DNP 825 Population Health Part II

DNP 825 Population Health Part II

This assignment aims to develop an intervention for the at-risk population selected for your Population Health: Part I assignment.

 General Requirements

• A minimum of three scholarly or peer-reviewed research articles are required. Sources must be published within the last 5 years and appropriate for the assignment criteria and nursing content.
• Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
• This assignment uses a rubric. Please review the rubric before beginning the assignment to become familiar with the expectations for successful completion.

Directions

For Part II of the Population Health assignment, propose an intervention to address the health issue for your selected at-risk population.

Include the following in a 1,250–1,500-word paper:

1. Before beginning Part II, review feedback and revise your initial paper (Part I) as indicated by your instructor. Based on these revisions and potential changes, complete Part II. Synthesize Parts I and II into a final paper.
2. Propose an evidence-based intervention relevant to your population-based health issue that can be implemented to improve health outcomes or decrease disparities for the at-risk population. Discuss the evidence supporting your proposed intervention and explain why your proposed intervention is realistic and appropriate for the population.
3. Outline a plan for implementing your proposed intervention for your at-risk population. Include community and interprofessional stakeholders needed for collaboration, permissions needed, and potential costs for implementation.
4. Discuss potential challenges to implementation and ways these can be addressed.
5. Identify a public health or health promotion theory and explain how it can be used to support the implementation of your intervention. Refer to and cite the seminal article for your theory.
6. Discuss the expected outcomes for the proposed intervention and how the outcomes will be measured to determine the efficacy of your proposed intervention. What is your plan if your outcomes do not show the desired improvement?
7. As a doctoral learner, what other factors do you believe contribute to the pervasiveness of the health issue for the at-risk group? Provide examples. Explain how you, as a doctoral learner, can advocate for social justice, equity, and ethical policies for this at-risk group. How can this be applied to different arenas in health care?

SOLUTION: DNP 825 Population Health Part II

Overview of the Population Health Issue

Diabetes mellitus is a chronic disease that causes significant morbidity and death, as well as a financial burden on individuals and the overall healthcare system. Adult patients aged 18 and above were identified as the high-risk population for diabetes mellitus in the prior assignment. Multiple variables, including an increasingly sedentary lifestyle, junk food intake, health-risk behaviors such as alcohol consumption and cigarette smoking, and advancing age, expose the identified group to diabetes. Diabetes affects 11.3% (37.3 million) of the adult population in the United States (CDC, 2022) and caused 137 million deaths worldwide in 2017 (Lin et al., 2020), highlighting the critical need to address the disease. According to Riddle and Herman (2018), the overall cost of diagnosed diabetes in the United States in 2017 was $237 billion, thus the significance of a cost-effective health promotion intervention to reduce the burden of the disease. Given the dynamics of chronic illness management, technology integration is a viable evidence-based solution to address the condition. The purpose of this paper, with a focus on the selected evidence-based intervention, is to discuss the implementation plan, analyze possible implementation challenges, identify a relevant health promotion theory consistent with the intervention, and emphasize the intervention’s expected results.

The Proposed Evidence-Based Intervention Relevant to the Population-Based Health Issue

            A telemedicine approach to treatment is a viable strategy for improving health outcomes and decreasing health inequalities among the identified high-risk group. Telemedicine usage has grown in the twenty-first century, notably during the Covid19 period, when non-physical forms of delivering treatment were prized. Telemedicine is defined as the use of information technology to provide treatment to patients who are physically far from the provider (Andrès et al., 2019). The proposed intervention consists of tools that fall into three categories: data collection devices, transmission devices, and communication/interaction devices. Diabetes data collection technologies include glucometers, weighing scales, diet-tracking software, and pedometers. Data about blood glucose, BMI, caloric intake, and level of physical activity are collected and recorded on mobile phones, smartphone apps, or tablets and then transferred to healthcare practitioners. Telephone conversations, text messaging, emails, and web-based interactions such as online portals may be used to facilitate interactions between healthcare practitioners and patients. Live real-time audio-visual consultations may be conducted on these platforms, or patients can write queries through online portals, text messaging, or emails to be answered later by healthcare experts.

Several studies back up the utility of telemedicine interventions in the treatment of diabetic patients. Von Storch et al. (2019) performed a randomized control trial to assess the effectiveness of a telemedicine self-management program for type 2 diabetic patients. The intervention group in the 12-month lifestyle telemedicine-assisted self-management program (LTP) received the necessary telemedicine tools (glucometer, tablet computer, step counter), as well as telemedicine-based coaching to improve motivation and diabetes self-management. In contrast, the control group received standard outpatient care. Hemoglobin A1C (HbA1C) levels were measured three months later to examine the intervention’s short-term impact on glycemic management. The findings were as follows: HbA1C levels in the intervention group at baseline and three months were (7.05% vs. 6.58%) and in the control group were (6.89% vs. 6.95%), demonstrating a substantial decrease of HbA1C in the intervention group. The outcomes for self-management were similar, with tele-assisted patients demonstrating a substantial improvement in diabetes self-management scale score and body mass index compared to usual care participants.

As aforementioned, the use of telemedicine interventions rose during the Covid19. Wong et al. (2021) did a retrospective review to evaluate the use of telemedicine interventions in diabetes patient care during the Covid19. Attendance and outcomes of diabetic patients’ telemedicine care during COVID-19 were compared to face-to-face sessions in 2019 before COVID-19. The data revealed that attendance for telehealth consultations (88.9%) was greater during Covid19 in 2020 than attendance for face-to-face visits (85.2%) in 2019. Furthermore, telemedicine intervention improved glycemic control during the Covid19 (HbA1C 7.81.4%) better compared to face-to-face intervention (HbA1C 8.21.7%). The two publications provide peer-reviewed and reliable evidence to support the use of the intervention (telemedicine) in the specified demographic.

A Plan for the Implementation of the Proposed Intervention

            The APIE (assessment, planning, implementation, and evaluation) tool will be necessary to carry out the proposed intervention. The patient’s level of functioning and interests are examined during the assessment phase. At this point, the needs of patients who will be unable to utilize the different telemedicine equipment will be assessed, and necessary assistance will be provided. The second step comprises planning, which includes formulating agreed-upon health objectives and preparing for the resources (finances, staff, and infrastructure) required for execution. This phase comprises the procurement of hardware (smartphones, laptops, desktop computers, glucometers), software (video software, nutrition monitoring software, diabetes software), and adequate training of patients and healthcare team members on how to use the equipment (Crossen et al., 2020). The actual execution of the intervention occurs at the implementation stage, which includes data collection, transfer, and scheduling for teleconsultations. Following that, an evaluation of the intervention is conducted to establish if the intended goals were met, whether the clients were satisfied and whether revisions or improvements are necessary.

Gathering personnel—the key stakeholders interested in the intervention—is part of the planning process. All healthcare personnel in the hospital setting, including physicians, nurses, the health informatics team, and pharmacists, have a stake in guaranteeing the intervention’s efficacy. Furthermore, community partners such as community health volunteers, social workers, and community-based organizations play an active role in assuring the success of the intervention. Moreover, to provide a more holistic approach to treatment, the patient, caregivers, and family members must all be included. In terms of permits, several states require medical practitioners to be certified before performing telehealth. The certification is comparable to approving that a healthcare practitioner understands how to approach telemedicine, as well as how to utilize acceptable and relevant equipment, appropriate channels, and accurate, current procedural terminology (CPT) codes (Mehrotra et al., 2021). While some studies produce inconsistent results on cost-effectiveness analyses, the majority of authors agree that remote management and monitoring of patients is potentially cost-effective when compared to physical contact.

Challenges to the Implementation of the Intervention and Ways to Address

            While telemedicine interventions may be cost-effective in the long run, the initial expenses of adoption may be substantial. Bhatt et al. (2018) recommend using a low-cost mHealth prototype to collect, store, and transmit data, building it with smartphone features to enable the transmission of images and videos and enabling a low-cost message, both audible and readable, and delivery for low-resource settings to avoid high implementation costs. Second, technical inefficiency or disparity in technology utilization is seen while implementing different telemedicine interventions. The disparity in usage is caused by poor internet coverage in remote places, a lack of digital health literacy, a lack of smartphone ownership, and low educational attainment (Crossen et al., 2022). To address the discrepancy in technology usage, Liu et al. (2019) advocate proper training to increase people’s computer literacy and the adoption of user-friendly and highly adaptive programs and tools to reduce difficulties navigating devices and apps. Another major issue when adopting telemedicine technology is the risk of breaching privacy and confidentiality. To prevent breaches of patient privacy, clinicians must include HIPAA security protections in the telemedicine interventions they deploy.

Public Health/Health Promotion Theory and how it supports the Implementation of the Intervention

            Patient conduct toward the intervention is critical for achieving the intended effects of telemedicine. The health belief model theory promotes the implementation of telemedicine intervention, primarily when the intervention targets diabetes patients’ self-management. The health belief model theory proposes that an individual’s beliefs about health and health problems impact their inclination to engage in health-related behaviors (Cho et al., 2018). Individuals’ participation in health behaviors is impacted by their understanding of the severity of the condition, perceived advantages, perceived barriers, self-efficacy, and cues to action. Following multiple research findings indicating that telemedicine may be effective in decreasing HbA1C, patients may find this sufficient incentive to engage in the intervention. Consequently, patient behaviors such as teleconsultation adherence, medication adherence, and increased physical activity will result in positive health outcomes.

The Expected Outcomes for the Proposed Intervention and Evaluation Strategies

            At the conclusion of the telemedicine intervention for diabetic patients, it is vital to assess if the desired goals were accomplished. The expected results for this high-risk group (diabetic patients) are lower HbA1C levels, greater physical activity, optimal caloric intake, enhanced diabetes education, and fewer hospitalizations due to diabetes complications. To determine the HbA1C, a biochemical blood test is required every three months, and weekly observation and review of the pedometer readings are required to determine whether the patients have achieved the American Heart Association’s (2018) recommended 150 minutes of moderate-intensity aerobic activity cardiovascular range of exercises. The data on nutrition monitoring software will be often checked daily, weekly, or monthly) to evaluate if the patients have been taking recommended calories.

The diabetes knowledge questionnaire will be used to assess the patient’s understanding of various diabetes issues and to identify knowledge gaps that need to be filled. Furthermore, hospital records will be critical in assessing the number of patients hospitalized for diabetic complications who are getting telemedicine therapy. Whether the results do not match what was expected, I will consider extending the intervention period to see if a longer time utilizing the intervention correlates to better outcomes.

Factors Contributing to the Pervasiveness of the Health Problem in the At-Risk Population

Aside from ethnicity, genetics, age, and lifestyle choices, social factors contribute to the prevalence of diabetes among adult patients. Socioeconomic status, which includes income, employment, and education, is one of the most important social determinants of diabetes. Individuals with low educational attainment have poor health literacy and are less likely to participate in health promotion activities such as physical exercise and healthy food intake (Hill-Briggs et al., 2020). Furthermore, those with low income or unemployed may have limited access to and usage of healthcare services such as diabetes screening. The food environment, such as easy access to fast food outlets, may also encourage residents to eat a lot of junk food, raising their risk of diabetes (Hill-Briggs et al., 2020). Additionally, the high expenses of healthcare services may hinder persons from poor socioeconomic backgrounds from accessing the most basic types of health prevention and promotion, such as diabetes screening. Moreover, social cohesiveness and social capital, in which all members of society have equal leverage, influence community residents’ participation in health promotion activities, hence lowering the risk of diabetes mellitus.

The socioeconomic disparities in diabetes risk and access to diabetes prevention and treatment services may be utilized as the foundation for developing risk-mitigation strategies. For example, access disparities caused by the unaffordability of screening services may be reduced by providing community-wide free mass screening programs aimed at persons living in remote areas with low socioeconomic status. The intervention promotes health equity and social justice by providing services regardless of an individual’s socioeconomic status, ethnicity, religion, and political beliefs. Furthermore, frequent community education about diabetes and the necessity of participating in preventative health activities would help to reduce access disparities caused by poor health literacy. Screening and health education may be delivered in a variety of settings, including hospitals and non-hospital settings, making them adaptable, reliable, and capable of reaching a broad audience.

Conclusion

            Despite growing efforts to develop diabetes prevention measures for the adult population, some obstacles significantly impede success. In today’s world, many adult patients lead sedentary lifestyles, eat unhealthy junk food, smoke cigarettes, and consume alcohol. Furthermore, socioeconomic gaps in access to diabetes prevention and treatment services continue to expand, undermining attempts to reduce the disease’s burden. As a result, multiple studies have highlighted telemedicine, a population-based intervention, as a solution to diabetes patient prevention and care. Even though the intervention is a cost-effective way to improve HbA1C, it faces challenges such as technology inefficiency and privacy concerns. As a result, providers are advised to decrease technological inefficiencies through adequate patient training and user-friendly technologies, as well as limit privacy breaches by incorporating HIPAA security protections into interventions.

 

 

References

American Heart Association. (2018). American heart association recommendations for physical activity in adults and kids. Www.heart.org. https://www.heart.org/en/healthy-living/fitness/fitness-basics/aha-recs-for-physical-activity-in-adults

Andrès, E., Meyer, L., Zulfiqar, A.-A., Hajjam, M., Talha, S., Bahougne, T., Ervé, S., Hajjam, J., Doucet, J., Jeandidier, N., & Hajjam El Hassani, A. (2019). Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies. Journal of Medicine and Life, 12(3), 203–214. https://doi.org/10.25122/jml-2019-0006

Bhatt, S., Isaac, R., Finkel, M., Evans, J., Grant, L., Paul, B., & Weller, D. (2018). Mobile technology and cancer screening: Lessons from rural India. Journal of Global Health, 8(2), 020421. https://doi.org/10.7189/jogh.08.020421

CDC. (2022, June 29). National Diabetes Statistics Report. Cdc.gov. https://www.cdc.gov/diabetes/data/statistics-report/index.html

Cho, Y.-M., Lee, S., Islam, S. M. S., & Kim, S.-Y. (2018). Theories applied to m-health interventions for behavior change in low- and middle-income countries: A systematic review. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association, 24(10), 727–741. https://doi.org/10.1089/tmj.2017.0249

Crossen, S., Raymond, J., & Neinstein, A. (2020). Top 10 tips for successfully implementing a diabetes telehealth program. Diabetes Technology & Therapeutics, 22(12), 920–928. https://doi.org/10.1089/dia.2020.0042

Crossen, S. S., Bruggeman, B. S., Haller, M. J., & Raymond, J. K. (2022). Challenges and opportunities in using telehealth for diabetes care. Diabetes Spectrum: A Publication of the American Diabetes Association, 35(1), 33–42. https://doi.org/10.2337/dsi21-0018

Hill-Briggs, F., Adler, N. E., Berkowitz, S. A., Chin, M. H., Gary-Webb, T. L., Navas-Acien, A., Thornton, P. L., & Haire-Joshu, D. (2020). Social determinants of health and diabetes: A scientific review. Diabetes Care, 44(1), 258–279. https://doi.org/10.2337/dci20-0053

Lin, X., Xu, Y., Pan, X., Xu, J., Ding, Y., Sun, X., Song, X., Ren, Y., & Shan, P.-F. (2020). Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Scientific Reports, 10(1), 14790. https://doi.org/10.1038/s41598-020-71908-9

Liu, Y., Zupan, N. J., Swearingen, R., Jacobson, N., Carlson, J. N., Mahoney, J. E., Klein, R., Bjelland, T. D., & Smith, M. A. (2019). Identification of barriers, facilitators and system-based implementation strategies to increase teleophthalmology use for diabetic eye screening in a rural US primary care clinic: a qualitative study. BMJ Open, 9(2), e022594. https://doi.org/10.1136/bmjopen-2018-022594

Mehrotra, A., Nimgaonkar, A., & Richman, B. (2021). Telemedicine and medical licensure – potential paths for reform. The New England Journal of Medicine, 384(8), 687–690. https://doi.org/10.1056/NEJMp2031608

Riddle, M. C., & Herman, W. H. (2018). The cost of diabetes care-an elephant in the room. Diabetes Care, 41(5), 929–932. https://doi.org/10.2337/dci18-0012

von Storch, K., Graaf, E., Wunderlich, M., Rietz, C., Polidori, M. C., & Woopen, C. (2019). Telemedicine-assisted self-management program for type 2 diabetes patients. Diabetes Technology & Therapeutics, 21(9), 514–521. https://doi.org/10.1089/dia.2019.0056

Wong, V. W., Wang, A., & Manoharan, M. (2021). Utilisation of telehealth for outpatient diabetes management during COVID-19 pandemic: how did the patients fare? Internal Medicine Journal, 51(12), 2021–2026. https://doi.org/10.1111/imj.15441