NURS FPX 9010 Assessment 2 Project Proposal

Student name

Capella University

NURS-FPX9010

Professor Name

Submission Date

Project Proposal

The increased percentage of poorly controlled Type 2 diabetes in adult patients in the practice site has unveiled the persistence of issues concerning inadequate self-management profligacy, the weak adherence to treatment advice, and insufficient knowledge about everyday lifestyle choices that can interfere with glycemic regulation. Though the routine diabetes teaching is offered by the staff members whenever they encounter the client in the clinical setting, the short interactions have never been effective to yield any significant long-term changes in the fasting glucose level or general self-management practices of diabetes.

It has been demonstrated that structured and evidence-based diabetes self-management education and support (DSMES), especially in accordance with the American Diabetes Association (ADA) guidelines, can positively affect clinical outcomes and patient attitude to the process of chronic disease management (American Diabetes Association, 2023). The PICOT question that would be used to drive the project would be as follows: In nursing staff that provide care to adult patients with Type 2 diabetes (P), the effect of applying DSMES as prescribed by the ADA (I) versus the current practice (C) on fasting glucose levels (O) in 12 weeks (T) is the research question. The current proposal contributes a detailed roadmap in the introduction and testing of an ADA-congruent DSMES program at the practice site to enhance the nursing practice and patient glycemic outcomes.

Project Problem

The emergency of type 2 diabetes is a major complication in out patient primary care clinics. The adult patients with type 2 diabetes at the project site have inefficient self-management behaviors and follow-up which has resulted into high mean fasting glucose level of 145mg/dL, which represents poor glycemic control. The American Diabetes Association (2023) suggests that patients should have a normal level of fasting glucose of less than 100 mg/dL; thus, the average fasting glucose of the current site of 145 mg/dL reveals inadequate self-management patterns, which result in low glycemic control in comparison to the recommended levels.

The adopted practice by the nursing staff members at the location does not sufficiently embrace the DSMES program promoted by the ADA, which prevents the patient to have a complete ownership of the condition. Over the last six months, the clinic treated approximately 450 adult patients with Type 2 diabetes, and the average level of fasting glucose was always approximately 145mg/dl, which is far higher than the recommended 100mg/dl set by the ADA (Executive nurse, personal communication, October 10, 2025). The continued high level among a big number of patients indicates that there is still a lack of good glycemic control and points to the necessity to reinforce the practice of diabetes management at the location.

The adoption of the structured DSMES by nursing personnel is presumed to increase the self managing behavior and consequently the degree of fasting glucose and the overall glycemic control will be reduced during the 12 weeks. The clinic has a culturally diverse population of approximately 5,000 adult patients each year and almost 40 percent were found to be in high risks with Type 2 diabetes. The recent review of patient outcomes indicated that the emergency department visits were common due to hyperglycemia.

Impact on Individuals and Stakeholders

The impact of the improper self-management of diabetes is also observed in various levels of patient well-being and the insufficient screening of high-risk population is another factor that causes later diagnosing and controlling of diabetes. As Lamptey et al. (2022) observed, there was a direct correlation between the worse results caused by poor self-management, and organized DSME programs facilitated the glycemic control. The disrupted application of the evidence-based model among the staff and administrators of healthcare facilities results in workflow inefficiency and poor performance in regard to the major quality indicators.

Asmat et al. (2024) found that the patient-centered self-management intervention plays a significant role in the glycemic control. Nonetheless, there is still a gap between current practices in the project location, which do not have the evidence based strategies that have been found to improve patient outcomes. The current statistics at the clinic indicate the need to ensure that an evidence-based model is continually adopted in order to address the current self-management behaviors that result in better glycemic control.

Recognizing Potential Areas for Improvement or Additional Knowledge

The existing interventions in the site need to be refined to address the need to raise self-management behaviors among patients with Type 2 diabetes. The plans are supposed to be incorporated in the current operations by educating employees and conducting regular assessment of adherence. The issues of the sustainability of protocol-based diabetes management in the long run should be discussed in future research (Lamptey et al., 2022). The applicability of the project to the principles of CITI supports ethical data management, patient confidentiality, and the observance of the institutional standards of quality improvement. Improving the aspects will increase the implementation fidelity and the self-management outcomes of the adult patients with Type 2 diabetes.

The problem identified in the practice demonstrates an incorrect process with unequal use of DSMES, the absence of planned follow-up, and inconsistency in nursing documentation regarding diabetes education. This issue was detected in the EHR audits, communications with the executive nurses, and the trend analysis that revealed constantly high fasting glucose levels during six months. The national statistics support the stated issue; the Centers of Disease Control and Prevention (2024) states that over 37 million Americans are diagnosed with diabetes, and about half of them do not achieve the recommended glycemic levels because of poor self-management care. In a similar fashion, ADA (2023), Asmat et al. (2024), and Religioni et al. (2025) identify the prevalence of gaps in the implementation of DSMES in the outpatient clinics. According to the literature, a structured, nurse-led DSMES model is needed in the country to improve patient engagement, glycemic outcomes, and decrease diabetes-related hospitalizations.

The Project Site

The location of the project is an outpatient primary care clinic in a suburban area, in the state of New Jersey. The clinic is a part of a community-based health system that focuses on patient-centered health care that is accessible to adults and elders. Services involve regular consultation with a doctor, health education, as well as laboratory examination. The facility has an interdisciplinary team-based care model that incorporates the medical, nursing and administrative personnel to facilitate continuity of care. The clinic sees an average of 15- 20 patients per week; it has about 40 percent in Type 2 diabetes or other related metabolic diseases (Executive nurse, personal communication, October 10, 2025). The facilities consist of three well-equipped examination rooms, a small lab for point-of-care tests, and a patient education space that would be used in counseling. The interprofessional team members are a physician, nurse practitioners, registered nurses, a medical assistant, and a part-time diabetes educator. The clinic has implemented the electronic health records (EHR) system, which allows the clinic to conduct the data-driven decision-making and continuous quality improvement (QI) programs.

Potential Implications of the Project Site for the Project

Primary care environment is the best place where an evidence based model can be put into practice. The prevention and chronic disease management orientation of the clinic is very much in line with the objective of the project of enhancing the fasting glucose levels among the adults with Type 2 diabetes. One of the benefits of a nurse-led practice model is the ability to use the expertise of nurses who are the keys to the successful management of diabetes (Baek et al., 2023). However, budget constraints can be considered a limiting factor to access high-level diagnostic instruments and require effective utilization of the resources (Mechley, 2021).

The nurse-led model can be implemented in a cost-effective manner and is applicable in improving self-management behaviors. The above-mentioned project is consistent with the mission of the organization to provide the patient-centered approach to chronic disease management that is accessible and enhances the current quality improvement programs. Past QI activities in the site were preoccupied with medication compliance and lifestyle education but had no organized DSMES model, which contributed to little long-term change of glycemic performance. The project directly contributes to the strategic objectives of the clinic in population health, quality measures, and decrease in avoidable emergency department visits through the use of a standardized DSMES protocol.

Project Population

The patient population of the project includes adult patients with at least 18 years old with Type 2 diabetes who are under the care of the outpatient primary care clinic. The population has common features, which include burden of chronic diseases, diverse cultures, and societal difference in terms of diabetes self-management proficiency. The inclusion criteria will be: (a) a confirmed diagnosis of Type 2 diabetes, (b) no less than one clinic visit during the last six months, and (c) must have fasting glucose in the EHR. The exclusion criteria will be Type 1 diabetes, gestational diabetes, severe cognitive impairment, or the inability to attend DSMES sessions. To identify any significant changes in the trends of fasting glucose, at least 30-50 patients will be required. The clinic population is sufficient to support the recruitment and offer continuous data required to support QI assessment as it has over 450 adult patients with Type 2 diabetes whom they have encountered in the last six months.

Proposed Interventions

Evidence-based intervention plays a vital role in enhancing the management of self-care and the management of glycemic levels in adults with Type 2 diabetes. The major intercession regarding the project would be the utilization of the DSMES program of the ADA that would assist patients in enhancing self-care skills and addressing the issue of fasting blood glucose. The implementation of the principles of the DSMES into the routine nursing practice has been reported to lead to a substantial improvement in the documentation of the self-management needs and patient involvement in the diabetes care (ElSayed et al., 2023). The structured intervention will entail coordination of the team, standardization of patient education and the continuity of care and follow-up protocols which will support behavior change. The approach will directly deal with the key practice gaps found at the project site, with the high rates in fasting glucose of approximately 145mg/dL, and contribute to the model of diabetes care at the primary care level that enhances the outcome and prevents the development of the diabetes-related complications.

Multidisciplinary collaboration, standard workflow, and staff training will be part of the concept of implementing the principles of DSMES in a consistent manner. Findings of a meta-analysis conducted by Romadlon et al. (2024) revealed that interventions that involved DSMES led to a reduction in HbA1c and fasting blood glucose by 0.61% and 23.33 mg/dL, respectively, as compared to usual care. Equally, the meta-analysis conducted by Chowdhury et al. (2024) revealed that the DSMES interventions led to higher self-care practices and glycemic control of patients, where the average reduction in HbA1c was 0.64% in low and middle-income environments. Moreover, a randomized controlled trial represented by Ibrahim et al. (2025) suggested that structured self-management education program, with 12 weeks of intervention, demonstrated significant benefits in terms of self-efficacy, self-management behaviors and glycemic outcome in patients in comparison to standard care. The combined studies offer strong empirical evidence on the incorporation of the evidence-based DSMES system into nursing practice to foster the improvement of self-management behaviors and glycemic control in adults with Type 2 diabetes.

The learning element of the intervention will entail a program of DSMES sessions conducted by nursing staff who are educated with ADA-approved resources and patient-centered communication. Educating the personnel on the standardized DSMES procedures will make the process of recording the needs of self-management of patients more uniform, which increases the patient involvement in managing diabetes. These studies have indicated that behavioral change-based, medication adherence, and lifestyle coaching DSMES programs enhance self-efficacy and glycemic control in patients, and reduce HbA1c levels (Romadlon et al., 2024; Chowdhury et al., 2024).

The DSMES sessions can be active learning sessions about nutrition, glucose monitoring, physical activity, and goal setting and progress monitoring, which the electronic health records would facilitate to guarantee continuity of care. The results of Ibrahim et al. (2025) indicate that the improvement of physical and emotional health is related to increased attendance and engagement during the sessions of DSMES. The results describe the enormous significance of culturally sensitive education and follow-up as the key to long-term engagement. The nursing-based DSMES model enables the Type 2 diabetes adult patients to enhance self-management practices, which result in an enhancement of the levels of fasting glucose and general glycemic control.

There is a profound review of literature that supports the use of DSMES as one of the most evidence-based interventions to enhance the glycemic outcomes. Plazas et al. (2023) showed that structured DSMES interventions are associated with significant benefits in HbA1c, fasting glucose, and self-efficacy in different groups of people. Bekele et al. (2021) verified that there were decreases in the percentage of HbA1c between 0.5% and 1.4% after the use of DSMES. Moreover, patient-centered education models with a focus on cultural customization, a prolonged follow-up, and the establishment of behavioral targets always provide positive results (Bekele et al., 2021). The discovered results are in line with the ADA (2023), which suggests the use of DSMES at diagnosis, yearly, in case of complications, and in case of the change of care. DSMES is a frequently agreed-upon solution in the literature as a highly effective approach to enhancing glycemic control in outpatient primary care, and is scalable.

Implementation plan

Step-by-Step Process for Implementing the DSMES Intervention

DSMES intervention will be implemented in a coherent, step-by-step model that will be used to achieve fidelity, consistency, and replicability throughout the clinical environment. The implementation will involve a special staff training session where nursing staff, nurse practitioners, medical assistants, and the diabetes educator will be taught the skills of DSMES content, ADA requirements, workflow integration, and the standard EHR documentation procedure (ElSayed et al., 2023; ADA, 2023). The preparatory stage is important because it makes sure that all staff members are familiar with the curriculum, as well as with the operational processes involved in the intervention, which is essential to the well-being of implementation fidelity (Silva et al., 2022).

After the training has been done, eligible adults with Type 2 diabetes will be enrolled by means of weekly electronic health record (EHR) reports which will be based on diagnosis codes and recent fasting glucose values; the medical assistant will create the reports, and the project lead will check the eligibility based on a set of pre-established inclusion and exclusion criteria. Once the patient has signed up, they will be enrolled in a four weeks of sessions of DSMEs provided by trained nursing personnel using standardized curriculum modules on nutrition, glucose monitoring, medication adherence, lifestyle modification, and structured problem-solving. At the end of each session, individualized SMART goal development is carried out in order to facilitate patient engagement.

Follow-up weekly, performed either face-to-face or over the phone will solidify learning and goal progression whereas assessment of fasting glucose on a monthly basis will give early clinical signs to influence iterative refinements. It has been evidenced that structured DSMES interventions provide significant benefits to patients in self-management behaviors and glycemic outcomes, such as decreases in the level of fasting blood glucose and HbA1c (Knight et al., 2022). All DSMES experiences, objectives, and evaluations will be recorded in a standardized EHR template and evaluated on a regular basis to drive continuous improvement. The project will keep the momentum going by having scheduled stakeholder huddles and monthly review meetings, where compliance to the project will be reviewed, process impediments identified, and strategies adjusted according to formative data.

Learner Role and Scholarly Leadership

The learner will supervise all the operational and clinical aspects of the intervention, as a project leader, to make sure that the intervention can meet the evidence-based standards and comply with the project objectives. The learner will organize the staff training in DSMES, carry out competency verification, ensure that the process of patient identification is performed on a regular basis, and make sure that the process of DSMES sessions is conducted according to the standard contents and delivery guidelines. Other roles will be ensuring ethical management in line with CITI training, checking the accuracy of documentation, ensuring data integrity, and a weekly analysis of the workflow adherence.

It has been demonstrated that the high level of project leadership, which is a proactive and active supervision, competency verification, and constant monitoring can positively influence the level of adherence to evidenced-based intervention and the quality of diabetes self-management education programs (Ernawati et al., 2021). The learner will also consider the feedback provided by the staff, monitor the emerging challenges, and provide leadership of continuous quality checks to facilitate procedural faithfulness. The learner can make sure the project steps are up-to-date by proactive leadership, effective communication, and organization of project oversight and ensure that data represent the appropriate clinical process.

Preceptor Partnership and Oversight Support

The collaboration with the preceptor will also offer needed clinical advice, operational information, and organizational fit in the implementation. Regular meetings between the learner and the preceptor will allow discussing the progress of the implementation and assessing the challenges faced by the staff, the quality of documentation, and the need to make changes. The preceptor will help to incorporate the DSMES workflow into the current clinical procedures, recommend on the possibilities of resource limitations, and support communication with the administrators.

Also, the preceptor will assist the learner in the interpretation of formative data and in making sure that changes in the intervention are consistent with the clinical standards and other site feasibilities. It has been demonstrated that collaborative preceptor-based supervision can improve the faithfulness and success of quality improvement projects through the use of expertise, compliance with evidence-based guidelines, and problem-solving in the context of complicated clinical settings (Knight et al., 2022). This interdisciplinary supervision leads to a balanced academic and practical possibility, which will result in the greater efficacy of the DSMES intervention.

Stakeholder Engagement and Participation

The engagement of stakeholders is a key to the success of the DSMES intervention. Internal stakeholders are nurses, nurse practitioners, physicians, medical assistants, the diabetes educator, clinic administrators and quality improvement personnel. The targeted people will be involved in delivering the content of DSMES, patient identification, scheduling, recording sessions, data monitoring, and the administration logistics. Internal stakeholders will be asked to follow new workflow, involve in training, and follow standard documentation practice and will directly be affected by the project as they will receive different organization of visits, more focus on patient education, and additional duties in regards to follow-up communication.

It was demonstrated that active involvement of internal stakeholders in systematized interventions toward diabetes management can help to increase the fidelity of the implementation, the level of staff compliance to standardized guidelines and protocols, and the patient-centered outcomes of the chronic disease management process (Silva et al., 2022). The external stakeholders will be adult patients who are the receivers of the DSMES and family members who are provided with the self-management support and the community-based partners, which contribute to the larger scope of diabetes resources.

The changes in the communication strategies, delivery of education and access to structured support strategies will influence the identified stakeholders and the engagement and satisfaction will be considered one of the main measures of intervention acceptability and sustainability. It has also been linked to increased self-management behavior, enhanced satisfaction, and increased sustainability of intervention outcomes when external stakeholders, such as patients, families, and community partners, are engaged in structured diabetes education programs (Ernawati et al., 2021; Asmat et al., 2024). The evaluation plan will involve patient satisfaction and perceived usefulness of DSMES.

Interprofessional Team Roles and Responsibilities

The interprofessional team involved in the DSMES intervention has well-articulated roles in an attempt to make the intervention efficient and coordinated. RNs will present modules of DSMES, identify patient learning requirements, facilitate the use of SMART goals, and record all the interactions with the help of standardized templates. Clinical oversight will be offered by nurse practitioners and physicians to ensure that the recommendations of DSMES are aligned with the individualized treatment plans, and will resolve medical problems that occur during the intervention. Medical assistants will coordinate patient identification by using EHR, schedule DSMES sessions, facilitate the process of follow-up, and help in data collection procedures.

The involvement of external stakeholders, such as patients, families, and community partners, in the organized intervention programs based on diabetes education has been linked to better self-management practices, increased satisfaction, and more long-term sustainability of intervention results (Ricci et al., 2023). The diabetes educator will be able to undertake specialized counselling on complex cases and guide the nursing staff to meet the goals on patient engagement. The quality improvement coordinator will help in data management, checking the reliability of outcome measures, and tracking compliance to the processes of improvement.

The clinic administrator will guarantee the availability of resources, logistical scheduling, communication systems, and workflow modifications. Interprofessional roles like diabetes educators, quality improvement coordinators, and administrators are proven to contribute to the effectiveness of the program, patient outcomes, and a steady adherence to the evidence-based diabetes management guidelines (Ernawati et al., 2021). The project will have coordinated implementation, reduced redundancy, and maximized the overall expertise required to achieve the desired results in improving self-management of diabetes through the involvement of the interprofessional team in well defined tasks.

Data Collection, Analysis, and Desirable Outcomes

Desired Outcomes

The preferred outcomes of the projects are quantifiable changes on self-management of diabetes which results into low fasting blood glucose levels in adult patients with Type 2 diabetes. The first deliverable of the project is a decrease in the poor glycemic control among adults with Type 2 diabetes indicated by the decrease in the mean of fasting glucose levels to 145 mg/dL to the ADA target of less than 130mg/dl realized through a development of improved self-management behaviors within 12 weeks of project implementation. The secondary outcomes are self-management behaviors and unnecessary referrals to specialist services. The EHR will be used to track data on the weekly referral activity, and the analysis of referral trends and staff compliance with the new EDSMES referral process would be feasible.

The objective evidence of goal progression would be the comparison of before and after intervention measures. The toolkit will have selected measures that will directly measure the effectiveness of the implementation of ADA-based DSMES at the practice site. Quantitative measures will be the mean fasting glucose of DSMES and the rate of documentation compliance, which will be compared between baseline and after 12 weeks. Qualitative indicators will involve the nursing staff feedback and patient satisfaction surveys that further evaluate the feasibility and sustainability of the intervention (Ricci et al., 2023). A combination of the measures will present the evidence of the project success in promoting the evidence-based management of diabetes and quality of care improvement.

Measurement of Outcomes

The measurement of clinical outcomes will involve the point-of-care fasting blood glucose at baseline (within 2 weeks before the beginning of the intervention) and monthly ones with a special emphasis on the 12 weeks as the main post-intervention measure. Secondary clinical evidence (when applicable) will encompass values of HbA1c in case the patient has at least one ordered included in the 3-month range of the intervention period, but fasting glucose will still be the primary outcome of operation since it is routinely available at the clinic. The measures of self-management behaviors and self-efficacy will be assessed at baseline and at 12 weeks with validated scales, the summary of diabetes self-care activities (SDSCA) to determine the frequency of the behavior (diet, physical activity, glucose monitoring, medication adherence) and a diabetes self-efficacy scale (e.g., diabetes management self-efficacy scale) to assess perceived capacity to manage diabetes.

Self-management behaviour and perceived competency have demonstrated to be reliably measured using some validated instruments like the SDSCA and diabetes self-efficacy scales, which have been shown to correlate with the improved glycemic control in adults with Type 2 diabetes (Ibrahim et al., 2025; Romadlon et al., 2024). Such process measures as DSMES attendance will be monitored with the assistance of attendance logs, and documentation compliance will be evaluated with the help of weekly audits of the standardized DSMES EHR template. Patient satisfaction will be measured by a short validated patient satisfaction questionnaire to be conducted on the completion of the program.

Evaluation Criteria to Ensure the Planned Change Occurred

Clinical and process thresholds will be used to ascertain success in the project. The main clinical success measure will be statistically and clinically significant mean fasting glucose baseline to 12 weeks reduction with a target group mean of less than 130mg/dl; success will be discussed as per effect size and confidence intervals besides p-values. Secondary success criteria are: DSMES attendance [?]70% of patients admitted attending three of four of the core sessions, documentation compliance [?]80% as assessed by audit of weekly charts and a statistically significant increase in mean SDSCA and self-efficacy scores between baseline and 12 weeks.

The mean scores of the survey are anticipated to be on the top tertile showing that there is acceptability of patient satisfaction. Setting specific clinical and process goals, including fasting glucose reductions, session attendance, and self-management scores, is in line with the evidence that well-structure DSMES initiatives using specific goals make measurable changes in patient glycemic control and attendance at sessions (Chowdhury et al., 2024; Romadlon et al., 2024). To ensure sustainability, a decrease in the number of diabetes-related ED visits, or urgent referrals, will be reported compared to the 12 weeks before the implementation period, but these data are only exploratory because of the short implementation period.

Measurement Tools and Psychometric Properties

The most significant measuring tools are: (1) a point-of-care fasting blood glucose and HbA1c (a clinical measure of laboratory data accepted by ADA standards), (2) a self-management behavior measure (SDSCA), (3) a validated diabetes self-efficacy scale, and (4) a short patient satisfaction measure that is proven to be used in the context of outpatient education. SDSCA is popular in diabetes QI and research and has shown reasonable reliability and construct validity in a variety of outpatient groups (Ibrahim et al., 2025; Ricci et al., 2023). It has been found that the scales of diabetes self-efficacy applied in related studies have been good in terms of internal consistency (the Cronbach alpha values are usually within the acceptable-good range) and change sensitivity following a number of education interventions (Chowdhury et al., 2024; Ibrahim et al., 2025).

Clinical glucose measurements are conventional, and fasting plasma glucose and HbA1c have been validated and reliable in the control of glycemia (ADA, 2023). In case any of the instruments chosen to be used in a survey are proprietary or not publicly accessible, the project lead will seek permission of the instrument owners and will record the permission before using it. The instruments and the psychometric properties will be mentioned in the final report and in the evidence matrix that justified the choice of instruments.

Data Analysis Plan

Data cleaning and descriptive statistics will be used to initiate the analysis with the purpose of describing the sample (means, standard deviations, medians, interquartile ranges in the case of continuous data, counts and percentages in the case of categorical data). The main method of analysis of the clinical outcome (fasting glucose) will be compared before and after 12 weeks of mean values with paired statistical tests: paired t-test in case the differences are normally distributed, and the Wilcoxon signed-rank test in case of non-normality. Mean difference, standard deviation, 95% confidence intervals, p-values, and an effect size (Cohen d in parametric tests or another nonparametric effect size, in nonparametric tests) will be reported in the project. Paired comparisons with pre-post will be done in a similar manner in case of self-report measures (SDSCA and self-efficacy).

Simple proportions will be used to analyze process measures (attendance, documentation compliance) and report these numbers as percentages with 95% confidence intervals. The visualization of the changes over time and identification of the special-cause variation during implementation will be performed using trend and run-chart techniques typical of QI (e.g., weekly run charts and control-chart annotations). Descriptive and paired statistical analyses with process monitoring tools, including run charts, is consistent with the best practices in quality improvement research and has demonstrated accuracy in ability to measure both clinical and operational outcomes in DSMES interventions (Knight et al., 2022).

The subgroup analyses (e.g. stratified by baseline glucose level or by attending the session) will be performed with t-tests or Mann-Whitney tests to investigate the differing effects in case the sample size and the quality of the data allow it. Since the sample size (no less than 30-50 participants) is expected, the focus will be put on the estimation of effects, the use of confidence intervals, and the practical significance, but not on the null-hypothesis significance testing only.

Handling Missing Data, Statistical Assumptions, and Practical Considerations

The problem of missing data will be reduced to the minimum by proactive scheduling, reminders, and flexible follow up procedures (phone, in-person). In the event of missing data, the project will note the extent and patterns of missingness and will carry out primary analysis on available-cases; simple sensitivity analysis (e.g., last observation carried forward or multiple imputation) can be conducted in case of nontrivial missing data and under the conditions that the appropriate assumptions are satisfied. Statistical tests and visual inspection will be used to determine normality assumptions; nonparametric tests will take place in instances where the assumptions are violated.

The interpretation of statistical significance will be done considering the clinical relevance and effect sizes to make practical conclusions to be applied to the clinic. Evidence supports the use of proactive follow-up strategies and proper statistical management of missing data as a tool to preserve the integrity of data by providing reliable interpretation of intervention effects in clinical quality improvement studies (Romadlon et al., 2024; Ibrahim et al., 2025). The standard statistical software available in the academic/practicum environment (e.g., SPSS, R, or other) will be used to conduct all analyses and an analysis log will be kept to make the analysis reproducible.

Data Management, Confidentiality, and Ethical Considerations

The data obtained in the EHR and survey will be de-identified and kept in encrypted password-protected drives in line with the HIPAA and institutional policies. It has been proved that conservative compliance with data encryption, safe storage measures, and the de-identification of data help to minimize the threat of unauthorized access and stay within the limits of HIPAA privacy standards (Ricci et al., 2023). Identifiable linkage files will be made available to only approved project team members where needed to schedule or follow-up; the linkage files will be stored separately and will be destroyed at the end of the project as required by their institution.

Study IDs will be used in the analysis of data collected in data collection forms and the EHR DSMES template, which will contain only minimal identifiers required. The learner will make sure that CITI training is adhered to and any site-specific human subjects or QI governance procedures; as required, the project lead will request that the IRB make a determination or exemption according to the institutional requirements. Findings will be made in aggregate to ensure confidentiality and will be distributed to stakeholders at monthly review meetings and a final project report.

Conceptual Model

Overview

The plan-do-study-act (PDSA) model is a quality improvement model that provides teams with opportunities to test change in actual clinical environments on a rapid and repeated cycle. The first step in the model is the plan stage in which the aim of the improvement is determined, predictions are drawn, and data gathering procedures are provided. The implementation of the intervention on a small scale takes place at the do stage. The study stage is the analysis of gathered data and drawing conclusions according to the results in the comparison with the forecasts made in the planning stage. Lastly, the act stage decides whether the intervention is to be adopted, adapted, or abandoned on the findings (Bechtold & Kome, 2025). The model is cyclic, so it is possible to constantly refine and react to the barriers as well as workflow issues or unforeseen results in the shortest possible time. The PDSA cycle is reputed to have the flexibility, practicability and the capacity to enable evidence-based, gradual changes in the provision of health care.

PDSA Model Incorporated Into the Project

Each step of the DSMES intervention implementation will be organized with the help of the PDSA framework. During the Plan phase, the project team will complete changes in the workflows and set the goals of the outcome (the decreasing of mean fasting glucose to less than 130 mg/dL), prepare patient education resources, and conduct staff training with ADA-compatible modules of DSMES. In the Do phase, DSMES will be launched and the appropriate patients will be identified by using EHR reports and the staff will provide the common educational activities and record all the encounters in the structured DSMES EHR template. During the Study phase, the fasting glucose levels, self-management scales, and documentation adherence will undergo the review on a biweekly basis in order to assess whether the early trends are indicative of an improvement.

The project lead and preceptor will investigate the deviation, obstacles, or inefficient workflow and compare the findings identified with the projected outcomes made at the baseline. It is proven that PDSA cycle contributes to the improvement of the fidelity of the interventions made and fastens the quality improvement, as it allows the teams to test changes in small and iterative steps and make adjustments to the processes based on the real-time data of the performances (Turner et al., 2022). Lastly, during the Act phase, the required changes, including scheduling changes, documentation assistance, or time of sessions, will be presented prior to proceeding to the next cycle. The specified iterative model will make sure that the DSMES intervention will be increasingly more efficient, more patient-centered, and more clinic-oriented.

Connection of the PDSA Model to the Project Goals and PICOT Question

The PICOT question of the project is based on the question whether the implementation of DSMES in patients with uncontrolled Type 2 diabetes and aged adults can decrease the fasting blood glucose levels in 12 weeks. The PDSA model is directly helpful towards the goal of offering a mechanism through which the DSMES intervention can be introduced and refined in a structured and step-founded way. Every round of the model enables the staff to quantify PICOT-compatible results, evaluate the advancement in the direction of glycemic enhancement, and modify the intervention to optimize clinical gain.

Since the PICOT question is focused on the glycemic improvement during a specified period, the continuous monitoring and feedback that is inherent in the PDSA approach will maintain a constant consistency between the intervention and a preferred outcome. The model, also, supports the project agenda of enhancing the compliance of the staff to standardized processes of diabetes education by integrating continuous monitoring and real-time assessment into the implementation plan (Bechtold and Kome, 2025). The combination of PICOT framework and PDSA methodology form a closely interlinked framework that helps to establish a reliable practice change, and achieve measurable and sustained positive changes in the self-management of diabetes and glycemic indices.

How the Model Will Guide the Project

The PDSA model will be used to drive the project by influencing the ways the decisions towards implementation are taken, the way the data are interpreted and the way refinements are brought out. The model offers a stepwise approach to introducing change (e.g. changing the documentation processes or the frequency of the DSMES sessions) to a broader population to reduce risk and encourage sustainability. The Plan phase helps to create clarity regarding team roles, the result expected, and data measures. Do phase is the phase that guarantees the uniform implementation of DSMES interventions by the trained nursing personnel.

In Study phase, the review of the process and clinical data will be repeated to aid in discovering gaps or undesired effects. The Act phase will inform how the improvements should be introduced, and a culture of constant learning should be reinforced. This guidance is structured and flexible and is extremely important in primary care where workflow variability and patient complexity may affect QI outcomes (Ebbers et al., 2022). The project has a chance to attain sustainable changes in delivering the DSMES and patient glycemic outcomes in a dynamic primary care setting by basing the intervention on an iterative, data-driven approach.

Literature Supporting PDSA Use in Similar Projects

PDSA model has been a prolifically recorded practice in chronic disease management and diabetes care improvement practice. Research indicates that PDSA-based DSMES interventions have a substantial loss of glycemic control, self-management behaviors, and clinical workflow efficiency (Patandung & Glorino, 2025). As an example, Patandung and Glorino (2025) applied the PDSA model to pilot and optimize a diabetes education program and achieved considerable improvement in fasting glucose and enhanced adherence to treatment.

In a similar manner, Pullyblank et al. (2024) observed that repeated PDSA cycles were associated with a positive change in patient self-efficacy scores and the increase of staff adherence to diabetes education guidelines. EHR templates have also been refined using the model, clinical documentation accuracy has improved, and chronic disease care processes in outpatient environments have also been standardized (Carr et al., 2023). The results justify the choice of PDSA as the best framework to use to direct the iterative testing, workflow, and constant evaluation in the DSMES project.

Methodology, Budget, and Ethical Considerations

Project Assumptions and Methodological Approach

The project methodology will be based on a quality improvement (QI) design but will focus on systematic implementation, continuous evaluation and refinement of the DSMES intervention. The main premise of the project is that the application of standardized education on diabetes self-management offered by the trained nursing staff in a consistent manner will result in decreased levels of fasting glucose, as well as self-management behaviors. The hypothesis is consistent with the available evidence that indicates the efficacy of DSMES in primary care units.

It has been found that patients undergoing regular structured DSMES with trained healthcare professionals show considerable changes in glycemic control with diabetes self-management behaviors that are more favorable than those that receive no standardized education (Chowdhury et al., 2024; Ibrahim et al., 2025). The other assumption is that the staff will undergo necessary training, comply with documentation requirements and refrain from betraying the DSMES curriculum. The project is based on a pre-post QI design with the PDSA framework that enables the intervention to be tested, evaluated, and modified throughout the time in systematic cycles.

Human Subjects Protection

Even though the initiative is a QI project and not a human subjects research, the consideration of ethics is still necessary. Inclusion of DSMES in routine clinical practice is regarded as normal clinical practice and no experimental process is introduced. Nevertheless, patient information employed in order to measure the outcomes shall be de-identified before analysis in order to safeguard the privacy of patients. Only authorized clinical personnel involved in data delivery and documentation will have access to identifiable patient data. The project will be implemented with respect to organization IRB/QI oversight and the learner will satisfy all the institutional demands of ethical behavior in accordance with CITI training and HIPAA requirements.

Project Limitations and Mitigation Strategies

There are a number of constraints that can impact the project implementation and results. The variability in staff workload is one of the limitations potentially decreasing the opportunity to provide regular DSMES and document it. In order to alleviate the issue, DSMES classes will be incorporated into current appointment times and the project lead will collaborate with the preceptor to have the staffing in place during that busy time. The second shortcoming is that there may be inconsistent attendance of patients, particularly when it comes to patients who have problems with transportation, employment and socioeconomic issues. DSMES session missed will be minimized by using reminder calls, flexible schedule, and short telephone based reinforcement calls.

Research indicates that the introduction of flexible scheduling, reminder tools, and telephonic follow-up is associated with high levels of patient attendance and adherence to programs of diabetes self-management education, specifically in those populations that experience socioeconomic or logistical challenges (Ricci et al., 2023). A third limitation is that the project has a short timeframe, and it might not be able to conduct long-term evaluation of glycemic stability. The above limitation will be overcome by monitoring fasting glucose every month, and early trends information will be emphasized to interpret it.

There is also the risk of data being mistaken and variability in documentation. In order to address the problems, the staff will be provided with systematic training and project leader will conduct weekly documentation audits to be sure that standardized DSMES fields are filled in correctly and life-long. Lastly, generalizability may be constrained by small sample sizes (as most QI projects feature). Nonetheless, even descriptive statistics and process measures will permit significant assessment of tendencies and viable influence.

Project Budget and Resource Allocation

Even though the plan is a low-cost QI project, there are a number of costs and resources factors that are inherent to implementation. The key budget areas involve the staff time spent on training of the DSMES, project meetings, review of documents and provision of education to patients. The training process will take about 2-3 hours of training per employee and can be included in the available professional development budgets. Allocating the additional staff time will be necessary to schedule, make follow-ups, and attend monthly project huddles.

Fewer material supplies are needed, looking at the printed handouts of the DSMES, glucose logs, SMART goal worksheets, and session attendance forms. The objectives will not be out of the ordinary budget of the clinic in teaching patients. An adjustment in EHR might need in the form of adding a standardized DSMES documentation field, which can take about 1-2 hours of IT personnel assistance. Even when the internal IT labor is already pre-budgeted the effort should be recognized as part of project budget. The project will not need any external financial support although the time investment in the project lead who will coordinate the staff, monitor and analyze the data will be a non monetary organizational cost. Staff time should be recognized as a budget rather than having it on the fringe facilitates transparency and proper organization planning on project sustainability.

HIPAA Compliance and Data Security Plan

The patient confidentiality and data security will be strictly ensured during the project. Any identifiable information such as the name of patients, date of birth and medical record number will be locked up in the secured EHR and will not be exported to be analyzed. Only de-identified data (e.g. fasting glucose values, attendance, self-management scores, etc.) will be entered into project data spreadsheet. The spreadsheet will be saved on an encrypted, password-protected laptop issued by the organization and only the preceptor and the learner can access it. No information is going to be stored on personal devices and cloud systems that are out of the scope of the secure system of the organization. Physical materials, including sign-in sheets or printed education logs, will be locked in the cabinets at the clinical site and shredded upon the completion of data entry.

Role-based permissive access will be used to curb electronic access to the EHR and staffs will log out after every session to inhibit unauthorized access. Following the guidelines of data protection and confidentiality, such as de-identification, encrypted storage, and limited access, is consistent with the requirements of the HIPAA and proven to minimize the threat of data breaches and protects patient privacy in clinical quality improvement initiatives (Ibrahim et al., 2024). Any form of data transmission will be on safe, HIPAA-compliant channels that have been endorsed by the organization. The above measures guarantee that the HIPAA requirements are adhered to and are in line with the best practices of safeguarding the health information in QI initiatives.

Project Timeline

The DSMES intervention will be implemented in a 12-week systematic schedule, which will control the organization of staff training, interaction with patients, data gathering, and periodic evaluation. The orientation week will be dedicated to the project orientation, finalizing of the protocols, and interactions of the internal stakeholders through an introductory meeting to identify the roles, responsibilities, and expectations. The period of week 2-3 will be dedicated to providing staff with training on ADA-approved modules of DSMES, documentation procedures, and incorporation of new workflows into regular care. Pre-training assessment will be conducted in order to determine staff level of knowledge and willingness to adopt DSMES.

Organized training and orientation (i.e., the mentioned ones related to the implementation of DSMES) have proven effective in enhancing the competence of the staff, compliance with evidence-based practices, and patient education outcomes in the context of diabetes care (ElSayed et al., 2023). The first step to the identification and enrollment of patients will be within weeks 4-6, at which the identification and screening of eligible patients will be achieved using EHR reports and contacted to plan the DSMES sessions. Nursing employees will initiate the provision of structured DSMES that will include nutrition, glucose monitoring, physical activity, medication adherence, problem-solving, and goal-setting. The learner will organize the logistics of sessions, make sure that the intervention protocol is followed, and track the initial data recording.

Weeks 7-9 will focus on active implementation with follow-up sessions with patients, reinforcement of self-management behaviors, and real-time modification depend on feedback of the staff and noticed difficulties. EHR reviews and weekly huddles will be used to monitor the attendance and engagement of patients, as well as initial shifts in the level of fasting glucose. Weeks 10-11 will be aimed at consolidation of the data, post-intervention patient outcome evaluation, and employee evaluation survey to assess the adherence to the DSMES protocols. The learner will conduct initial analyses and make progress reports to the preceptor and the clinic leadership.

The week 12 will entail final assessment, which entails post-intervention fasting glucose measurement, survey of qualitative feedbacks among staff and patients, and final report compiling to be disseminated. Lessons learnt, obstacles and sustainability recommendations will be recorded. The gradual strategy enables constant observation, reaction, and adjustments back and forth, making the intervention effective and consistent whilst keeping up with the project objectives and PICOT question.

Figure 1

Project Implementation Timeline

Practicum Hours Plan of Action

Practicum hours are also deliberately designed to offer an overarching and progressive learning experience pathway with competencies at the doctoral level and high professional growth standards. In order to achieve the objectives of planning, implementing, evaluating and dissemination activities of a project, the DNP program necessitates the completion of 1,000 practicum hours that will be distributed throughout the advanced doctoral courses. The hours identified are a wide scope of direct and indirect practice hours, such as patient-centered clinical practice, implementation of quality improvement projects, interprofessional practice, stakeholder interaction, staff education, systematic data acquisition, outcome evaluation, and academic reporting.

Quality improvement practicums mandate long-term interaction in the clinical environment, where evidence-based interventions can be implemented, developed, and assessed to achieve valuable and quantifiable outcomes (Krishnappa et al., 2022). All practicum hours will be recorded regularly through the program-approved tracking system and continued approval is offered by the preceptor and faculty mentor. Through a structured and well-coordinated practicum plan, the necessary hours will be completed and will assist in the advancement of advanced clinical judgment, leadership ability, systems thinking, and scholarly practice that will be required in the DNP position.

Table 1

DNP 1,000-Hour Practicum Plan of Action

Conclusion

The proposed project will bring an organized evidence-based intervention to enhance the results of diabetes self-management by applying a comprehensive DSMES intervention. A well-articulated implementation plan, a strict data collection and evaluation framework, and PDSA model application are the elements of the QI plan that enables continuous changes to be made during the project duration. The engagement of interprofessional team and internal and external stakeholders, as well as a robust interaction with the preceptor, will provide the organization with the continuity and alignment. The methodology lays an emphasis on ethical issues, data safety, HIPAA adherence, and reasonable financial plan to facilitate viability.

An elaborate week-by-week work plan and a 1,000-hour practicum plan are also examples of how the project is prepared to be implemented. The proposal presents a logical and practical model on how the desired change in patient self-management practices and glycemic control outcomes can be attained, and advanced nursing leadership skills can be enhanced.