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Evidence-Based Population Health Improvement Plan

Evidence-Based Population Health Improvement Plan

ORDER CUSTOM, PLAGIARISM-FREE PAPERS HERE Evidence-Based Population Health Improvement Plan

Develop a population health improvement plan, based on your evaluation of the best available demographic, environmental, and epidemiological data, that focuses on your diagnosis of a widespread population health issue.

Part of effectively engaging in evidence-based practice is the ability to synthesize raw health data with research studies and other relevant information in the literature. This will enable you to develop sound interventions, initiatives, and outcomes to address health concerns that you find in data during the course of your practice.

In this assessment, you have an opportunity to evaluate community demographic, environmental, and epidemiological data to diagnose a widespread population health issue, which will be the focus of a health improvement plan that you develop.

Assessment Instructions


Your organization is undertaking a population health improvement initiative focused on one of the pervasive and chronic health concerns in the local community. Examples of health improvement initiatives include nationwide concerns, such as type 2 diabetes, HIV, obesity, and Zika. However, your organization has asked you to determine which widespread health concern should be addressed in a population health improvement plan for the community in which you practice and has entrusted you with gathering and evaluating the relevant data.


Note: The requirements outlined below correspond to the grading criteria in the scoring guide, so be sure to address each point. In addition, you may want to review the performance level descriptions for each criterion to see how your work will be assessed.

Data Evaluation

Evaluate community demographic, epidemiological, and environmental data.

  • Identify the relevant data.
  • Describe the major community health concerns suggested by the data.
  • Explain how environmental factors affect the health of community residents.
Health Improvement Plan

Develop an ethical health improvement plan that effectively addresses the population health concern that you identified in your evaluation of the relevant data.

  • Base your plan on the best available evidence from a minimum of 3–5 current scholarly or professional sources.
    • Apply correct APA formatting to all in-text citations and references.
    • Attach a reference list to your plan.
  • Ensure that your plan meets the cultural and environmental needs of your community and will likely lead to some improvement in the community’s health related to this concern.
    • Consider the environmental realities and challenges that exist in the community.
    • Address potential barriers or misunderstandings related to the various cultures prevalent in the community.
  • Justify the value and relevance of the evidence you used as the basis of your plan.
    • Explain why the evidence is valuable and relevant to the community health concern you are addressing.
    • Explain why each piece of evidence is appropriate and informs the goal of improving the health of the community.
  • Propose relevant and measurable criteria for evaluating the outcomes of your plan.
    • Explain why your proposed criteria are appropriate and useful measures of success.
  • Explain how you will communicate with colleagues and members of the community, in an ethical, culturally sensitive, and inclusive way, with regard to the development and implementation of your plan.
    • Develop a clear communications strategy mindful of the cultural and ethical expectations of colleagues and community members regarding data privacy.
    • Ensure that your strategy enables you to make complex medical terms and concepts understandable to members of the community, regardless of language, disabilities, or level of education.



    The resources provided here are optional. You may use other resources of your choice to prepare for this assessment; however, you will need to ensure that they are appropriate, credible, and valid. The MSN-FP6011 – Evidence-Based Practice for Patient-Centered Care and Population Health Library Guide can help direct your research, and the Supplemental Resources and Research Resources, both linked from the left navigation menu in your courseroom, provide additional resources to help support you.

Population Health and Evidence-Based Practice

Learner’s Name

Capella University

Evidence-Based Practice for Patient-Centered Care and Population Health

Population Health and Evidence-Based Practice

July, 2017




Population Health and Evidence-Based Practice

In 2015, chronic diseases such as heart disease, diabetes, and cancer were among the top seven causes of death in the United States (Centers for Disease Control and Prevention [CDC], 2017). Many health care organizations have focused their resources on controlling and preventing chronic diseases among community members through population health management (PHM) strategies (see Appendix, Terms and Definitions). In response to the prevalence of chronic diseases, the Gilbert-Hopes Family Health Center (GHFHC) in Southern Arizona has created a population health improvement plan based on PHM strategies to improve one pervasive health concern in its community—Type 2 Diabetes Mellitus (T2DM). Type 2 Diabetes Mellitus in American Indian (AI) communities is the focus of the plan. The initiatives implemented under the health improvement plan will use the best available evidence on Southern Arizona’s AI communities gained through the evaluation of demographic, epidemiological, and environmental data. Additionally, the plan will apply strategies for communicating health improvement goals with AI communities and health care professionals in an ethical, culturally sensitive, and inclusive way.

Environmental and Epidemiological Data About American Indian Communities

According to 2012 data, diabetes is a serious chronic disease affecting 29.1 million people in the United States. It can lead to conditions such as kidney failure, blindness, and heart disease. Diabetes also makes patients vulnerable to infections that require amputation (CDC, 2014). In Arizona, which has the third largest population of AIs in the country, almost 16% of AIs reported having diabetes, especially T2DM (Bass, Bailey, Gieszl, & Gouge  2015). In Southern Arizona, the CDC estimates that about 24.1% of adult AIs have diabetes. The state’s distribution of T2DM is caused by a combination of genetic and environmental factors.

Behavioral risk factors such as smoking, alcoholism, sedentary lifestyles, weight gain, and poor diets can be classified as environmental factors of T2DM and were observed among Navajo Nation and Pima Indians (Arizona Department of Health Services, Bureau of Tobacco and Chronic Disease [AZDHS], 2011; Murea, Ma, & Freedman, 2012). Exposure to pollutants is another environmental factor that can be associated with T2DM because pollutants affect insulin sensitivity and glucose metabolism (Eze et al., 2015). Genetic factors include a family history of obesity or diabetic vascular complications. Individuals with such a family history are at high risk of getting Type 2 diabetes (Murea, Ma, & Freedman, 2012).

The evaluation of epidemiological and environmental data about T2DM in AI communities has revealed several gaps in knowledge. To begin with, most epidemiological data about AIs by federal agencies such as the CDC do not have information on populations living in Indian reservations as reservations are independent governmental entities (AZDHS, 2011). Moreover, further evaluation is needed on the effects of exposure to environmental pollutants; most studies tend to focus on behavioral risk factors. These gaps in knowledge can cause health disparities among urban AIs and those living in reservations, thereby making it difficult to identify chronic disease patterns.

Furthermore, there is a need for further evaluation of sociocultural and linguistic factors that often prevent people from accessing health care. The concept of cultural competence (see Appendix, Terms and Definitions) is imperative if the GHFHC wishes to successfully implement a population health improvement plan that will address the various needs of AI communities.

Health Improvement Plan to Address Diabetes Among American Indians

Among the many models adopted into PHM efforts, the collaborative chronic care model (CCM) framework is successful at managing diabetes and other chronic diseases among affected populations. There are six elements that are essential to the CCM: (a) health systems, (b) delivery system design, (c) decision support, (d) clinical information systems, (e) community resources and policies, and (f) self-management support (Improving Chronic Illness Care, 2003; see Appendix, Terms and Definitions). The GHFHC’s will follow the CCM for its health improvement plan based on certain assumptions about the plan. These assumptions are that the plan (a) needs to be sustained for a long time, (b) needs to comply with evidence-based guidelines for patient care, (c) needs to focus on patient education and lifestyle improvement, (d) needs to provide affordable and cost-effective care for AIs, and (e) needs to be culturally sensitive and equitable for disadvantaged community members.

The CCM’s six elements complement these assumptions and when the model is implemented in a PHM, the CCM will allow an informed, active community to productively interact with a proactive, prepared clinical team to achieve improved outcomes. Key components of the plan that are consistent with the CCM elements and the GHFHC’s assumptions are as follows: (a) establishing a system for collecting data and tracking health outcomes among AI patients; (b) establishing an operational leadership that will change staff management policies to ethnically match ethnicity and language of AI patients; (c) training all health care professionals on the CCM and cultural and linguistic competence; (d) sharing reports, lab-work, and epidemiological data with local health systems; (e) identifying local resources such as community health centers, YMCAs, religious centers, and senior centers that can help connect patients with the GHFHC; and (f) planning regular meetings for all stakeholders to resolve issues, discuss outcomes, and make recommendations.

The different components in the plan will enable health care professionals in diagnosing widespread diabetes in the AI community and ensure that cultural competence is deployed at the patient, health care professional, organizational, and systems levels. The next section will discuss why the CCM was selected over other community-based population health management models. Relevant evidence and examples will be provided.

Value and Relevance of the Chronic Care Model

Since its inception more than 15 years ago, the chronic care model has been for diabetes care in health care organizations across the United States with positive outcomes (Baptista et al., 2016). Most of the evidence supporting the model comes from randomized control trials (RCTs), qualitative reviews, meta-analyses, and systematic reviews of articles on the CCM in health care organizations. The results of one systematic review of 16 studies on the CCM application revealed better diabetes management programs in several health organizations (Stellefson, Dipnarine, & Stopka, 2013). Organizational leaders used the CCM to initiate system-level changes that improved delivery of diabetes care to patients. The organizations introduced disease registries and electronic records to establish patient-centered goals, educate patients on self-management, and train health care professionals in evidence-based care.

Another study evaluated the success of Project Dulce, a CCM-based diabetes care program developed by the Scripps Whittier Diabetes Institute in collaboration with San Diego County, San Diego State University, and federally qualified health centers (Philis-Tsimikas & Gallo, 2014). The project used specially trained teams and peer educators to implement the CCM elements in an ethnically and racially diverse community. The results showed significant cost-effectiveness hospitalizations and emergency visits reduced.

While the CCM has many merits, there are conflicts in the data provided by the aforementioned studies. Health care organizations implement only one or two elements such as delivery design systems or self-management rather than the combined implementation of all six elements. As a result, it is difficult to determine the overall impact of the CCM or identify the combinations of elements that are ideal (Davy et al., 2015). Other conflicting problems are related to the study process of RCTs: participants were sometimes aware of their participation in trials, follow-up periods and sample sizes were insufficient, and study nurses were inadequately trained (Baptista et al., 2016).

Despite these problems, the CCM remains a popular model compared to the acute care model of case management. While the two are similar in terms of care coordination and cost-effective strategizing, the CCM is more overarching, community and population-based, and more proactive in health improvement. Acute care, on the other hand, is case-specific, client-centered, and episodic (Huber, 2017). Because of the reach and magnitude of GHFHC’s health improvement plan, evaluating its outcomes can become a complex task. As the plan follows already established population health management strategies, evaluative criteria will correspondingly borrow from PHM theories of outcomes measurement.

Criteria to Evaluate Achievement of Plan Outcomes

Population health improvement plans are the convergence of different health care roles and resources. To devise an effective PHM plan, GHFHC must identify, define, and assess standards that can evaluate its plan in its entirety. One of the leading organizations conducting research and development on population health programs is Care Continuum Alliance (CCA). The CCA identified key components of PHM (Care Continuum Alliance [CCA], n.d.-a; see Appendix, Terms and Definitions) in its population health improvement model. The model proposed an additional five measures of population health plan outcomes: (a) optimal clinical indicators, including process and outcomes measures; (b) assessment of patient satisfaction with health care; (c) economic and health care utilization indicators; (d) functional status and quality of life; and (e) impact on known population health disparities (CCA, n.d.-a).

These five measures can be supplemented with evaluative criteria adapted from the CCA’s six components of disease management (DM) programs. The DM evaluation criteria can help determine whether the plan objectives are completed and detail the performance indicators or methods used in the process (Huber, 2017; CCA, n.d.-b; see Appendix, Terms and Conditions). The combination of both sets of criteria can provide a well-rounded evaluation and simultaneously consider different components and methods within the GHFHC’s health improvement plan.

Other evaluative criteria that were considered, but rejected, belonged to the RAND Corporation’s DISMEVAL project for chronic disease management. The RAND evaluation is organized under broad categories—input measures, process measures, output measures, outcome measures, and other impacts—with specific dimensions to be selected based on the design and goals of the health intervention (Nolte et al., 2012). However, the criteria do not adequately address sociocultural impacts from the health care intervention. As the health improvement plan specifically targets a vulnerable community, the plan will benefit from an evaluative framework that presupposes cultural disparities and promotes specific steps for optimal social outcomes.

Implementation and evaluation of the population health improvement plan depend on a concrete communication strategy. To coordinate care across different times, settings, providers, and community members, the structure and challenges of its communication plan become even more relevant.

Culturally Competent Communication Strategies for the PHM Plan

Communication is central to achieving each component of the population health improvement plan. Therefore, communication is a core task for GHFHC’s health care professionals. The biggest communication barriers to be expected in care coordination and self-management are distrust, misunderstanding due to language differences, inappropriate educational methods, a lack of cultural competence, and low levels of interaction between health care providers and patients brought on by language barriers (Tiedt & Sloan, 2015; Ghosh & Spitzer, 2014).

One strategy to remove these barriers is training health care professionals, especially nursing professionals who are primary caregivers, in cultural and linguistic competence. The strategy includes enlisting the expertise of interpreters and hiring staff from AI backgrounds to achieve language concordance (Dauvrin, Lorant, & d’Hoore, 2015). Peer specialist-led interventions are instrumental in communication because they connect with patients through their shared experiences. Peer specialists are individuals who have personal experiences with a health issue such as T2DM and who may belong to the same ethnicity or community as the patient (Cabassa et al., 2015; Dauvrin, Lorant, & d’Hoore, 2015). Cultural tailoring of resources and facilities is a part of this strategy. For example, educational materials and content-based resources for patients can depict substantial graphic content instead of plain text to avoid ambiguity.

Another strategy is to improve interaction between health care providers and patients. Establishing regular contact through telephonic calls and emails, conducting meetings at public or community spaces, and arranging for clinic-to-home services for patients are appropriate communication methods (CCA, 2012). Face-to-face patient follow-ups can be set up on a daily, weekly, or monthly basis determined by the severity and risk of the case (Cabassa et al., 2015). Additionally, interactions are simplified by setting up clinical information systems for sharing all patient-related data among care providers and care coordinators because health care professionals who can answer patients’ queries are more trusted (Dauvrin, Lorant, & d’Hoore, 2015).

Despite these strategies, there is potential for challenges in communication. For instance, many community members may be spread across relatively isolated rural areas and reservations. They may not be able to procure access to health interventions, or health care providers may not be able to go to them. The solution will be enlisting the help of local community leaders and medical centers to bridge the communication gap and execute the communication strategies. Another challenge can arise if health professionals do not involve patients’ families in the health management process. Families play an important role in enforcing behavioral and lifestyle changes and sustaining those changes to improve health outcomes. It is impossible to plan for each problem, communication-related or other, as health care is a high-risk environment. Instead, health care professionals must focus on the professional guidelines on patient-centered care and cultural competence that will help them solve problems as they arise. Health care professionals focused on proactive care delivery are part of what makes a population improvement plan effective.


As chronic diseases such as type 2 diabetes become more widespread, health care professionals and communities must work together to create focused health interventions. However, interventions cannot ignore the cultural contexts that shape individuals and their experiences in health care. Models such as the chronic care model are successful in customizing care for ethnic and racial communities. Yet, experts have voiced a need for improving such models and PHM guidelines that focus on the specific and unique needs of multicultural patients. The GHFHC, through its health improvement plan, will join other health care organizations in promoting research and innovation in an important health care field.    Evidence-Based Population Health Improvement Plan























Arizona Department of Health Services, Bureau of Tobacco and Chronic Disease. (2011). Arizona Diabetes Burden Report:2011. Retrieved from http://azdhs.gov/documents/prevention/tobacco-chronic-disease/diabetes/reports-data/AZ-Diabetes-Burden-Report-2011.pdf

Baptista, D. R., Wiens, A., Pontarolo, R., Regis, L., Reis, W. C. T., & Correr, C. J. (2016). The chronic care model for type 2 diabetes: A systematic review. Diabetology & Metabolic Syndrome8(7). https://dx.doi.org/10.1186/s13098-015-0119-z

Bass, J., Bailey, R., Gieszl, S., & Gouge, C. A. (2015). Arizona Behavioral Risk Factor Surveillance System Survey [Data file]. Retrieved from http://azdhs.gov/documents/preparedness/public-health-statistics/behavioral-risk-factor-surveillance/annual-reports/brfss-annual-report-2015.pdf

Cabassa, L. J., Stefancic, A., O’Hara, K., El-Bassel, N., Lewis-Fernández, R., Luchsinger, J. A., . . . Palinkas, L. A. (2015). Peer-led healthy lifestyle program in supportive housing: Study protocol for a randomized controlled trial. Trials, 16(388). https://dx.doi.org/10.1186/s13063-015-0902-zEvidence-Based Population Health Improvement Plan

Care Continuum Alliance. (n.d.-a). Advancing the population health improvement model. Retrieved from http://carecontinuum.org/phi_definition.asp

Care Continuum Alliance. (n.d.-b). Definition of disease management. Retrieved from http://carecontinuum.org/dm_definition.asp

Care Continuum Alliance. (2012). Implementation and evaluation: A population health guide for primary care models. Retrieved from http://populationhealthalliance.org/publications/population-health-guide-for-primary-care-models.html

Centers for Disease Control and Prevention. (2017). Deaths and Mortality. Retrieved from https://cdc.gov/nchs/fastats/deaths.htm

Centers for Disease Control and Prevention. (2014). National Diabetes Statistics Report. Retrieved from https://cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdfEvidence-Based Population Health Improvement Plan

Dauvrin, M., Lorant, V., & d’Hoore, W. (2015). Is the chronic care model integrated into research examining culturally competent interventions for ethnically diverse adults with type 2 diabetes mellitus? A review. Evaluation & the Health Profession. 38(4), 435–463. https://dx.doi.org/10.1177/0163278715571004

Davy, C., Bleasel, J., Liu, H., Tchan, M., Ponniah, S., & Brown, A. (2015). Effectiveness of chronic care models: Opportunities for improving healthcare practice and health outcomes: A systematic review. BMC Health Services Research15(194). https://dx.doi.org/10.1186/s12913-015-0854-8

Eze, I. C., Hemkens, L. G., Bucher, H. C., Hoffmann, B., Schindler, C., Künzli, N., . . . & Probst-Hensch, N. M. (May, 2015). Association between ambient air pollution and diabetes mellitus in Europe and North America: Systematic review and meta-analysis. Environmental Health Perspectives123(5), 381–389. https://ehp.niehs.nih.gov/wp-content/uploads/123/5/ehp.1307823.alt.pdf

Ghosh, H., & Spitzer, D. (2014). Inequities in diabetes outcomes among urban First Nation and Métis communities: Can addressing diversities in preventive services make a difference? The International Indigenous Policy Journal5(1). Retrieved from http://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1108&context=iipj

Huber, D. L. (2017). Leadership and nursing care management (6th ed.). Philadelphia: W. B. Saunders. http://dx.doi.org/10.7748/nm.21.6.13.s14

Improving Chronic Illness Care. (2003). The chronic care model. Retrieved from http://improvingchroniccare.org/index.php?p=The_Chronic_Care_Model&s=2

Murea, M., Ma, L., & Freedman, B. I. (2012). Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. The Review of Diabetic Studies 9(1), 6–22. DOI: 10.1900/RDS.2012.9.6Evidence-Based Population Health Improvement Plan

Nolte, E., Adams, J. L., Brunn, M., Cadier, B., Chevreul, K., Conklin, A., …Vrijhoef, H. (2012). Evaluating chronic disease management: Recommendations for funders and users. Retrieved from http://rand.org/content/dam/rand/pubs/technical_reports/2012/RAND_TR1213.pdf

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Stellefson, M., Dipnarine, K., & Stopka, C. (2013). The chronic care model and diabetes management in US primary care settings: A systematic review. Preventing Chronic Disease10(E26). http://dx.doi.org/10.5888/pcd10.120180

Tiedt, J. A., & Sloan, R. S. (2015). Perceived unsatisfactory care as a barrier to diabetes self-management for Coeur d’Alene tribal members with type 2 diabetes. Journal of Transcultural Nursing26(3), 287–293. DOI: 10.1177/1043659614526249Evidence-Based Population Health Improvement Plan




Terms and Definitions

Population Health Management

Population health management (PHM) is one of the three initiatives used in the coordination of care along with disease management (DM) and case management (CM). While CM focuses intensively on an individual patient in relation to one or more health conditions, DM involves intensive health interventions for a population group. Moving up another level, PHM devises health strategies for community-based populations. Of late, all the three initiatives are being combined under the umbrella of population health improvement (Huber, 2017). Evidence-Based Population Health Improvement Plan

Cultural competence

Cultural competence is “a set of congruent behaviors, attitudes, and policies that come together in a system or agency or among professionals that enable effective interactions in a cross-cultural framework.” Linguistic competence is “providing readily available, culturally appropriate oral and written language services to limited English proficiency (LEP) members through such means as bilingual and bicultural staff, trained medical interpreters, and qualified translators” (Agency for Healthcare Research and Quality, 2013, para. 2 and 3).


Agency for Healthcare Research and Quality. (2013). What is cultural and linguistic competence? Retrieved from https://ahrq.gov/professionals/systems/primary-care/cultural-competence-mco/cultcompdef.htmlEvidence-Based Population Health Improvement Plan

The Chronic Care Model

The CCM is a set of six elements that identify processes and conditions that are essential for high-quality chronic disease management. The CCM can be implemented for a variety of chronic diseases, health care settings, and populations. The first element, health care system, focuses on the health care system by using senior leadership to encourage strategies that facilitate change, manage errors, and prevent communication problems by coordinating care across and within an organization. The second element, delivery system design, focuses on improving care delivery by giving culturally competent care and providing continuum of care, regular follow-up of patient care, and case management services.

Decision support, the third element, promotes clinical care that is consistent with scientific evidence. It integrates specialist expertise into patient care, shares information with patients, and includes their preferences in clinical care. The fourth element, clinical information systems, focuses on organizing patient and population data to facilitate effective and efficient care. The fifth element, self-management support, develops ways to empower, educate, and prepare patients to manage their health and health care options. The last element, community resources, concentrates on mobilizing community resources and advocating policies for improvement in patient care (Improving Chronic Illness Care, 2003). Evidence-Based Population Health Improvement Plan

Components of Population Health Management

The key components of the CCA’s population health improvement model are as follows: (a) population identification strategies and processes; (b) comprehensive needs assessments that assess physical, psychological, economic, and environmental needs; (c) proactive health promotion programs that increase awareness of the health risks associated with certain personal behaviors and lifestyles; (d) patient-centric health management goals and education, which may include primary prevention, behavior modification programs, and support for concordance between the patient and the primary care provider; (e) routine reporting and feedback loops, which may include communication with patients, physicians, health plan and ancillary providers; (f) self-management interventions aimed at influencing the targeted population to make behavioral changes; and (g) continuous evaluation of humanistic, clinical, and economic outcomes with the goal of improving the health of overall population (Care Continuum Alliance, n.d.-a).

Criteria of Disease Management Programs

The criteria are as follows: (a) population identification and assessment, including risk assessment and risk stratification; (b) use of evidence-based practice guidelines; (c) selection of a type of practice model; (d) patient self-management, including education, primary prevention, behavior modification, lifestyle change motivation, adherence to health objectives, and surveillance; (e) identification, measurement, and evaluation of the process and outcomes; and (f) routine reporting and feedback loop, which include establishing open lines of communication between patients, physicians, health plan and ancillary providers, and sharing information to health care professionals about individual roles (Huber, 2017; CCA, n.d.-b)Evidence-Based Population Health Improvement Plan