and comprehend basic health information and navigate health services to make an informed decision (Sawkin et al., 2015). Medication adherence is broadly identified as a patient’s ability to follow a prescribed medical treatment (Sawkin et al., 2015). Researchers Glanz et al. (2015) have explored the impact of low health literacy rates on patient compliance with medications and health-related advice. The authors stated that approximately 35% of American adults possess basic or below basic health literacy levels (Glanz et al., 2015).
Chima et al. (2020) conducted a systematic review to evaluate the impact of health literacy and medication adherence. Literature searches were performed using Ovid Medline, CINAHL, EMBASE, Scopus, and PsycInfo. The inclusion criteria for the articles were conducted in the United States, 18 years or older with a diagnosis of Type I or II diabetes, medication adherence was an outcome variable, quantifiable measure reported, and was a full text journal article. Articles were graded using Joanna Briggs Institute Critical Appraisal Checklists, which is appropriate for the respective study designs identified. Thirteen articles were retained in the review, most of which used a cross-sectional design.

The results demonstrated four of the 11 studies found a positive association between health literacy and medication adherence (Chima et al., 2020). Two of the four studies had methodological shortcomings. The authors concluded there was some evidence that health literacy is linked with medication adherence among diabetic adults in the United States. Recommendation for future research to design and execute longitudinal studies to determine a deeper relationship between the variables (health literacy and medication adherence (Chima et al., 2020).

Given inadequate literacy rates, among members of the general population, world practitioners continue to create unique strategies that can be used to reduce lacking health adherence among patients with diabetes. Improved literacy is a theme that should be of the utmost priority, specifically since it creates the foundation for long-term sustained profitability. Furthermore, as patients can understand the importance of medication compliance, adherence to medication regimens improves (Glanz et al., 2015).

Using universally implemented and published resources that can improve medication adherence is important. Tools and resources can be utilized by HCPs to identify patients who are not taking their prescribed medications. Prescriptions need to be taken seriously for exceptional results and for the continued well-being of patients who have critical illnesses like diabetes.

The use of simple language by HCPs, as well as by medication manufacturers, can encourage providers to meet patients where they are and utilize teach-back techniques to ensure a patient’s understanding of his/her prescribed medication regimen. Teach-back methods have been utilized to enhance medication adherence among many types of non-adhering patients. Most of the time people opt to not take their medication as they cannot read all the instructions written on the medicine and are afraid that they will die, especially in the cases that they mistake those drugs for poison or some drug that may look like a famous poison causing death. This is a key issue that has left most of the people victims of non-adherence (National Academies of Sciences, Engineering, and Medicine, 2018).

Huang et al. (2020) conducted a cross-sectional study aimed to identify patient factors linked with diabetes medication adherence and health literacy levels. One hundred and seventy-five participants were involved in the study and recruited from two family medical clinics. All the participants were over the age of 20, diagnosed with Type II diabetes, taken one oral diabetic medication, and understood English. The authors evaluated the participants’ health literacy levels using the Newest Vital Sign, a six-item questionnaire, and an eight-item Morisky Medication Adherence Scale.

The results showed a self-reported status of (β = 0.17, p = 0.015) and medication self-efficacy (β = 0.53, p, 0.001), which were positively associated with diabetes and medication adherence (Huang et al., 2020). Health literacy was neither associated with diabetes medication adherence (β = −0.04, p = 0.586). The authors concluded that health literacy measured using the Newest Vital Sign did not correlate with medication adherence or glucose control among Type II diabetics. They recommended that healthcare clinics develop interventions to improve their patients’ self-efficacy of medication to improve the medication adherence rates (Huang et al., 2020).

In conclusion, the subject was selected because of its importance in helping Type 2 diabetic patients sustain self-management of their disease. It is imperative that clinicians do not assume that the patients understand the instructions and can read. Utilizing the teach-back method could ensure the patient’s knowledge and comprehension level of the material given to them. Healthcare providers should use a health literacy screening tool to guarantee to instruct the patient on their literacy level.
Socioeconomic Factors
Socioeconomic-related factors that affect medication adherence include one’s location of residence, medical costs of treatment, and finances (Yeam et al., 2018). Other factors that could influence medication adherence are low health literacy, education level, lack of social support, living conditions, and medication costs (Hennessey & Peters, 2019). Health care providers must conduct a thorough assessment before providing a patient the prescription and consider any of the factors as mentioned above.

Medication costs. Kang et al. (2018) conducted a quantitative, longitudinal study to examine factors that affected cost-related medication nonadherence. Cost-related medication nonadherence (CRMN) is defined as taking medication then indicated or prescribed due to costs (Kang et al., 2018). Unknown sample size noted, but the Behavioral Risk Factor Surveillance System data for 2013–2014 was used to identify individuals with diabetes and their CRMN. Weighted multivariable logistic regressions were used, and analyses were conducted using the Survey suite of programs in Stata SE version 14. The survey weights were used to obtain population-level estimates and subpopulation methods to estimate standard errors for the subgroup’s analyses (Kang et al., 2018).
The results demonstrated that CRMN among American adults was 16.5% (Kang et al., 2018). Individuals with an annual income of < $50k and without health insurance had the highest rates of CRMN. Insulin users had a 1.24 times higher risk of CRN than those not using insulin. Factors influencing CRMN were diabetes care and lifestyle factors, depression, arthritis, and asthma (Kang et al., 2018). Health insurance was the most significant factor for the participants < 65 years of age and depression for respondents > 65 years (Kang et al., 2018).

The authors (Kang et al., 2018) concluded that one’s annual income and health insurance status were the most significant factors for younger adults, while depression was for older adults > 65 years. When the younger and older groups were combined, it showed the largest impact of CRMN affecting individuals < 55years of age and having higher rates of non-medication adherence (Kang et al., 2018). Recommendations were for healthcare organizations to develop policies, resources, and support systems that address the factors to help improve CRMN. Social Support. Various factors impact medication adherence. However, Linni et al. (2015) emphasized that social support must be considered a core component in interventions that improve the management of Type II diabetic patients. The social support theory has three components a) subjective support (emotional experience and fulfillment of the individual being respected and understood; b) objective support (direct material help from the social network in the communities; c) support utilization (various support strategies from family, friends, and colleagues) (Linni et al., 2015; Shao et al., 2017). A quantitative study conducted by Linni et al. (2015) determined whether social support was linked with medication adherence in patients with Type II diabetes. The study was conducted in a Beijing hospital with a random sampling of 412 participants with Type II diabetes. The adult patients’ assessment of their social support was retrieved from medical records and self-reported surveys (Social Support Rate Scale 14-item questionnaire). The support scale measured objective, subjective, and support utilization. The Chinese version of the Morisky Medication Adherence Scale, eight-item, was translated for the participants to complete. Three hundred and thirty participants completed the self-report measure medication adherence six months after the initial data collection. A t-test demonstrated a significant difference in social support between the low and high medication adherence groups (t = -2.11, p= 0.036) (Linni et al., 2015). A regression analysis was used to determine the subscales of the support, which presented statistical significance and association with medication adherence (β = 0.29, p = 0.011), rather than another two subscales of subjective (β = −0.02, p = 0.80) and objective support (β = −0.04, p = 0.33) (Linni et al., 2015). The authors concluded that social support was a critical factor in improving medication adherence in diabetic patients. It must be impressed on this population to have open attitudes to receiving help from friends, family, and outside organizations. A quantitative, longitudinal study conducted by Shao et al. (2017) determined the impact of social support and medication adherence among 532 Chinese patients from an outpatient and inpatient endocrine clinics. The authors used the ten-item Social Support Rating Scale for data collection related to social support. It measured the three components of social support (objective, subjective, and support utilization). A six-item self-efficacy scale was used to measure (emotional control, communication with physicians, symptom management, role function, and perceived adaptability to chronic diseases). Shao et al. (2017) developed a 13-item adherence scale that was divided into three subscales a) Do you take the medicine every day according to the doctor’s advice? b) Do you take the dosages according to the doctor’s advice? c) Do you take the medication on time? Data were collected and entered EpiData 3.1 software (Shao et al., 2017). A Pearson’s correlation coefficients were calculated to evaluate the pairwise associations between the social support scores, self-efficacy, and adherence (Shao et al., 2017). The descriptive data showed the participants were mostly older females. The coefficients for the three components were statistically significant demonstrated the goodness-of-fit indices (χ2 = 2 47, P = 0 12; GFI = 0 99; AGFi = 0 98; CFI = 0 98; and RMSEA = 0 05) (Shao et al., 2017). In summary, Linni et al. (2015) and Shao et al. (2017) utilized an adequate number of participants for their quantitative studies. They used the same support rating scale, which validated their findings. The key difference is that the studies were conducted in various settings (hospital and endocrine outpatient/inpatient clinics). In conclusion, the studies validated the role of social support in managing Type II diabetic patients. Hence, it must be considered as a key component in any intervention a healthcare provider develops to improve self-managing and glycemic control (Linni et al., 2015; Shao et al., 2017). Interventions Using tools and instruments that are considered effective and appropriate is vital in supporting adherence in different ways and in achieving self-efficacy among the various patients. Positive family and social support are vital aspects associated with adherence to the issue of diabetes management (Rodríguez-Saldana, 2019). The engagement of family members can enhance self-care activities for patients suffering from diabetes, including eating effective and healthy foods, keeping fit, monitoring blood glucose, and adhering to medication. A web-based portal is an innovative resource that can be used to assist patients. This web-based portal can improve medication reconciliation processes among patients and providers. The web-based portal can help patients with various regimens navigate challenges. Furthermore, this medication information, available through the portal can help individuals understand medication requirements, as the portal often helps to clarify and verify inaccuracies. The web portal aims to enhance medication adherence and prevent the improved use of the medication (Forman & Shahidullah, 2018). When patients can verify information in their electronic medical records to ensure proper medication adherence, this can enhance patient well-being. The EMR provides an accurate list of a patient’s medications and provides detailed medication information (e.g., type of drug, what the drug is used to treat, frequency of drug use, etc.). Also, the use of screening tests is vital in understanding how well patients are taking their drugs. If there is no consistency in medication-taking then motivation aspects should be utilized to enhance adherence (Eskola et al., 2017). Medication Adherence Project (MAP). The MAP resources were introduced, developed, and implemented by the New York City Department of Health and Mental Hygiene in response to clinicians and pharmacists working in primary care practices (Starr & Sacks, 2010). It serves patient populations impacted by several chronic diseases (Starr & Sacks, 2010). The resources provide practical tools to help practitioners communicate with patients related to medication adherence. It consists of a training course and toolkit that was piloted and assessed by doctors, nurses, pharmacists, medical assistants, nutritionists, and healthcare educators (Starr & Sacks, 2010). The objectives of the tool are to acquaint healthcare providers with the obstacles associated with medication adherence with individuals who have chronic diseases: Other aspects include a) evidence-based solutions that improve adherence, b) educate healthcare providers to engage in conversations regarding medication taking, c) help practitioners to combine the tool into the clinical practices and quality improvement methods, and d) help providers train their peers to use the resources effectively (Starr & Sacks, 2010). Patient-Centeredness Care. Patient-centeredness entails ensuring that all the identified interventions described in the first theme are focused on the individual patient who is being helped to effectively adhere to the given medication during home care settings. Patients who have been diagnosed with various critical illnesses and have been asked to go home for home-based care have been associated with poor adherence to the medications they are given when they are discharged from the hospital (Steinberg & Miller, 2015). Practice recommendations, whether they are focused on evidence or expert opinion, are intended to offer the desired guidance on an overall approach to care (da Costa et al., 2018). The science, as well as the art associated with medicine, usually come together when the identified clinician is experiencing or has experienced some sort of situation whereby, they must make treatment recommendations for any patient who would be considered to not have effectively met the eligibility criteria for the studies on which the given guidelines were based. Patient Advocacy. Advocacy is a vital aspect in healthcare since it addresses the needs of the patient who need the utmost help and care, thereby allowing them to go back to their previous health state (D’Onofrio et al., 2018). Advocacy is an aspect that can be referred to as active support, as well as engagement, that aims to effectively develop a cause as well as a policy (Mollaoglu, 2018). Furthermore, advocacy is usually needed to enhance the lives of individuals suffering from diabetes. The various issues that diabetic patients experience, such as obesity, physical inactivity, and societal challenges reinforce the need for advocacy (Firstenberg & Stawicki, 2017). Clinician’s Cultural Awareness. Healthcare professionals in the current era facilitate the delivery of care for an increasing number of culturally and linguistically diverse patients. Clinicians need to be culturally aware and competent to deliver high-quality care. Cultural competence is defined as one’s ability to provide practical, quality care to patients who have diverse attitudes, values, and behaviors (Agency for Healthcare Research and Quality [AHRQ], n.d.). This type of practice requires healthcare systems and settings to personalize patients' healthcare according to their cultural and linguistic differences (AHRQ, n.d.). It also requires clinicians to understand cultural differences' impact on healthcare delivery (AHRQ, n.d.). Cultural competency is an accepted standard of care for meeting diverse and vulnerable patient populations (Narayan, 2019). The author suggests that home health nurses could enhance their cultural competence by working on their implicit biases, practicing mindfulness during patient encounters, and practicing patient-centered care (Narayan, 2019). A qualitative study conducted by Kaihlanen et al. (2019) explored nurses’ perceptions about the content and utility of cultural training and increasing their cultural knowledge. A sample size of n=14 registered nurses from six different hospital units in Finland participated in the study. The methods used included semi-structured, small group interviews were conducted to examine their perceptions. The codes showed three categories: general utility of training, the personal utility of the cultural competency training, and utility training for the patients (Kaihlanen et al., 2019). The general utility was related to the general manner the training provided regarding cross-cultural care. Personal utility pertained to realizing one’s cultural features, changing the way of thinking, and receiving a new perspective that would allow workable practices. Utility for patients was related to the nurse’s fostering awareness and acknowledging cultural differences while respecting them. The authors concluded that cultural competency training for these nurses was viewed as thought-provoking (Kaihlanen et al., 2019). Increasing one’s awareness facilitates better communication between healthcare providers and patients (Kaihlanen et al., 2019). A study conducted by Li et al. (2017) focused on the communication between healthcare providers and patients. The study analyzed verbal and nonverbal barriers towards maintaining effective communication with limited English-speaking patients. The issue occurred in three areas a) continuing education for healthcare providers, b) certification of healthcare interpreters, and reimbursement for language services for Medicaid participants (Li et al., 2017). The authors concluded that two new strategies be added, such as increasing the healthcare providers' awareness of verbal and nonverbal barriers to cross-cultural communication. The second strategy was to increase the multicultural competencies of the clinicians (Li et al., 2017). A quantitative study conducted by Liu et al. (2018) evaluated a workshop designed to increase cultural competence among Chinese undergraduate nursing students. A sample size of 40 nursing students (n=40) participated in the study. The participants attended a one-day cultural workshop provided by the authors in groups of eight to 12. The workshop segments included self-reflective activities, debriefing, cross-cultural simulation games, and debriefing. A one-group pretest and posttest were used to analyze the results (Liu et al., 2018). A paired t-test evaluated the cultural competency scores. The results showed the nurses’ scores after receiving classes increased significantly (p < .001), components of cultural awareness (p = .003), cultural knowledge (p < .001), cultural understanding (p = .007), and cultural skills (p < .001) (Liu et al., 2018). The study concluded that the nursing students were satisfied with learning new material. The results supported the need for nursing students to all practicing nurses to understand, learn, and practice cultural competency. The studies Kaihlanen et al. (2019) and Li et al. (2017), although conducted differently (quantitative and qualitative), showed the importance of nurses adapting cultural competency and developing awareness for their patients. Kaihlanen et al. (2019) focused on home health registered nurses' perspectives on learning cultural competency. Li et al. (2017) concentrated on a different aspect of cultural competency; learning the patient’s non-verbal and verbal cues that could affect the patient-provider interaction. In conclusion, the subject of cultural competency was selected because home healthcare nurses must understand that culture plays a large part in patient care. Home care is different since the patient controls the environment of their home. Cultural background, values, and beliefs must be considered in implementing an appropriate plan of care (Crivello, 2021). Home health nurses must comprehend patients within the same culture could have differing opinions and beliefs to deliver patient-centered care. Each clinician should individualize the patient's care. Comment by Author: This literature review is very confusing. The themes and subthemes that you have mentioned earlier are not what is down in your literature review. It is difficult to ascertain which is a theme and which is a subtheme due to the incorrect formatting. Summary Comment by Author: Your summary needs to include a synthesis of all three of your themes with citations to substantiate your comments. The prevalence of Type II diabetes is affecting one in ten Americans (Ahmed et al., 2018). The disease is expected to continue rising higher by 2030 (Lin et al., 2018). Medication adherence for Type II diabetic home health patients is critical in decreasing the poor patient outcomes associated with the disease. Medication adherence with Type II diabetic patients remains a challenge for many healthcare professionals. Education for the healthcare providers and the patients can make a difference in this population’s lives. Chapter 2 discussed reintroduced the topic and presented the theoretical framework and change model to guide the project. Other sections include the literature review related to patient-related, socio-economic, and health system factors. A summary of the chapter was provided with an introductory sentence that previews Chapter 3. Chapter 3 reinstated the selected topic. Other segments presented the project’s methodology, design, population, and sample selection. A description of the MAP resources and the electronic medical record (EPIC) are provided. The validity and reliability of the instrument was demonstrated along with the data collection and analysis procedures, potential bias. The last few sections discuss the ethical considerations, limitations, and a summary that leads into Chapter 4. Chapter 3: Methodology Medication adherence is a critical component in minimizing adverse patient-related outcomes among individuals with chronic illnesses (Type II diabetic patients). Ahmed et al. (2018) stated that medication adherence refers to how a home healthcare patient can correctly take their medications in the absence of their healthcare providers. Individuals looking to adhere to their medical regimens must follow all instructions provided by their physicians (Bellou et al., 2018). In the United States, one in ten people has type II diabetes (Ahmed et al., 2018). The prevalence of the disease is expected to increase by 2030 because of the growing number of older adults and the aging of the population (Lin et al., 2018). Due to the growth of home health services, there is now a need for education regarding medication adherence. Approximately 45% of patients cannot maintain their glucose levels in the home health care setting (Polonsky & Henry, 2016). Poor medication adherence is associated with higher financial obligations to patients, hospitals, and insurance companies. Polonsky and Henry (2016) emphasized that adverse outcomes cause frequent hospitalizations and lower quality of life for patients and their families. Chapter 3 reestablished the selected topic. Other sections of the chapter included the problem statement, clinical question, project methodology (quantitative), and project design (quasi-experimental). The chapter described the population and sample selection, the instrumentation (MAP resources), validity, reliability, and data collection procedures. The last few segments comprised the data analysis procedures, potential bias, ethical considerations, limitations, and a summary. Statement of the Problem It was not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients. The targeted population was Type II diabetic patients in an urban healthcare facility in urban Texas. The primary investigator collaborated with the facility’s stakeholders; it was noted that medication adherence among the patients was lacking. The information was obtained from the electronic medical records, which showed that ten percent of the diabetic patients were not adhering to their medication regimen. Factors that influence poor medication adherence are many and include poor knowledge or awareness of the disease, medication costs, and lack of understanding of the medication treatment, which reinforced the project’s purpose (Heath, 2019; Sharma et al., 2020). Healthcare providers play an essential role in assisting patients with medication adherence. The primary investigator will introduce a standardized strategy for the facility’s healthcare providers to assess the patients’ medication adherence using MAP resources (Starr & Sacks, 2010). Using a standardized method helped to solve the facility’s problem with medication adherence rates. It also improved the healthcare providers' knowledge levels and awareness regarding the barriers associated with medication adherence. Complying with the new guidelines developed by the Centers for Disease Control and Prevention (2020) could help patients control their glucose levels, minimize healthcare costs, hospitalizations, and potential infections. Clinical Question The clinical question that directed the primary investigator’s answer was: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas? The independent variable was the MAP resources. The dependent variables were the medication adherence rates. The data collection process will not begin before approval is received by Grand Canyon University IRB. The primary investigator developed informational flyers for the nurses to give their patients during their home health visits. The staff answered questions related to the project regarding risks, benefits, and purpose while instructing that participation is voluntary. A convenience sample was used because of the easy access to the participants for the primary investigator. The primary investigator collected data (four weeks) prior to implementation of the project. The data will be collected from the electronic medical records using Cradle Solutions for the impact of the MAP resources and medication adherence rates. In the first week, the primary investigator will educate the staff to use the MAP resources. Once the staff begins to implement the tool, post-medication rates will be assessed post-four weeks. The data was inserted into a Microsoft Excel 2016 codebook developed by the primary investigator. It was exported into SPSS-27 and analyzed by using a chi-square test. The five-item demographic survey will collect the descriptive statistics of the home healthcare patients. The questionnaire comprised of (age, gender, years with Type II diabetes, oral or insulin, and education). The level of measurement of the independent variable was nominal. The chi-square analysis was the appropriate test to identify the scale of measurement of the variables. The interval scale of measurement was for the dependent variable (XXXX) that had a nominal scale, which used a nonparametric test to answer the clinical question. Project Methodology The primary investigator selected a quantitative methodology for the quality improvement project. It was the most appropriate method because the numerical data was being analyzed. The numerical data were presented using graphs, charts, and tables (Creswell & Creswell, 2018). The data collection involves one or more of the following, such as surveys, tests, or questionnaires (Leedy & Ormrod, 2020). The quantitative method uses test statistics to determine if the hypotheses are valid by evaluating the observed relationship between the variable (Leedy & Ormrod, 2020). The statistics are summarized in mean, mode, and standard deviations with the p-value to conclude the statistical significance (Creswell & Creswell, 2018). Utilizing this methodology was cost-effective, easy for future projects to replicate, and generalize similar populations. The qualitative methodology was not selected because it explored the patient’s lived experiences, behaviors, and perspectives related to a phenomenon (Creswell & Creswell, 2018). Qualitative data is collected via semi-structured interviews, one-on-one interviews, and focus groups (Creswell & Creswell, 2018). In this project, the primary investigator did not consider the participants behaviors and experiences related to medication adherence. Project Design Quasi-experimental designs are frequently defined as non-randomized, pre-post intervention studies (Leedy & Ormrod, 2020). The design was selected to assess the benefits of the proposed intervention. Using a quasi-experimental design allowed the primary investigator to determine the causal relationship between the intervention and patient outcome (Leedy & Ormrod, 2020). The design was specific for pre-intervention and post-intervention measurements to a non-random selected group. A correlational design was not appropriate for the project. According to Leedy and Ormrod (2020), this design is descriptive, relying on a hypothesis. The findings from a correlational design can determine the prevalence and relationship among the variables. It can also allow one to forecast events taken from the data (Leedy & Ormrod, 2020). In this project, the primary investigator did not seek to understand the relationships between the independent variable (MAP resources and education intervention) and the dependent variable (medication adherence rates). The targeted population was home health patients ages 35 to 64 years old. The selected site serves approximately 100 patients annually. The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria comprised of individuals with language or cognitive deficits and diagnosed with Type I diabetes. Five registered nurses helped to implement the project. They were individuals who worked full-time and had been employed for over a year. The data collection process began once approved by Grand Canyon University IRB. Recruitment occurred from informational flyers given to the patients during their home health visits with the providers. The nurses answered questions regarding the project's risks, benefits, and purpose and were instructed that the participation was voluntary. The primary investigator used a convenience sample because of the access to the participants. Data was collected four weeks before project implementation from the electronic medical records (Cradle Solutions) (medication adherence rates). In the last portion of the first week, the primary investigator educated the healthcare providers regarding using the MAP resources. The staff began implementing the tool, and the post medication adherence rates will be assessed four weeks post-implementation. The primary investigator documented the data in a Microsoft Excel 2016 codebook developed by self. Once completed, it was exported into the SPSS-27 and analyzed using a chi-square analysis. A five-item demographic questionnaire was used for descriptive statistics of the population. The survey included (age, gender, years with Type II diabetes, oral or insulin, and education). Pre-intervention and post-intervention data were obtained using the MAP resources. The questions analyzed were: (1) “Have you experienced any increase in thirst?” (2) “How often do you urinate?” (3) “Do you often feel fatigued even when doing little tasks?” and (4) “Do you experience blurred vision?” In addition to the questions, home healthcare providers will ask the patient “Are you taking your medications?” Any information attained from the question and due to probing, observation of patient’s medications, and patient-related medication adherence was documented in the project site’s EHR. The data was analyzed using an independent t-test to determine the statistical significance. The electronic medical record used to collect data was Cradle Solutions, software for home health companies. It serves the specialized needs of home health care providers that give a web-based point-of-contact information entry and management (Cradle Solutions, 2021). It complied with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solutions, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator obtained the measurement of the medication adherence rates and aligned it with new protocols and guidelines developed by the facility. Population and Sample Selection The specific population addressed were adult home health patients ages 35 to 64 years old. The primary investigator chose this population because of the prevalence of Type II diabetes rising in children, adolescents, and young adults in the United States (12:100000) (Centers for Disease Control and Prevention, 2020; Kao & Sabin, 2016; Reinehr, 2013). The selected site serves approximately 100 patients annually, and 30 patients are diagnosed with Type II diabetes. The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria are individuals with language or cognitive deficits and diagnosed with Type I diabetes. Five female staff nurses were trained to help implement the quality improvement project. They were registered nurses, worked full-time, and had been employed with the facility for over one year. The geographic location of the project was in an urban area of Texas. The county statistics showed that approximately 17.6% of the population has Type II diabetes (Houston, 2021). During 2016-2018, 20.2% of the population was hospitalized because of diabetic complications (Houston, 2021). To determine the minimum sample size for the project, a G-Power* analysis was conducted. The primary investigator used version 3.1.9.2, an alpha measure of 0.05, a large effect size of 0.5, a power of 0.80, and one degree of freedom. The projected sample size was 34 (n=34). The project sample size was under met (n=30). During the informed consent process, the principal investigator explained to the nurses the project's purpose, risks, and benefits. The participants were informed that participation was voluntary, and they could withdraw without repercussions to their careers or personal lives. The project participants were not compensated. The primary investigator did not use the participants’ names or other identifying information during the project to protect the participants’ identities. For security reasons, each participant was assigned a random number. The primary investigator adhered to the University’s Institutional Review Board guidelines and the recommendations of the Belmont Report (justice, respect for persons, and beneficence) (U.S. Department of Health & Human Services, 2018). Hard copies of the data were stored on a flash drive at the home office of the primary investigator (in a locked cabinet). Data files will be stored on the primary investigator's laptop, which is digitally protected. The data will be stored for three years according to Grand Canyon University procedures (June 2024). Upon completion of the project and meeting all requirements, the primary investigator will shred the information using Iron Mountain shredding services and ERASER software on the laptop. Instrumentation or Sources of Data The instrument used in the project was the MAP Toolkit and Training Guide resources, which includes (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list (Starr & Sacks, 2010). The questions to ask poster encourages patients to ask the provider about their medication. For this project, the nurses will review the medications with the Type II diabetic patients. Six questions will be asked (1) Why do I need to take this medicine, (2) Is there a less expensive medicine that would work as well, (3) What are the side-effects and how can I deal with them, (4) Can I stop taking any of my medicines, (5) Is it okay to take my medicine with over the counter drugs, herbs, or vitamins, and (6) How can I remember to take my medicine? (Starr & Sacks, 2010). The second section, the Adherence assessment pad, explored the barriers to the patient’s maintaining medication adherence (Starr & Sacks, 2010). The questions include (1) makes me feel sick, (2) I cannot remember, (3) too many pills, (4) costs, (5) nothing, and (6) other (Starr & Sacks, 2010). The third component is my medication list. It provides detailed information in chart form, which is discussed between the patient and the healthcare provider (Starr & Sacks, 2010). It comprises of (1) name and doses of my medicine, (2) this medication is for my diabetes, (3) when do I take and how much (options include: morning, noon, evening, or bedtime), and (4) I will remember to take my medicine (a blank that will be filled in) (Starr & Sacks, 2010). The source of data for this project was the electronic medical record. It complied with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solutions, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator measured the medication adherence rates and aligned them with new protocols and guidelines developed by the facility. Validity Validity conveys how accurately a method is measured (Creswell & Creswell, 2018). If the method measures what it should and the findings correspond closely, it is considered valid. There are four types of validity are constructs, content, face, and criterion (Creswell & Creswell, 2018). For this project, construct and face validity was applied to the instrument. A group of professionals developed the tool, which comprised physicians, pharmacists, nurses, and medical assistants (Starr & Sacks, 2010). It was based on their years of work experience in their perspective fields. The toolkit’s improvements were adjusted and in alignment with the CDC and other healthcare governing bodies. Altman et al. (2018) stated hospital electronic health records are frequently being used in research studies. Many studies report validity using Cohen’s kappa, which measured performance and specificity (Altman et al., 2018). In another study conducted by Goulet et al. (2007), a robust correlation was found (between .86 and .99) for measures obtained from the patient’s electronic medical record and compared in a manual review. Reliability Reliability refers to the consistency of an instrument measuring something (Creswell & Creswell, 2018). If the same results occur regularly using the same procedures under the same conditions, the measurement is reliable (Creswell & Creswell, 2018). For this project, the MAP toolkit reliability was confirmed by inter-rater reliability (Starr & Sacks, 2010). The observers noted the same results associated with using the instrument, which aligned with the literature findings regarding collecting data for medication adherence rates. A study conducted by Harrell (2017) occurred over 90 days, where weekly medication adherence rates were assessed. Seventy-eight percent of the patients did not adhere to their prescribed medication regimen before the study's implementation. Post three-months of the project, 56% of the patients improved regarding medication adherence rates. For this project, test-rest reliability was noted, because the nurses used the MAP toolkit over time (two different times) (Creswell & Creswell, 2018). Electronic medical records are considered a reliable and valid source for data collection. A study conducted by McGinnis et al. (2009) examined EMR and written records. The results demonstrated the EMR-based data validity was shown to be moderate to excellent, with Pearson r correlations ranging from .875 to .99 for EMR and documentation records (McGinnis et al., 2009). Electronic medical records are considered a reliable source of data, as emphasized by Goulet et al. (2007), found strong agreement (Kappa between .86 and .99) and high sensitivity and specificity (≥.95) for quality measures based on electronically abstracted structured data compared with manual review. Data Collection Procedures The data collection process began once approved by Grand Canyon University IRB. Recruitment occurred from informational flyers given to the patients during their home health visits with the providers. The nurses answered questions regarding the project’s risks, benefits, and purpose and were instructed that the participation was voluntary. The primary investigator used a convenience sample because of the accessibility to the participants. The goal was to attain approximately 34 participants (n=34). Five home healthcare nurses were trained to implement the project. Training sessions were offered twice so that the nurses working on the weekends could participate. The primary investigator offered two 60-minute Zoom training sessions. During these sessions, the primary investigator provided information regarding using the MAP toolkit and resources. A 10-minute PowerPoint presentation was included during the 45-minute training session, along with a MAP toolkit binder for each participant. The nurses educated the participants regarding the purpose of the informed consent and its contents. The participants were informed regarding the benefits, risks (minimal), and purpose of the project. The potential risk (minimal) was related to emotional circumstances such as the stigma of the disease, anxiety, or depression. The participants were instructed that if they felt increased anxiety, depression, or embarrassment during the project, they could withdraw without any reason, or the project would end immediately. They were directed to a primary care physician or professional who could further help them. There was a slight chance that the hard copies (demographic and MAP surveys could be lost. To ensure that this did not occur, the primary investigator used a digitally password-protected laptop to protect their privacy. The participants were informed that data was kept in a password-protected folder on the laptop, accessible only to the primary investigator. The nurses collected the signed informed consents and returned them to the primary investigator after their visits. The primary investigator collected them daily during the project. The primary investigator protected participants’ rights and welfare by adhering to the Belmont report principles of a) justice, b) respect for the individual, and c) beneficence (Office for Human Research Protections, 2016). The primary investigator adhered to Grand Canyon University’s IRB guidelines. By providing fair treatment to all participants, the primary investigator upheld the principles of justice. There was no exploitation of this population or manipulation of their health conditions or diseases by the participants. Participants were treated as autonomous individuals to demonstrate respect for them. All the participants were treated using ethical conduct by respecting their answers and decisions, thus protecting them from harm. Hence, this allowed the primary investigator to abide by the beneficence guidelines. The primary investigator worked with the information technology department, ensured that the three MAP resources were incorporated into the Cradle Solutions documentation application. As part of week one, nurses provided the patients with informed consent, answered questions regarding the project, conducted a five-item demographic survey, and administered a pre-MAP survey. In the second to fourth week, the nurses examined the patient’s medication list and adherence (ten minutes). Each week, nurses documented medication adherence information in the patient’s electronic medical record. All nursing staff input was completed by the end of week four. The system was updated if the patient stated that they did not adhere to the medication regimen. The primary investigator collected post-test scores regarding medication adherence rates. The results were entered into a Microsoft Excel 2016 codebook developed by the primary investigator to analyze the data. The data was exported to SPSS-27 and analyzed based on a chi-square test. To maintain the confidentiality of data, hard copies of the demographic and MAP surveys were stored in a locked cabinet in the primary investigator’s home. The results were saved on the primary investigator’s digitally password-protected laptop. To ensure additional cyber-security, an encryption program was installed. Under Grand Canyon University’s Institutional Review Board guidelines, the data will be kept for three years (June 2024). The primary investigator will clean the laptop data using ERASER (a computer program) and Iron Mountain shredding services (Eraser, 2020). Data Analysis Procedures This quality improvement project aimed to address medication adherence among Type II diabetic patients who received home healthcare services. Information was obtained from the electronic medical records (Cradle Solutions), which indicated that ten percent of diabetic patients did not adhere to their medication regimens. The data for both the comparative and implementation patients were collected from the EMR after the four-week implementation period, and a PDF report was sent via encrypted email to the primary investigator. The dependent variable (medication adherence rate) was manually entered into a secure Microsoft Excel file (2016) for the comparative and implementation patients. All data collected was in numerical values. A unique identifier was assigned to each patient to facilitate data organization. The medication adherence rate was a nominal-level variable with two mutually exclusive options (adherent or non-adherent) for each patient. It was analyzed using a chi-square test, which is the most appropriate test for comparing two independent groups on a dependent categorical variable (Schober & Vetter, 2019). The patient groups are independent as patients in the comparative group (four weeks before implementation) were not matched for the implementation group. The project analysis employed a chi-square test, which aligned with the project design. The test compared group differences when the dependent variable was measured at a nominal/categorical level (Schober & Vetter, 2019). To address the clinical question, the medication adherence rate for 30-days before and 30-days after implementing MAP resources was compared. Additionally, a chi-square test was conducted. The chi-square test allowed for a comparison of the medication adherence rate for patients 30 days before and 30 days after the implementation, thereby answering the clinical question. The level of significance was set to .05, indicated a p-value of less than .05 would reveal statistical significance. Data were organized using a Microsoft Excel file (2016) containing a unique identifier for each patient. The quality department staff collected information regarding medication adherence and entered it manually into an Excel file as a categorical variable with numeric codes representing 0 for non-adherent patients and 1 for adherent patients. Data was imported into IBM SPSS version 27 after data entry was completed in Microsoft Excel. To ensure that the data was correctly prepared for analysis, a preliminary analysis of all variables was performed to identify any missing data or inaccuracies in the dataset. A frequency count was conducted for variables to ensure no missing data or values outside of the range of 0 to 1 for medication adherence rates. Potential Bias and Mitigation The internal validity is related to the extent the primary investigator can be confident that the cause-and-effect relationship found cannot be explained by other factors (Leedy & Ormrod, 2020). Thus, the conclusions of this project are credible and trustworthy (Leedy & Ormrod, 2020). The external validity of the project was affected by two factors: the maturity of the participants and the instrument (MAP resources). Participants' maturity may be impaired by their recollection, poor memory, or follow-through. The outcomes of the project would vary over time, affecting the results. To reduce this occurrence, one method was to have the participant complete the survey at the best time for them. For example, if an individual is a morning person (have them take the survey in the morning versus the afternoon or late evening). The second factor is the instrumentation process (MAP resources). The primary investigator educated the nurses regarding providing the participants the same time (30 minutes) to ensure the same measures were used during the pre-implementation and post-implementation phases. Bias is defined as any tendency that prevents an impartial discussion of a clinical issue (Pannucci & Wilkins, 2010). This could occur at any stage of the research process, including study design, data collection and analysis, and publication (Pannucci & Wilkins, 2010). A possible bias may arise from the selection process. The primary investigator avoided bias by selecting individuals and using strict inclusion and exclusion criteria that were previously developed for the project. Participants were drawn from the specified population. The second bias was related to recall bias, a systematic error that occurs when participants do not accurately remember previous events or experiences (Creswell & Creswell, 2018). The project could be affected because the participants are self-reporting to the nurses using the MAP resources. To prevent this bias, nurses were trained to carefully teach each participant using the same method, preventing their responses from being influenced (Creswell & Creswell, 2018). Ethical Considerations This project was conducted according to the University's Institutional Review Board and Belmont report guidelines. According to Belmont (1979), there are three principles to be followed: respect for participants, justice, and beneficence (Office for Human Research Protections, 2016). The primary investigator and nurses demonstrated respect by listening to the participants, validating their feelings, and answering their questions regarding the education or project. The primary investigator occasionally observed the nurse's interactions with the participants throughout the project. The participants were instructed that there are no repercussions to their personal or professional lives upon withdrawing from the project. Nurses and the principal investigator safeguarded the privacy and confidentiality of the participants by not discussing the project, the participants, or its findings with anyone not associated with the project or without their consent. Beneficence was demonstrated by informing the participants that the primary investigator or nurses would immediately cease questioning if they felt emotionally injured. To participants who were affected by the questions or project, a psychological resource was provided. Participants were informed of the risks, benefits, and minimal harm that may occur to them, for example, loss of data, conflict with family and friends, and feelings of anxiety or depression. As stated in the Belmont Report (1979), justice refers to allocating burdens (Office for Human Research Protections, 2016). It was possible that the project would lead to unwanted stigma from the participants' colleagues, family members, or friends. Each participant was treated uniformly following their wishes, so it did not affect the project’s findings. There could be a potential conflict of interest since the primary investigator works at the facility. To minimize the conflict, the primary investigator did not interact with the participants. Limitations The project had several limitations, including the self-reporting of medication compliance by patients. To minimize this limitation, the primary investigator validated the self-reporting instrument (MAP resources) before utilizing it for data collection (Althubaiti, 2016). Furthermore, the patient’s self-reporting was compared to their fasting blood glucose levels, medical records, or reports from family and friends (Althubaiti, 2016). The second limitation was the potential impact of the COVID-19 pandemic on healthcare organizations. The new COVID-19 guidelines have had an impact on the current healthcare delivery model. Due to the pandemic, the primary investigator has redirected resources and ceased in-person training sessions for nurses. Recruitment has been limited to Zoom meetings and telephone interviews. The third limitation was the location and setting of the project. The findings of the project cannot be generalized to other home healthcare agencies with similar populations. The fourth limitation is the time required to complete the project (four weeks). A longer timeframe would help the primary investigator analyze the site’s challenges, trends, and sustainability. Summary Medication adherence among Type II diabetic home health patients remains a critical factor in their quality of life. The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources when compared to current practice among Type II diabetic patients of a home healthcare agency in urban Texas over four weeks. A quasi-experimental design allowed the primary investigator to evaluate the impact of the MAP resources and educational intervention on the dependent variable (medication adherence rates). By utilizing a quasi-experimental design, the primary investigator assessed the impact of the MAP resources and educational intervention on the dependent variable (medication adherence rates). The medication adherence rates, and weekly glucose levels were collected before and after project implementation (four weeks). Data was collected by the primary investigator and stored on a digitally protected laptop. Hard copies were stored in a secure file cabinet at the primary investigator's home office. Chapter 4 provided a summary of the topic, along with descriptive data of the participants. Other sections consisted of the data analysis procedure, project findings, and summary. Chapter 4: Data Analysis and Results It was not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients. The stakeholders have cited that medication adherence among diabetic patients is lacking. According to data obtained from the site’s electronic health record (EHR), home healthcare providers documented that ten percent of diabetic home healthcare patients are not adhering to their medication regimens. A quantitative quasi-experimental project was conducted to address the clinical question: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas? Data on medication adherence was collected for the comparative group and compared to an implementation patient group. Chapter 4 presented the descriptive data for the patient sample. The data analysis procedures were outlined, and the results presented using narrative and chart format. The chapter concluded with a summary of the findings regarding the clinical question and the significance of the data analysis. Descriptive Data The quality improvement project used a quantitative, quasi-experimental approach for data collection. The targeted population for the project were from a home health care facility in urban Texas. The primary investigator used a G* power version 3.1.9.7, effect size 0.3, power 0.95, and df 0.5 to calculate the sample size needed for the project (N=34) for a significant level. The participants completed a five-item demographic questionnaire comprised of (gender, education level, race, age, and years as Type II diabetic). A total of 30 patients were included in the project, n= 15 in the comparative group and n= 15 in the implementation group. The descriptive data for gender, education level, and race are displayed in Table 1. It shows 10 males (66.7%) and five females (33.3%) in the comparative group and eight males (53.3%) and seven females (46.7%) in the implementation group. For education level in the comparative group, 2 (13.3%) graduate high school, 9 (60.05) had some college, 2 (13.3$) had an associate degree, 1 (6.7%) had a bachelor’s degree, and 1 (6.7%) had a doctorate degree. For educational level in the implementation group, 2 (13.3%) graduated high school, nine (60.0%) had some college, 1 (6.7%) had associate degree, and 3 (20.0%) had a doctorate degree. There were three Asian (20.0%), five (33.3%) Black, and seven (56.7%) White participants in the comparative group and there were two (13.3%) Asian, six (40.0%) Black, and seven (46.7%) White participants in the implementation group. Table 1 Descriptive Data for Gender, Education, and Race Variable Comparative (n = 15) Implementation (n = 15) Gender n % n % Male 10 66.7 8 53.3 Female 5 33.3 7 46.7 Education Level Graduate High School 2 13.3 2 13.3 Some College 9 60.0 9 60.0 Associate Degree 2 13.3 1 6.7 Bachelor’s degree 1 6.7 0 0.0 Doctorate 1 6.7 3 20.0 Race Asian 3 20.0 2 13.3 Black 5 33.3 6 40.0 White 7 56.7 7 46.7 Table 2 displayed the descriptive data for age and years with Type 2 diabetes for project participants. The mean age for the comparative group was 49.94 years (SD = 11.67) and the mean age from 35 to 64 and the implementation group was 52.80 years (SD = 9.47) with a range from 35 to 65. The comparative patients had a mean of 3.47 years since diagnosis (SD = 1.19) with a range from 1 to 5 and the implementation group had a mean of 2.93 years since diagnosis (SD = 1.03) with a range from 1 to 5. Table 2 Descriptive Data for Age and Years with Type II Diabetes Variable Comparative (n = 15) Implementation (n = 15) M SD M SD Age 49.94 11.67 52.80 9.47 Years with Type II Diabetes 3.47 1.19 2.93 1.03 Note. M = mean; SD =standard deviation Data Analysis Procedures The data analysis procedures included evaluating de-identified data of medication adherence rates four weeks prior and four weeks post-implementation of the project. The primary investigator abstracted a PDF report of the medication adherence rates for both the comparative and implementation groups. Raw data were input into a Microsoft Excel (2016) file (codebook). The independent variable was the MAP resource implementation (categorical), and the dependent variable was the medication adherence rates (yes/no). After data entry in Microsoft Excel was completed, data were exported to IBM SPSS version 27. To ensure data was prepared for analysis, a preliminary analysis of all variables was conducted to determine if the dataset has missing data or inaccurate entries. This included frequency counts for variables to check for missing data and values outside of the possible range of 0= no medication adherence and 1= medication adherence. A chi-square test was conducted to answer the clinical question. The chi-square test compares the association between two independent categorical variables (Schober & Vetter, 2019), which compared the medication adherence rate for patients 30 days before and 30 days after the implementation, thereby answering the clinical question. The primary investigator used a G* power version 3.1.9.7, effect size 0.3, power 0.95, and df 0.5 to calculate the sample size needed for the project (N=34) for a significant level. The sample size for this project did not meet the required sample for 95% power with N= 30. The significance level was set to .05. Therefore, a p-value of less than .05 would demonstrate statistical significance. The patient outcome-dependent variable was collected from the electronic medical records (Cradle Solutions) within the project site. The p-value (significance level) indicates the degree to which the null hypothesis is rejected or failed to be rejected. Researchers typically compare the p-value with a significance level of 0.05. If the p-value is less than 0.05, it implied that there was enough evidence to reject the null hypothesis. When the p-value is greater than 0.05, the null hypothesis is rejected. Electronic medical records are considered a reliable and valid source for data collection. A study conducted by McGinnis et al. (2009) examined EMR and written records. The results demonstrated the EMR-based data validity was shown to be moderate to excellent, with Pearson r correlations ranging from .875 to .99 for EMR and documentation records (McGinnis et al., 2009). Electronic medical records are considered a reliable source of data, as emphasized by Goulet et al. (2007), found strong agreement (Kappa between .86 and .99) and high sensitivity and specificity (≥.95) for quality measures based on electronically abstracted structured data compared with manual review. One identified potential error was related to the data is coverage error, which resulted in a difference between the sample size and the population measured (Qualtrics, 2020). To reduce the chances of this occurring, the primary investigator utilized a recruitment method accessible to all potential participants (such as word of mouth, text messages, and emails). The random error related to the quality improvement project is the measurements (Leedy & Ormrod, 2020). The error could occur after the primary investigator collects the data while being processed (Leedy & Ormrod, 2020). To minimize the chances of errors, the primary investigator hired a statistician to interpret the data patterns using statistical tests and perform data cleaning (Leedy & Ormrod, 2020). Results To answer the clinical question, the results are displayed in Table 3. The chi-square was utilized as the independent variable (MAP resources) and the dependent variable (medication adherence yes/no) are categorical variables and a chi-square determines whether there is an association between categorical variables. There was an increase in medication adherence from the comparative (n = 15, 66.7%) to the implementation group (n = 15, 73.3%), X2 (1, N = 30) = .159, p =. 999. The p-value was greater than .05, which indicated that the increase in medication adherence was not significant. Thus, there was no evidence that there was a statistically significant difference in medication adherence rates between the comparative and implementation groups. An interpretation of the project's results based on the chi-square test probability value indicated there was no association between the independent and dependent variables, and the data did not reject the null hypothesis at a significance level of 0.05. Table 3 Medication Adherence Rates in the Comparative and Implementation Groups Variable Comparative (n = 15) Implementation (n = 15) X2 p-value n % n % Medication Adherence 10 66.7 11 73.3 .159 .999 The results of the chi-square test analysis supported the implementation of MAP resources to improve medication adherence as compared to current practice among Type II diabetic home healthcare patients, ages 35 to 64 of a home healthcare organization. The rate increased in the implementation group, although the p-value was not less than .05 indicating no statistical significance. Given these findings, the data analysis strengthened clinical improvement after the implementation of the MAP resources for improving medication adherence rates. Summary statistics were conducted for the knowledge test for the nurses before and after the MAP implementation. Figure 1 displays the mean knowledge % scores for the five nurses at pre and post implementation. As shown, the mean knowledge % score at pre implementation was 63% (SD = 5.70%) with a range from 55% to 79%. The mean knowledge % score at post implementation was 85.6% (SD = 7.02%) with a range from 78% to 95%. 1Figure 1 Mean Knowledge Scores for Participants at Pre and Post Implementation Summary The purpose of this quantitative quasi-experimental project was to evaluate the impact of the Medication Adherence Project (MAP) resources on patient medication adherence rates for Type II diabetics patients. Data on medication adherence was collected from the site’s EMR for four weeks before the SBAR intervention and for four weeks after the intervention. Data on N= 30 patients was extracted from the facility EHR. A chi-square test was conducted to address the clinical question using IBM SPSS Version 27 with the level of significance set to p <.05. The results showed an increase in medication adherence from the comparative (n = 10, 66.7%) to the implementation group (n = 11, 73.3%), X2 (1, N = 30) = .159, p =. 999. The p-value was greater than .05, which indicated no statistical significance. The results do however support clinical improvement as the adherence rate improved after the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources. Chapter 5 offered a summation of the results and conclusions based on the findings showing increased medication adherence after the MAP resource implementation. The theoretical and practical implications of the results were summarized. The chapter concluded with recommendations for future projects, including adult home health patients with Type II diabetes, concerning the project findings that support MAP resources to improve medication adherence rates. Chapter 5: Summary, Conclusions, and Recommendations Diabetes impacts approximately one in ten Americans (Centers for Disease Control and Prevention, 2020). The prevalence of the disease continues to rise and is expected to grow by 0.3% annually until 2030 (Lin et al., 2018). This particularly troublesome for Type II home healthcare patients diagnosed with the disease. Polonsky and Henry (2016) emphasized that roughly 45% of this population fail in sustaining a normal glucose level. Poor medication adherence is associated with increased morbidity and mortality rates, finances, hospital readmissions, and diminished quality of life (Polonsky & Henry, 2016). This quality improvement project was developed to address a standardized method for healthcare providers to assess their patients’ medication adherence. A quantitative, quasi-experimental design contributed to the participants promoting self-reliance and increased knowledge levels in maintaining healthier glucose levels. Furthermore, the project improved the practitioner’s awareness of the need to evaluate their patient regarding medication adherence frequently. The project provided current information related to Type II diabetic patients and medication adherence, which validated other studies such as Heath (2019) and Sharma et al. (2020). Chapter 5 summarized the project related to Type II diabetic home health patients and medication adherence. Other segments comprised of the summary of the project’s findings and conclusions. The theoretical (Orem’s self-care deficit theory and Roger’s diffusion of innovation model), practical, and future implications were discussed. The last section consisted of recommendations for future projects and clinical practices. Summary of the Project The clinical question that directed the project was: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas? A chi-square test was conducted for a comparison of the medication adherence rates for the patients 30 days prior and 30 days post-implementation. A level of significance was set to .05, which indicated a p-value of less than.05 would reveal statistical or non-statistical significance. A convenience sampling was used to recruit N=15 participants for the comparative group and N=15 for the implementation group. Five nurses were educated regarding the use of the MAP resources. A retrospective chart audit was done to evaluate the medication adherence rates before the project implementation. The chi-square test was utilized to determine the variations among the two groups for statistical difference. Summary of Findings and Conclusion A sample size of N=30 participants was compared utilizing a chi-square test with the significance level at p <.05. However, 21 participants were adherent n=10 (comparative group) and n=11 (implementation group). The number of medication adherence rates were evaluated four weeks pre-implementation and post-implementation of the project. The clinical question that was answered using the chi-square analysis was: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources impact patient medication adherence rates when compared to current practice among Type II diabetic patients in urban Texas? There was an increase in medication adherence from the comparative group (n= 10, 66.7%) to the implementation (n=11, 73.3%), X2 [1, N=30] = .159, p= .999. The p-value was greater than .05, which indicated that there was statistical significance in the medication adherence rates. The project demonstrated that healthcare providers should check on their diabetic patients' medication adherence frequently during each home health visit. Implications Nursing is a practice discipline. Therefore, when a quality improvement project is carried out, it should focus on issues that directly affect nursing practice (Polit & Beck, 2018). With this project, the emphasis was on patient care and the potential clinical implications that affected the findings (Polit & Beck, 2018). The theoretical, practical, and future implications for this project are based on the data and the literature that preceded it. Theoretical Implications Orem's self-care deficit theory was selected since it aligned well with the clinical question within the project. The theory guided the primary investigator and the nurses during the conception and implementation of the project based solely on the self-care needs of Type II diabetic patients’ in-home health care. As a result of the theory, strategies that assisted participants in understanding their disease and maintain self-care were integrated into the project. Orem's self-care deficit theory consists of three components: the self-care theory, the self-care deficit, and the nursing system (RenpenningN et al., 2003). The intervention focused on a) abilities and actions related to medication adherence, b) staff nurses coordinating resources for diabetic patients, monitoring the disease, assessing medication adherence using a patient-centered approach (Orem, 1985). The strength of Orem’s self-care deficit theory allowed the primary investigator to provide the nurses with increased awareness in understanding their patients while addressing barriers that could impact them in understanding and maintaining medication adherence. In Chapter 2, the literature review examined how patients could effectively manage medication adherence while contributing to previous literature that utilized the theory on Type II diabetic patients (Borji et al., 2017; Ebrahimi, 2015; Ghafourifard & Shahbaz et al., 2016). One strength noted in the project was the increased curiosity and desire to learn exhibited by the patients. This was related to the nursing staff using a patient-centered approach in addressing their medication adherence. The patients verbalized that they appreciated the extra time that the nurses spent with them regarding maneuvering the chronic disease. The project's weakness was instructing the nurses to become familiar with Orem's self-care deficit theory, which formed the foundation of the project. Orem’s theory could be utilized and implemented in other projects related to Type II diabetic home healthcare patients since the findings cannot be generalized. Additionally, the project was constrained by a very short time frame (four weeks). If the project had been conducted over a longer period, the primary investigator might have been able to observe interactions between nurses and patients, trends, and obstacles that prevented an individual from maintaining medication adherence procedures. Practical Implications Practical implications included the agency evaluating and developing patient-specific medication adherence guidelines using MAP resources and the Orem theory. Several nurses suggested that one of the home visits focus entirely on a patient's current medication list and medication adherence. Another suggestion was for the primary nurse to send text messages to the patient's cell phone to remind them to take their medication. The last practical implication of this study was that nurses should not confront the patients regarding medication adherence status but should instead develop interventions designed to meet their specific needs (Sansbury et al., 2014). As a result of strategies such as goal setting, behavior contracts, or having an accountability partner, medication adherence can be improved (Sansbury et al., 2014). Future Implications Among the future implications of the project is the possibility of other quality improvement initiatives investigating the rates of medication adherence among teenagers in home healthcare settings. This should incorporate medication adherence strategies specific to their age group. A second implication pertains to diabetic medications; home health patients should be encouraged to participate in phase three trials for new diabetic products to improve their compliance. These products are becoming available and provided to participants at monthly or longer intervals. As a result, some of the short-term barriers to medication adherence would be addressed (Polonsky & Henry, 2016). A second future implication of the project is the implementation of strategies for medication adherence that are tailored to the participant's demographic characteristics (race, gender, age, personal preferences, culture, and social determinants) (Williams et al., 2014). Understanding and addressing the factors that affect the patient might facilitate better management of the disease (Williams et al., 2014). The use of a multi-systems approach to medication adherence can increase medication effectiveness, adherence, healthcare outcomes, and decrease healthcare costs (Williams et al., 2014). Recommendations The recommendations constitute a solid foundation for the nursing workforce by ensuring they are appropriately educated and ready to implement the practice (Institute of Medicine, 2011). To meet their patients' future health care needs, they must act as change agents within the healthcare sector (Institute of Medicine, 2011). This home health agency will require time, finances, resources, and staff commitment to implement and maintain the recommendations. The following paragraphs discussed recommendations for future research projects and clinical practices. Recommendations for Future Projects As a first recommendation, these projects should use a standardized assessment strategy to evaluate their patients' medication adherence practices and behaviors. Patient engagement in decision-making is limited when medical records are inaccurate, or medication assessments are inadequate. It would be helpful if diabetic patients were informed about the importance of adhering to their medication regimens. Patients who exhibited a change in their behavior are the best indicators of medication compliance. The second recommendation was to conduct the project using a larger population size focused on caregivers of diabetic patients. Focusing attention on this sector would emphasize the emotional and family support to help the patient remain compliant. Since many Type 2 diabetic patients have friends, family, or caregivers in their circle, it would be significant to include them in the discussion and the importance of medication adherence. This would allow a deeper understanding of the subject and generalization of the findings on this population. The next step in moving this type of project forward is implementing and sustaining MAP resources by the home health agency for maximum impact on the patients. By using this tool, frequent hospitalizations would be decreased, medical expenses reduced, and health quality improved. The project must be tailored to fit the needs and demands of the home health agency to enhance the project's sustainability. Recommendations for Practice One recommendation for current practices is for home health nurses to offer other options to help their patients remain medication adherence compliant. Kirkman et al. (2015) suggested via their project findings that encouraging patients to use mail-order pharmacies increases the patient’s chance of medication adherence. An analysis conducted by Medicare Part D showed an increase in medication adherence by diabetic patients (Kirkman et al., 2015). Another suggestion was the use of a medication events monitoring system to evaluate the patient’s medication adherence. The device would be incorporated into the patient’s packaging of the prescription medication (Lam & Fresco, 2015). It records the dosing events and stores the information with audiovisual reminders. The last option was to receive automated electronic reminders such as (text messages) using REMIND software from the visiting home health nurse. The second recommendation was for future clinic practices to establish and educate the nursing staff on cultural competency care. This type of nurse-patient relationship allows a stronger connection with the patient who feels comfortable expressing concerns and knowledge deficits because of a non-judgmental environment that helps them maintain medication adherence behaviors. Patients with Type II diabetes and their caregivers cope with Type II diabetes more effectively when they have effective communication (Aloudah et al., 2018). References Comment by Author: There are multiple errors in your reference list and in test citations please run your document through Recite Works to identify. Administration on Aging. (2015). 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(2018). Read the Belmont report. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html Williams, J., Walker, R., Smalls, B., Campbell, J., & Egede, L. (2014). Effective interventions to improve medication adherence in type 2 diabetes: A systematic review. Diabetes Management, 4(1), 29-48. https://doi.org/10.2217/dmt.13.62 Wong, Z. S., Siy, B., Da Silva Lopes, K., & Georgiou, A. (2020). Improving patients’ medication adherence and outcomes in nonhospital settings through eHealth: Systematic review of randomized controlled trials (Preprint). https://doi.org/10.2196/preprints.17015 World Health Organization. (2017). Adherence to long-term therapies: Evidence for action. http://apps.who.int/iris/bitstream/10665/42682/1/9241545992.pdf Yeam, C., Chia, S., Tan, H., Kwan, Y., Fong, W., & Seng, J. (2018). Systematic review of factors affecting medication adherence among patients with osteoporosis. Osteoporosis International, 29(12), 2623-2637. https://doi.org/10.1007/s00198-018-4759-3 Yip, J. (2021). Theory-based advanced nursing practice: A practice update on the application of Orem's self-care deficit nursing theory. SAGE Open Nursing, 7, 237796082110119. https://doi.org/10.1177/23779608211011993 Appendix A Grand Canyon University IRB Approval Letter Text Description automatically generated Appendix B MAP Resources Graphical user interface, text Description automatically generated Appendix C Permission to Use the MAP Resources Text, letter Description automatically generated Per the website of Starr and Sacks (2010), the MAP tools are available free of charge. Tools can be downloaded from https://hfproviders.org/documents/root/pdf_9a3a46fa03.pdf Pre Post 0.63 0.85599999999999998 Mean % Knowledge Score Overall Quality PICOT-AQR-FMR Scoring Sheet Project Title: Improving Medication Adherence among Type II Home Healthcare Diabetic Patients Student Name: Bola Odusola-Stephen Holistic Assessment For All Chapters (Rate Each Item on a scale of 1, 2, 3, 4, or 5 using whole numbers only.) 1= Unacceptable 2= Does Not Meet Expectations 3= Partially Meets Expectations 4= Meets Expectations 5= Exceeds Expectations Brief comments must be included for all criteria. Approved PICOT (DNP-815) Comments Reviewer Name & Date AQR-1 DNP955 Comments Reviewer Name & Date: AQR-2 DNP965 Comments Reviewer Name & Date Department Review (Final Manuscript Review) Comments Reviewer Name & Date Dr. JoAnna Cartwright 10/14/2021 I. PICOT: 1. A description of population being assessed can be linked to a direct practice improvement and is extremely thorough with substantial supporting evidence. Students must avoid projects related to falls, prisons, psychiatric and mental health patients. 2. The setting must be a single site (no multi-site projects). The site is not implicated, must only include general information about location of site, and no specific names. 3. A description of the intervention is extremely thorough with substantial evidence and supporting literature. The intervention must be evidence- based and include supporting literature. Must be new to the facility and not already in place. 4. The intervention must be measurable & impact patient outcomes. The student must not have direct patient contact. 5. A description of the comparison information is extremely thorough with substantial evidence and measurable outcomes. The DPI project outcomes compare the current practice (or no practice) to outcome/results after implementation of the evidence-based intervention. 6. A description of the timeline is extremely thorough with substantial evidence. Data must be collected over a four-week period after intervention is initially implemented. 7. A description of the outcome is extremely thorough with substantial evidence, pertaining to a directly related measurable patient outcome. 8. May use archival data from the electronic health records (EHR), alternate data sources like facility records/metrics but may NOT use HCAHP scores or Press Ganey scores. 9. May use valid, reliable instruments (surveys, scales, questionnaires, etc.) with evidence of permission to use. 10. Students may not create their own instruments and may not modify instruments. Is the DPI project feasible, ethical, & theoretically sound? N/A N/A N/A N/A N/A II. Title Page, Copyright Page, Signature Page: 1. The Title Page and Signature Page are formatted to the DPI Project template 2. “Proposed” is removed from the signature page for final manuscript. 3. All signatures and credentials are written using correct format 4. The copyright page is present. 5. The manuscript appears to be formatted using the Final DPI Project template. N/A 3 Remove Dedication and Acknowledgement pages if your are not going to use. III. Abstract: The DPI Project includes an evidence- based intervention and describes in detail how it will be (or for final manuscript was) implemented by the student by preparing individuals at the facility regarding the new technique, protocol, screening, toolkit, etc. The student MUST NOT compare groups and does not name the clinical site. 1. Opening sentence (compelling, overall project descriptor, must be generalized enough that it does not have to be cited) 2. One to two sentences stating the specific problem at the project site 3. The purpose statement templated exactly with all required elements 4. One sentence that includes the theoretical models/theories of both the nursing theorist and the change theorist 5. Description of data relative to the measurable patient outcome (templated abstract) must have sample size. 6. The test used and APA formatted results with a p=value for the measurable patient outcome 7. Explanation of statistical and clinical significance relative to the p=value and the measurable patient outcome 8. Explanation of clinical significance relative to the practice change/measurable patient outcome 9. Implications of the project 10. Recommendations for future projects and sites; KEYWORDs included using correct format N/A N/A 4 Appropriate IV. Table of Contents (TOC): 1. Refresh TOC to update entire table to check it is correct. 2. Check the page numbers for the tables and figures. The TOC pages are counted; the page number is right justified 3. Must be 12-point Times New Roman typeface, double-spaced and right justified. 4. Dot leaders must be used. 5. Title should be styled as “TOC Heading” [double spaced, no indent, bold, “keep with next”]. 6. Reflects the specific levels of organization within the manuscript. 7. All major chapter headings must be worded exactly the same and occur in the same order as they do in the GCU manuscript template. 8. Any heading that appears in the TOC must appear in the text, and any heading in the text must appear in the TOC. 9. Subheadings that differentiate subsections of each chapter are single-spaced, upper, and lowercase. 10. The headings and subheadings in the TOC must exactly match the text body. N/A N/A 2 Your TOC needs reformatting. Please consider highering a formatter. V. Chapter 1: 1. Starts on page 1 2. Provides an introduction to the project including defining important terms used within the project e.g. definition for the intervention, definitions for the outcome 3. Purpose, problem, clinical questions aligns (matches) in all locations. 4. DPI Project Intervention: includes an evidence- based intervention and describes in detail how it will be (or for final was) implemented by the student by preparing individuals at the facility regarding the new technique, protocol, screening; must be directly related, measurable & impact patient outcomes 5. If a resource is used (toolkit, protocol, clinical practice guideline, etc.), correct reference and evidence of permission to use must be included 6. The student must not have direct patient contact 7. Must include a nursing theory and a change model is suggested 8. Does not mention hypothesis, hypothesized, researched, researcher, study, correct tense is used (future for proposal & past for final manuscript) 9. Site cannot be identified 10. Definitions and terms are conceptual, operational, and include citations from peer reviewed sources 11. Clinical questions and variables are aligned (matches) and optimal for addressing the clinical practice problem. 12. Student refers to proposed /proposal for the chapters 1-3 and then removes those words (prosed & proposal) after obtaining QI/IRB approval and for completed chapters 1-5. 13. Seminal/original sources are cited for theories/theorists; additional secondary sources may be cited in Chapter 2 14. Include the assumptions being accepted for the project as methodological, theoretical, or topic specific. Provide a rationale for each assumption, incorporating multiple perspectives, when appropriate. 15. Limitations are things that the investigator has no control over, such as bias. The limitations and assumptions should not contradict one another. Assumptions are also present with the statistical tests performed in the DPI project. 16. Delimitations are things over which the investigator/project manager has control, such as location of the project, population and sample, and data collection tools like the electronic health record (EHR). Include the limitations and delimitations of the project design and provide an associated explanation. Discuss the potential generalizability of the project findings based on these limitations. N/A 4.0 Appropriate VI. Chapter 2: 1. Should be minimum 20 pages; occasionally there are exceptions 2. Themes and subthemes are formatted correctly; themes are level 3 and subthemes are level 4 3. Each theme has an intro and summary 4. Seminal/original sources are cited for theories/theorists and instruments in Chapter 1 and secondary sources may be included in Chapter 2 5. Literature should be synthesized 6. 85% of references must be within past five (5) years 7. Most should be primary sources, some secondary sources, and limited systematic reviews and meta-analysis 8. Must include a nursing theory and a conceptual/change model 9. Writing conveys a deep understanding of the literature and theories/models to support the need of the project (Explains clearly why the project is needed) N/A 2.0 Your subthemes and themes do not match what is listed in the introduction so it is difficult to ascertain if you have the appropriate synthesis paragraphs. Your summary needs to include a synthesis of your three themes and needs to have citations to substantiate your comments. VII. Chapter 3: 1. Must include the methodology (quantitative), the design (quasi-experimental), and the step-by-step data collection process 2. Includes the methodology (quantitative) and how it will answer the clinical question(s) 3. Describes the design (quasi-experimental) and how it will be used to collect the type of data needed to answer the clinical question(s) 4. Includes the specific data source(s) that will be used to collect the directly related, measurable patient outcome data 5. The data collection process should be clearly detailed step-by-step 6. All instruments (surveys, questionnaires, scales, tools) must be evidence- based and include validity and reliability psychometric data along with citation of seminal source 7. Students may not create their own instruments or modify existing instruments even with permission 8. May not compare groups 9. May use archival data from the electronic health records (EHR), alternate data sources like facility records/metrics but may NOT use HCAHP scores or Press Ganey scores 10. includes a discussion of the population, projected sample and sampling procedures, analysis, ethical considerations, bias and mitigation, limitations and delimitations N/A N/A 4.0 Appropriate VIII. Chapter 4: 1. Identifies the DPI project population 2. Include a description of the specific sample (the patients impacted by the intervention) 3. The healthcare providers are NOT the sample 4. The actual data collected, the actual analyses performed, and the results of the analyses including the statistical test and significance level 5. Sampling and data collection were well planned and executed, according to scientific and ethical standards. 6. May not compare groups 7. Data analysis procedures were well planned and well executed 8. Includes statistical findings, MUST HAVE ACTUAL test statistic and p-value (p=###) (p< or p> is not acceptable) 9. Includes a statement following the statistical findings indicating overall meaning of the findings. N/A N/A N/A N/A 4.0 Appropriate
IX. Chapter 5: 1. Provides a cogent summary of the results, both statistical and clinical significance 2. Implications are included 3. Recommendations are included 4. Project findings and discussion are fully articulated 5. Convincing in relation to the clinical questions with all major limitations identified N/A N/A N/A N/A 4.0 Appropriate.
X. References: 1. Formatted in APA 7th Edition. 2. All citations are referenced 3. All references are cited 4. 85% of references must be within past five (5) years 5. Most references are primary sources, some secondary sources, and limited systematic reviews and meta-analysis N/A N/A 2.0 Multiple intext citations that need revised as well as references in your reference list.
XI. Appendices: 1. The order of the appendices in the Appendix does not follow APA guidelines. 2. Appendix A is the 10 Strategic Points for the proposal and then, it is replaced with the GCU QI/IRB determination (approval) letter for final manuscript 3. Appendix B is the resource/instrument (toolkit, screening tool, clinical practice guideline, etc.) used in the evidence-based intervention 4. Appendix C is the evidence of permission to use the resource/instrument 5. Additional appendices may include other resources/instruments; permissions to use resources/instruments as applicable; and educational material that is relevant to the intervention 6. Site authorization letter is not included as an appendix 7. Informed consent is not included as an appendix (it is located in IRB packet) N/A N/A 4.0 Appropriate
XII. Scholarly Writing & APA: 1. Abstracts must be double-spaced; limited to one (1) page. 2. Logical flow and argumentation exists at all levels within the manuscript [sentences, paragraphs, sections, chapters] 3. Scholarly writing quality is present throughout the manuscript; ensure correct grammar, punctuation, spelling, and formatting 4. With the exception of a minor margin of edits, chapters are written in accordance with APA 7th Edition standards 5. The project is written at an appropriate level for doctoral scholarly work with clear argumentation, a well-structured, comprehensible discussion, and contain synthesized findings. 3.0 Reformatting need of your TOC.
Holistic Assessment For All Chapters The total score will automatically calculate and it will be an average of all applicable ratings. 1= Unacceptable 2= Does Not Meet Expectations 3= Partially Meets Expectations 4= Meets Expectations 5= Exceeds Expectations ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! 3.3
Areas highlighted here should be considered as recommended resources. • ThinkingStorm • Grammarly Premium • Intellectus Statistics • Writing Resources in DC Network • Use of an external editor and formatter • Use of a statistician • Use GCU Library and APA resources

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